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Nikolaus Hautsch

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüß, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Menac, 2024. "Nonstandard errors," LSE Research Online Documents on Economics 123002, London School of Economics and Political Science, LSE Library.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad‐Díaz & Menachem (Meni) Abudy , 2024. "Nonstandard Errors," Journal of Finance, American Finance Association, vol. 79(3), pages 2339-2390, June.
    • Utz Weitzel & Michael Razen & Sebastian Neussüs & Michael Kirchler & Magnus Johannesson & Juergen Huber & Felix Holzmeister & Anna Dreber & Albert J. Menkveld & Javier Gil-Bazo, 2021. "Non-Standard Errors," Working Papers 1303, Barcelona School of Economics.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," IWH Discussion Papers 11/2021, Halle Institute for Economic Research (IWH).
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neussüs & Michael Razen & Utz Weitzel & Christian T. Brownlees & Javier Gil-Baz, 2021. "Non-standard errors," Economics Working Papers 1807, Department of Economics and Business, Universitat Pompeu Fabra.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Mena, 2021. "Non-Standard Errors," Working Papers 2021:17, Lund University, Department of Economics.
    • Albert J. et al. Menkveld, 2021. "Non-Standard Errors," CESifo Working Paper Series 9453, CESifo.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Post-Print halshs-03500882, HAL.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neus ss & Michael Razen & Utz Weitzel & Edwin Baidoo & Michael Fr mmel & et al, 2021. "Non-Standard Errors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1032, Ghent University, Faculty of Economics and Business Administration.
    • Francesco Franzoni & Roxana Mihet & Markus Leippold & Per Ostberg & Olivier Scaillet & Norman Schürhoff & Oksana Bashchenko & Nicola Mano & Michele Pelli, 2022. "Non-Standard Errors," Swiss Finance Institute Research Paper Series 22-09, Swiss Finance Institute.
    • Menkveld, A. & Dreber, A. & Holzmeister, F. & Huber, J. & Johannesson, M. & Kirchler, M. & Neusüss, S. & Razen, M. & Neusüss, S. & Neusüss, S., 2021. "Non-Standard Errors," Cambridge Working Papers in Economics 2182, Faculty of Economics, University of Cambridge.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Hasse, Jean-Baptiste & e.a.,, 2023. "Non-Standard Errors," LIDAM Reprints LFIN 2023002, Université catholique de Louvain, Louvain Finance (LFIN).
    • Menkveld, Albert J. & Dreber, Anna & Declerck, Fany & Moinas, Sophie, 2023. "Non-Standard Errors," TSE Working Papers 23-1451, Toulouse School of Economics (TSE).
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-04676112, HAL.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," SAFE Working Paper Series 327, Leibniz Institute for Financial Research SAFE.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & David Abad-Dí­az & Menachem Abudy & Tobi, 2021. "Non-Standard Errors," Working Papers 2021-31, Faculty of Economics and Statistics, Universität Innsbruck.
    • Gerardo Ferrara & Simon Jurkatis, 2021. "Non-standard errors," Bank of England Staff Working Paper series 955, Bank of England.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-05077550, HAL.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03500882, HAL.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05077550, HAL.
    • Ciril Bosch-Rosa & Bernhard Kassner, 2023. "Non-Standard Errors," Rationality and Competition Discussion Paper Series 385, CRC TRR 190 Rationality and Competition.
    • Wolff, Christian & Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüess, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-Standard Errors," CEPR Discussion Papers 16751, Centre for Economic Policy Research.
    • Albert J. Menkveld & Anna Dreber & Félix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard, 2021. "Non-Standard Errors," Documents de travail du Centre d'Economie de la Sorbonne 21033, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

    Cited by:

    1. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Management Science Reproducibility Collaboration, 2023. "Reproducibility in Management Science," Department for Strategy and Innovation Working Paper Series 03/2023, WU Vienna University of Economics and Business.
    2. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Ardia, David & Guidotti, Emanuele & Kroencke, Tim A., 2024. "Efficient estimation of bid–ask spreads from open, high, low, and close prices," Journal of Financial Economics, Elsevier, vol. 161(C).
    4. Liu, Fang & Rasch, Alexander & Schwarz, Marco A. & Waibel, Christian, 2025. "The role of diagnostic ability in markets for expert services," European Economic Review, Elsevier, vol. 180(C).
    5. Stephen A. Gorman & Frank J. Fabozzi, 2023. "Alternative risk premium: specification noise," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 459-473, October.
    6. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2025. "Factor momentum versus price momentum: Insights from international markets," Journal of Banking & Finance, Elsevier, vol. 170(C).
    7. Hasso, Tim & Brosnan, Mark & Ali, Searat & Chai, Daniel, 2025. "Perceived problems, causes, and solutions of finance research reproducibility and replicability: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    8. Huntington-Klein, Nick & Pörtner, Claus C. & Acharya, Yubraj & Adamkovic, Matus & Adema, Joop & Agasa, Lameck Ondieki & Ahmad, Imtiaz & Akbulut-Yuksel, Mevlude & Andresen, Martin Eckhoff & Angenendt, , 2025. "The Sources of Researcher Variation in Economics," HEC Research Papers Series 1551, HEC Paris.
    9. Andreas Bergh & Philipp C. Wichardt & Philipp Christoph Wichardt, 2024. "On Credibility and Causality in Economics: A Critical Appraisal," CESifo Working Paper Series 11224, CESifo.
    10. Nate Breznau & Eike Mark Rinke & Alexander Wuttke & Hung H. V. Nguyen & Muna Adem & Jule Adriaans & Amalia Alvarez-Benjumea & Henrik K. Andersen & Daniel Auer & Flavio Azevedo & Oke Bahnsen & Dave Bal, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(44), pages 2203150119-, November.
    11. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    12. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert J Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    13. Nusret Cakici & Christian Fieberg & Daniel Metko & Adam Zaremba, 2024. "Do Anomalies Really Predict Market Returns? New Data and New Evidence," Review of Finance, European Finance Association, vol. 28(1), pages 1-44.
    14. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," Ruhr Economic Papers 1055, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Soebhag, Amar & Van Vliet, Bart & Verwijmeren, Patrick, 2024. "Non-standard errors in asset pricing: Mind your sorts," Journal of Empirical Finance, Elsevier, vol. 78(C).
    16. Breznau, Nate & Auspurg, Katrin & Brüderl, Josef & Holzmeister, Felix & Nilsonne, Gustav & Aczel, Balazs & Clark, Cory J & Nguyen, Hung Hoang Viet & Varga, Marton Aron & Uhlmann, Eric Luis, 2023. "Pre-Registered Analysis Plan: Is the Inter-Researcher Variability in Social Scientific Results Explicable? An Adversarial Collaboration and Joint Effort to Parse Model and Estimate Dispersion," MetaArXiv h9t5c_v1, Center for Open Science.
    17. Deer, Lachlan & Adler, Susanne J. & Datta, Hannes & Mizik, Natalie & Sarstedt, Marko, 2025. "Toward open science in marketing research," International Journal of Research in Marketing, Elsevier, vol. 42(1), pages 212-233.
    18. Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Sep 2025.
    19. van Cappelle, Tjeerd & Pokidin, Dmytro & Zwinkels, Remco C.J., 2025. "The cross section of stock returns in an artificial stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 239(C).
    20. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
    21. Ferman, Bruno & Finamor, Lucas, 2025. "There must be an error here! Experimental evidence on coding errors' biases," I4R Discussion Paper Series 266, The Institute for Replication (I4R).
    22. Henriquez-Salman, Ricardo, 2025. "Methodological ESG uncertainty in portfolio sorts," Research in International Business and Finance, Elsevier, vol. 80(C).
    23. van Dolder, Dennie & Vandenbroucke, Jurgen, 2024. "Behavioral risk profiling: Measuring loss aversion of individual investors," Journal of Banking & Finance, Elsevier, vol. 168(C).
    24. Christoph Huber & Christian König-Kersting & Matteo M. Marini, 2022. "Experimenting with Financial Professionals," Working Papers 2022-07, Faculty of Economics and Statistics, Universität Innsbruck, revised Jun 2024.
    25. Andrew Delios & Tianyou Hu & Shu Yu & Nan Zhou & Faisal M. Ahsan & Mona Bahl & Tao Bai & Madhurima Basu & Hanoku Bathula & Georgios Batsakis & Jorge Carneiro & Dwarka Chakravarty & Danyang Chen & Weih, 2025. "The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 56(9), pages 1102-1124, December.
    26. Auer, Tobias & Ulasik, Maria & Holzmeister, Felix, 2024. "A Comment on "Motivated Errors" by Exley and Kessler (2024)," I4R Discussion Paper Series 161, The Institute for Replication (I4R).
    27. Campbell, Douglas & Brodeur, Abel & Dreber, Anna & Johannesson, Magnus & Kopecky, Joseph & Lusher, Lester & Tsoy, Nikita, 2024. "The Robustness Reproducibility of the American Economic Review," I4R Discussion Paper Series 124, The Institute for Replication (I4R).

  2. Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.

    Cited by:

    1. Federico Vittorio Cortesi & Giuseppe Iannone & Giulia Crippa & Tomaso Poggio & Pierfrancesco Beneventano, 2026. "Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series," Papers 2603.02620, arXiv.org.
    2. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
    3. Zhang, Chao & Pu, Xingyue & Cucuringu, Mihai & Dong, Xiaowen, 2025. "Forecasting realized volatility with spillover effects: Perspectives from graph neural networks," International Journal of Forecasting, Elsevier, vol. 41(1), pages 377-397.
    4. Brini, Alessio & Toscano, Giacomo, 2025. "SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1093-1111.
    5. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Jan 2025.
    6. Dimitriadis, Timo & Halbleib, Roxana & Polivka, Jeannine & Rennspies, Jasper & Streicher, Sina & Wolter, Axel Friedrich, 2026. "Efficient sampling for realized variance estimation in time-changed diffusion models," Journal of Econometrics, Elsevier, vol. 254(PA).
    7. Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2025. "Learning Volatility:A Bayesian Neural Stochastic Framework," DES - Working Papers. Statistics and Econometrics. WS 47944, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Radmir Mishelevich Leushuis & Nicolai Petkov, 2026. "Advances in forecasting realized volatility: a review of methodologies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-29, December.
    9. Patton, Andrew J. & Zhang, Haozhe, 2026. "Bespoke realized volatility: Tailored measures of risk for volatility prediction," Journal of Econometrics, Elsevier, vol. 254(PA).

  3. Nikolaus Hautsch & Christoph Scheuch & Stefan Voigt, 2018. "Building Trust Takes Time: Limits to Arbitrage for Blockchain-Based Assets," Papers 1812.00595, arXiv.org, revised Oct 2023.

    Cited by:

    1. Gang Chu & Xiao Li & Dehua Shen & Andrew Urquhart, 2025. "Price divergence in bitcoin market," Review of Quantitative Finance and Accounting, Springer, vol. 65(3), pages 1135-1176, October.
    2. Johnson, William C. & Scharnowski, Stefan, 2025. "Price discovery through wrapped tokens," Economics Letters, Elsevier, vol. 257(C).
    3. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2020. "Equilibrium Bitcoin Pricing," EconPol Working Paper 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Son, Bumho & Lee, Yunyoung & Jang, Huisu, 2025. "A two-stage game model of probabilistic price manipulation in decentralized exchanges," Economic Modelling, Elsevier, vol. 147(C).
    5. Hadhri, Sinda & Younus, Mehak & Naeem, Muhammad Abubakr & Yarovaya, Larisa, 2025. "Listening to the Market: Music sentiment and cryptocurrency returns," Journal of International Money and Finance, Elsevier, vol. 157(C).
    6. Chen, Meichen & Qin, Cong & Zhang, Xiaoyu, 2022. "Cryptocurrency price discrepancies under uncertainty: Evidence from COVID-19 and lockdown nexus," Journal of International Money and Finance, Elsevier, vol. 124(C).
    7. Guo, Weiwei & Intini, Silvia & Jahanshahloo, Hossein, 2025. "Bitcoin arbitrage and exchange default risk," Finance Research Letters, Elsevier, vol. 71(C).
    8. Brauneis, Alexander & Mestel, Roland & Riordan, Ryan & Theissen, Erik, 2022. "Bitcoin unchained: Determinants of cryptocurrency exchange liquidity," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 106-122.
    9. Anantha Divakaruni & Peter Zimmerman, 2022. "The Lightning Network: Turning Bitcoin into Money," Working Papers 22-19, Federal Reserve Bank of Cleveland.
    10. Stefan Scharnowski & Yanghua Shi, 2026. "Bitcoin Blackout: Proof-of-Work and the Risks of Mining Centralization," Working Papers EMS_2026_08, Research Institute, International University of Japan.

  4. Hautsch, Nikolaus & Scheuch, Christoph & Voigt, Stefan, 2018. "Limits to arbitrage in markets with stochastic settlement latency," CFS Working Paper Series 616, Center for Financial Studies (CFS).

    Cited by:

    1. Peter Zimmerman, 2020. "Blockchain structure and cryptocurrency prices," Bank of England Staff Working Paper series 855, Bank of England.
    2. Andrea Barbon & Angelo Ranaldo, 2021. "On The Quality Of Cryptocurrency Markets: Centralized Versus Decentralized Exchanges," Papers 2112.07386, arXiv.org, revised Sep 2024.
    3. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.

  5. Hautsch, Nikolaus & Horvath, Akos, 2017. "How effective are trading pauses?," CFS Working Paper Series 571, Center for Financial Studies (CFS).

    Cited by:

    1. Machado, Caio, 2023. "Managing Overreaction During a Run," MPRA Paper 117896, University Library of Munich, Germany, revised 08 Jul 2023.
    2. Steven Shuye Wang & Kuan Xu & Hao Zhang, 2019. "A Microstructure Study of Circuit Breakers in the Chinese Stock Markets," Working Papers daleconwp2019-02, Dalhousie University, Department of Economics.
    3. Marjolein E. Verhulst & Philippe Debie & Stephan Hageboeck & Joost M. E. Pennings & Cornelis Gardebroek & Axel Naumann & Paul van Leeuwen & Andres A. Trujillo-Barrera & Lorenzo Moneta, 2021. "When Two Worlds Collide: Using Particle Physics Tools to Visualize the Limit Order Book," Papers 2109.04812, arXiv.org.
    4. Bats, Joost & Greif, William & Kapp, Daniel, 2022. "The rise in the cross-sectoral dispersion of earnings expectations during COVID-19," Working Paper Series 2664, European Central Bank.
    5. Wong, Kin Ming & Kong, Xiao Wei & Li, Min, 2020. "The magnet effect of circuit breakers and its interactions with price limits," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    6. Zhang, Xiaotao & Li, Xinxian & Hao, Jing & Li, Peigong, 2023. "Price limit change and magnet effect: The role of investor attention," Finance Research Letters, Elsevier, vol. 53(C).
    7. Sifat, Imtiaz Mohammad & Mohamad, Azhar, 2020. "A survey on the magnet effect of circuit breakers in financial markets," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 138-151.
    8. Ester Félez‐Viñas & Björn Hagströmer, 2021. "Do volatility extensions improve the quality of closing call auctions?," The Financial Review, Eastern Finance Association, vol. 56(3), pages 385-406, August.
    9. Joost Bats & William Greif & Daniel Kapp, 2021. "The rise in the cross-sectoral dispersion of earnings expectations during COVID-19," Working Papers 724, DNB.
    10. Suwan (Cheng) Long & Ioannis Chatziantoniou & David Gabauer & Brian Lucey, 2024. "Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(13), pages 1470-1489, September.
    11. Kyong Shik Eom & Kyung Yoon Kwon & Sung Chae La & Jong-Ho Park, 2022. "Dynamic and Static Volatility Interruptions: Evidence from the Korean Stock Markets," JRFM, MDPI, vol. 15(3), pages 1-19, February.
    12. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    13. Apergis, Nicholas & Lau, Chi Keung & Xu, Bing, 2023. "The impact of COVID-19 on stock market liquidity: Fresh evidence on listed Chinese firms," International Review of Financial Analysis, Elsevier, vol. 90(C).
    14. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    15. Kyong S. Eom & Kyung Y. Kwon & Jong‐Ho Park, 2021. "Effectiveness of the conditional random‐end trading mechanism on the Korea Exchange: Normal trade and Option Shock," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1545-1568, October.
    16. Xinyue He & Teresa Serra, 2022. "Are price limits cooling off agricultural futures markets?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1724-1746, October.
    17. Lars Winkelmann & Wenying Yao, 2026. "Breaking news," Papers 2603.22835, arXiv.org.
    18. Aloosh, Arash & Choi, Hyung-Eun & Ouzan, Samuel, 2023. "The tail wagging the dog: How do meme stocks affect market efficiency?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 68-78.
    19. Qi, Qingyu & Uchida, Konari & Liu, Jianlei, 2024. "Do stock swap bidders suspend their stock trading? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    20. Lin, Hai & Lo, Ingrid & Qiao, Rui, 2021. "Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market," Journal of Banking & Finance, Elsevier, vol. 133(C).
    21. Castro, Carlos & Agudelo, Diego A. & Preciado, Sergio, 2020. "Measuring the effectiveness of volatility auctions," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 566-581.
    22. Hao Li & Zhisheng Li, 2022. "The effect of daily price limits on stock liquidity: Evidence from the Chinese stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(5), pages 4885-4917, December.

  6. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).

    Cited by:

    1. Bellia, Mario & Christensen, Kim & Kolokolov, Aleksey & Pelizzon, Loriana & Renò, Roberto, 2022. "Do designated market makers provide liquidity during a flash crash?," SAFE Working Paper Series 270, Leibniz Institute for Financial Research SAFE, revised 2022.
    2. Park, Seongkyu Gilbert & Ryu, Doojin, 2019. "Speed and trading behavior in an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 145-164.
    3. Yamada, Masahiro, 2022. "Profitability and liquidity provision of HFTs during large price shocks: Does relative tick size matter?," Finance Research Letters, Elsevier, vol. 46(PA).
    4. Brice Corgnet & Mark DeSantis & Christoph Siemroth, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Working Papers 2313, Groupe d'Analyse et de Théorie Economique Lyon St-Etienne (GATE Lyon St-Etienne), Université de Lyon.
    5. Banerjee, Anirban & Nawn, Samarpan, 2024. "Proprietary algorithmic traders and liquidity supply during the pandemic," Finance Research Letters, Elsevier, vol. 61(C).
    6. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    7. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).

  7. Nikolaus Hautsch & Stefan Voigt, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty," Papers 1709.06296, arXiv.org, revised Jun 2018.

    Cited by:

    1. Qiao, W. & Bu, D. & Gibberd, A. & Liao, Y. & Wen, T. & Li, E., 2023. "When “time varying” volatility meets “transaction cost” in portfolio selection," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 220-237.
    2. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    3. Ruike Wu & Yonghe Lu & Yanrong Yang, 2026. "Sustainable Investment: ESG Impacts on Large Portfolio," Papers 2602.14439, arXiv.org.
    4. Hafner, Christian M. & Wang, Linqi, 2024. "Dynamic portfolio selection with sector-specific regularization," Econometrics and Statistics, Elsevier, vol. 32(C), pages 17-33.
    5. Yukari Shirota & Basabi Chakraborty, 2022. "Amplitude-Based Time Series Data Clustering Method," Gakushuin Economic Papers, Gakushuin University, Faculty of Economics, vol. 59(2), pages 127-140.
    6. Filippou, Ilias & Maurer, Thomas A. & Pezzo, Luca & Taylor, Mark P., 2024. "Importance of transaction costs for asset allocation in foreign exchange markets," Journal of Financial Economics, Elsevier, vol. 159(C).
    7. Bian, Zhicun & Liao, Yin & O’Neill, Michael & Shi, Jing & Zhang, Xueyong, 2020. "Large-scale minimum variance portfolio allocation using double regularization," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    8. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    9. Foteini Kyriazi & Sophia Tarani & Dimitrios D. Thomakos, 2025. "Median-adaptive portfolios: a minimum criteria approach to asset allocation," Annals of Operations Research, Springer, vol. 353(1), pages 377-400, October.
    10. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
    11. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    12. Kouaissah, Noureddine, 2021. "Using multivariate stochastic dominance to enhance portfolio selection and warn of financial crises," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 480-493.
    13. Hongxin Zhao & Yilun Jiang & Yizhou Yang, 2023. "Robust and Sparse Portfolio: Optimization Models and Algorithms," Mathematics, MDPI, vol. 11(24), pages 1-20, December.
    14. Stollenwerk, Michael, 2026. "Probability distributions for realized covariance measures," Journal of Econometrics, Elsevier, vol. 254(PA).
    15. Li, Jie & Han, Yingwei, 2025. "Nonlinear hedging climate policy uncertainty: A dynamic mixed copula approach," Economic Modelling, Elsevier, vol. 151(C).
    16. Feldkircher, Martin & Gruber, Thomas & Huber, Florian, 2020. "International effects of a compression of euro area yield curves," Journal of Banking & Finance, Elsevier, vol. 113(C).
    17. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    18. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).
    19. Hafner, Christian & Wang, Linqi, 2020. "Dynamic portfolio selection with sector-specific regularization," LIDAM Discussion Papers ISBA 2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  8. Andersen, Torben G. & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2017. "Volatility, information feedback and market microstructure noise: A tale of two regimes," CFS Working Paper Series 569, Center for Financial Studies (CFS).

    Cited by:

    1. Simon Clinet & Yoann Potiron, 2021. "Estimation for high-frequency data under parametric market microstructure noise," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 649-669, August.
    2. Clinet, Simon & Potiron, Yoann, 2019. "Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 289-337.

  9. Gould, Martin D. & Hautsch, Nikolaus & Howison, Sam D. & Porter, Mason A., 2017. "Counterparty credit limits: An effective tool for mitigating counterparty risk?," CFS Working Paper Series 581, Center for Financial Studies (CFS).

    Cited by:

    1. Deimante Teresiene & Beatrice Gudaviciute, 2021. "Counterparty risk management framework: theoretical approach in COVID-19 environment," Technium Social Sciences Journal, Technium Science, vol. 17(1), pages 184-193, March.

  10. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).

    Cited by:

    1. Dissanayake, Pushpa & Flock, Teresa & Meier, Johanna & Sibbertsen, Philipp, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Hannover Economic Papers (HEP) dp-690, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    3. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    4. Inés Jiménez & Andrés Mora-Valencia & Javier Perote, 2026. "Cross-moment interaction in multivariate semi-nonparametric densities for risk forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-22, December.
    5. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    6. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    7. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    8. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).
    9. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).

  11. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," Cambridge Working Papers in Economics 1464, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Yasin Simsek, 2025. "Spot Regressions with Candlesticks," Papers 2510.12911, arXiv.org, revised Apr 2026.
    2. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    3. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    4. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.
    5. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
    6. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    7. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    8. Chae-Deug, Yi, 2024. "Realized normal volatility and maximum outlying jumps in high frequency returns for Korean won–US Dollar," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    9. Kim Christensen & Ulrich Hounyo & Zhi Liu, 2024. "A nonparametric test for diurnal variation in spot correlation processes," Papers 2408.02757, arXiv.org, revised Jan 2026.
    10. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.
    11. Yucheng Sun, 2024. "Testing for jumps with robust spot volatility estimators," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 79-104, February.
    12. Bibinger, Markus & Madensoy, Mehmet, 2019. "Change-point inference on volatility in noisy Itô semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 4878-4925.
    13. Bibinger, Markus & Neely, Christopher & Winkelmann, Lars, 2019. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 158-184.
    14. Todorov, Viktor & Zhang, Yang, 2023. "Bias reduction in spot volatility estimation from options," Journal of Econometrics, Elsevier, vol. 234(1), pages 53-81.
    15. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org, revised Aug 2025.
    16. Jacod, Jean & Mykland, Per A., 2015. "Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2910-2936.
    17. Jir^o Akahori & Nien-Lin Liu & Maria Elvira Mancino & Tommaso Mariotti & Yukie Yasuda, 2023. "Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix," Papers 2304.04372, arXiv.org.
    18. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    19. Hautsch, Nikolaus & Horvath, Akos, 2019. "How effective are trading pauses?," Journal of Financial Economics, Elsevier, vol. 131(2), pages 378-403.
    20. Zhang, Congshan & Li, Jia & Bollerslev, Tim, 2022. "Occupation density estimation for noisy high-frequency data," Journal of Econometrics, Elsevier, vol. 227(1), pages 189-211.
    21. Jakob Albers & Mihai Cucuringu & Sam Howison & Alexander Y. Shestopaloff, 2021. "Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets," Papers 2108.09750, arXiv.org.
    22. Laurent, Sébastien & Renò, Roberto & Shi, Shuping, 2026. "Realized drift," Journal of Econometrics, Elsevier, vol. 254(PA).
    23. Torben G. Andersen & Martin Thyrsgaard & Viktor Todorov, 2021. "Recalcitrant betas: Intraday variation in the cross‐sectional dispersion of systematic risk," Quantitative Economics, Econometric Society, vol. 12(2), pages 647-682, May.
    24. Ulrich Hounyo & Zhi Liu & Rasmus T. Varneskov, 2023. "Bootstrapping Laplace transforms of volatility," Quantitative Economics, Econometric Society, vol. 14(3), pages 1059-1103, July.

  12. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    2. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2018. "Dynamic and granular loss reserving with copulae," Papers 1801.01792, arXiv.org.

  13. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2014. "Systemic risk spillovers in the European banking and sovereign network," CFS Working Paper Series 467, Center for Financial Studies (CFS).

    Cited by:

    1. Naeem, Muhammad Abubakr & Yousaf, Imran & Karim, Sitara & Yarovaya, Larisa & Ali, Shoaib, 2023. "Tail-event driven NETwork dependence in emerging markets," Emerging Markets Review, Elsevier, vol. 55(C).
    2. Huang, Shoujun & Bossman, Ahmed & Gubareva, Mariya & Teplova, Tamara, 2024. "International transmission of shocks and African forex markets," Energy Economics, Elsevier, vol. 131(C).
    3. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Wu, Shan & Liu, Yilong & Song, Ziyu & Zhou, Yuqin & Guo, Wenjing, 2024. "Network structure, dynamic evolution and block characteristics of sovereign debt risk: The global evidence," Research in International Business and Finance, Elsevier, vol. 72(PA).
    5. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    6. Billah, Mabruk & Elsayed, Ahmed H. & Rabbani, Mustafa Raza & Shaik, Muneer, 2025. "Decoding investment strategies across agricultural commodities, Islamic equities, and Sukuk markets," Research in International Business and Finance, Elsevier, vol. 80(C).
    7. Ben Amor, Souhir & Althof, Michael & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter for emerging markets," Research in International Business and Finance, Elsevier, vol. 60(C).
    8. Erick Treviño Aguilar, 2020. "The interdependency structure in the Mexican stock exchange: A network approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-31, October.
    9. Zhang, Xingmin & Zhang, Shuai & Lu, Liping, 2022. "The banking instability and climate change: Evidence from China," Energy Economics, Elsevier, vol. 106(C).
    10. Christian Gross & Pierre L. Siklos, 2019. "Analyzing Credit Risk Transmission to the Non-Financial Sector in Europe: A Network Approach," CAMA Working Papers 2019-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Sarah Vella, 2025. "Constructing a country-specific indicator for cyclical systemic risk," Economic Change and Restructuring, Springer, vol. 58(3), pages 1-63, June.
    12. Buch, Claudia M. & Koetter, Michael & Ohls, Jana, 2016. "Banks and sovereign risk: A granular view," Journal of Financial Stability, Elsevier, vol. 25(C), pages 1-15.
    13. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    14. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    15. Shahzad, Syed Jawad Hussain & Hoang, Thi Hong Van & Arreola-Hernandez, Jose, 2019. "Risk spillovers between large banks and the financial sector: Asymmetric evidence from Europe," Finance Research Letters, Elsevier, vol. 28(C), pages 153-159.
    16. Arnold, Ivo J.M. & Soederhuizen, Beau, 2018. "Sovereign bond holdings and monetary policy operations in the euro area," Journal of Policy Modeling, Elsevier, vol. 40(6), pages 1243-1254.
    17. Deng, Yang & Zhang, Ziqing & Zhu, Li, 2021. "A model-based index for systemic risk contribution measurement in financial networks," Economic Modelling, Elsevier, vol. 95(C), pages 35-48.
    18. Nistor Simona, 2017. "Banks’ Vulnerability and Financial Openness across Central and Eastern Europe," Studia Universitatis Babeș-Bolyai Oeconomica, Paradigm, vol. 62(3), pages 47-66, December.
    19. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    20. Beibei Zhang & Xuemei Xie & Chunmei Li, 2023. "How Connected Is China’s Systemic Financial Risk Contagion Network?—A Dynamic Network Perspective Analysis," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    21. Wen, Fenghua & Wang, Kangsheng & Zeng, Aiqing, 2024. "Return spillover across the carbon market and financial markets: A quantile-based approach," Research in International Business and Finance, Elsevier, vol. 69(C).
    22. Clancy, Daragh & Gabriele, Carmine & Žigraiová, Diana, 2022. "Sovereign bond market spillovers from crisis-time developments in Greece," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    23. Borjigin, Sumuya & Gao, Ting & Sun, Yafei & An, Biao, 2020. "For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    24. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    25. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    26. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2019. "Effectiveness of policy and regulation in European sovereign credit risk markets: a network analysis," ESRB Working Paper Series 90, European Systemic Risk Board.
    27. Caterina Pastorino & Pierpaolo Uberti, 2024. "An empirical comparison of correlation-based systemic risk measures," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(3), pages 2289-2314, June.
    28. Linh H. Nguyen & Linh X. D. Nguyen & Linzhi Tan, 2021. "Tail risk connectedness between US industries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3624-3650, July.
    29. Billah, Mabruk & Karim, Sitara & Naeem, Muhammad Abubakr & Vigne, Samuel A., 2022. "Return and volatility spillovers between energy and BRIC markets: Evidence from quantile connectedness," Research in International Business and Finance, Elsevier, vol. 62(C).
    30. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    31. Sophie Béreau & Nicolas Debarsy & Cyrille Dossougoin & Jean-Yves Gnabo, 2022. "Contagion in the Banking Industry: a Robust-to-Endogeneity Analysis," Working Papers halshs-03513049, HAL.
    32. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2022. "Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?," Journal of International Economics, Elsevier, vol. 139(C).
    33. Lim, Seo-Yeon & Choi, Sun-Yong, 2024. "Dynamic credit risk transmissions among global major industries: Evidence from the TVP-VAR spillover approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    34. Costola, Michele & Lorusso, Marco, 2021. "Spillovers among Energy Commodities and the Russian Stock Market," MPRA Paper 108990, University Library of Munich, Germany.
    35. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile spillovers and connectedness analysis between oil and African stock markets," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 60-83.
    36. Cao, Guangxi & Xie, Wenhao, 2022. "Detrended multiple moving average cross-correlation analysis and its application in the correlation measurement of stock market in Shanghai, Shenzhen, and Hong Kong," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    37. Liu, Jiahao & Shen, Wenyu, 2024. "Financial instability in Europe: Does geopolitical risk from proximate countries and trading partners matter?," Finance Research Letters, Elsevier, vol. 66(C).
    38. Liu, Bing-Yue & Fan, Ying & Ji, Qiang & Hussain, Nazim, 2022. "High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system," Energy Economics, Elsevier, vol. 105(C).
    39. Miao He & Yanhong Guo, 2022. "Systemic Risk Contributions of Financial Institutions during the Stock Market Crash in China," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    40. Foglia, Matteo & Angelini, Eliana, 2020. "The diabolical sovereigns/banks risk loop: A VAR quantile design," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    41. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    42. Ali Namaki & Jamshid Ardalankia & Reza Raei & Leila Hedayatifar & Ali Hosseiny & Emmanuel Haven & G. Reza Jafari, 2020. "Analysis of the Global Banking Network by Random Matrix Theory," Papers 2007.14447, arXiv.org.
    43. Chen, Yan & Wang, Gang-Jin & Zhu, You & Xie, Chi & Uddin, Gazi Salah, 2023. "Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China," Global Finance Journal, Elsevier, vol. 58(C).
    44. G. Chiesa & J. M. Mansilla-Fern ndez, 2018. "Non-Performing Loans, Cost of Capital, and Lending Supply: Lessons from the Eurozone Banking Crisi," Working Papers wp1124, Dipartimento Scienze Economiche, Universita' di Bologna.
    45. Nandita Bhattacharjee & Ambika Prasad Pati, 2023. "Exploring Systemic Risk Measurement Issues in Shadow Banks: A Case of an Emerging Economy," South Asian Journal of Macroeconomics and Public Finance, , vol. 12(2), pages 186-217, December.
    46. Arreola Hernandez, Jose & Kang, Sang Hoon & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2020. "Spillovers and diversification potential of bank equity returns from developed and emerging America," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    47. He, Yue & Tao, Miaomiao & Ren, Xiaohang, 2025. "U.S. interbank risk spillover network: Temporal dynamics and external shocks," Finance Research Letters, Elsevier, vol. 85(PB).
    48. Sheikh, Umaid A. & Asadi, Mehrad & Roubaud, David & Hammoudeh, Shawkat, 2024. "Global uncertainties and Australian financial markets: Quantile time-frequency connectedness," International Review of Financial Analysis, Elsevier, vol. 92(C).
    49. Fei Wu & Zhiwei Zhang & Dayong Zhang & Qiang Ji, 2023. "Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach," Annals of Operations Research, Springer, vol. 330(1), pages 119-153, November.
    50. Y'erali Gandica & Sophie B'ereau & Jean-Yves Gnabo, 2019. "A multilevel analysis to systemic exposure: insights from local and system-wide information," Papers 1910.08611, arXiv.org.
    51. Md. Saiful Islam & Md. Monirul Islam & Faroque Ahmed & Anis ur Rehman & Md. Fakhre Alam & Md. Aynul Islam, 2026. "Exploring Nexus Between Oil Price Shocks and Copper Production: Analysing the Role of Mineral Prices and Geopolitical Factors in Saudi Arabia," Computational Economics, Springer;Society for Computational Economics, vol. 67(2), pages 1375-1412, February.
    52. Liao, Yin & Pan, Zheyao, 2022. "Extreme risk connectedness among global major financial institutions: Links to globalization and emerging market fear," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    53. Nekhili, Ramzi & Foglia, Matteo & Bouri, Elie, 2023. "European bank credit risk transmission during the credit Suisse collapse," Finance Research Letters, Elsevier, vol. 58(PB).
    54. Abbassi, Puriya & Brownlees, Christian & Hans, Christina & Podlich, Natalia, 2016. "Credit risk interconnectedness: What does the market really know?," Discussion Papers 09/2016, Deutsche Bundesbank.
    55. Foglia, Matteo & Angelini, Eliana, 2020. "From me to you: Measuring connectedness between Eurozone financial institutions," Research in International Business and Finance, Elsevier, vol. 54(C).
    56. Billah, Mabruk, 2025. "Unraveling financial interconnectedness: A quantile VAR model analysis of AI-based assets, sukuk, and islamic equity indices," Research in International Business and Finance, Elsevier, vol. 75(C).
    57. Pham, Thach N. & Powell, Robert & Bannigidadmath, Deepa, 2024. "Tail risk network analysis of Asian banks," Global Finance Journal, Elsevier, vol. 62(C).
    58. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    59. Mokni, Khaled & Al-Shboul, Mohammed & Assaf, Ata, 2021. "Economic policy uncertainty and dynamic spillover among precious metals under market conditions: Does COVID-19 have any effects?," Resources Policy, Elsevier, vol. 74(C).
    60. Jingyi TIAN & Jun NAGAYASU, 2024. "AI and Financial Systemic Risk in the Global Market," TUPD Discussion Papers 55, Graduate School of Economics and Management, Tohoku University.
    61. Moratis, Georgios & Sakellaris, Plutarchos, 2021. "Measuring the systemic importance of banks," Journal of Financial Stability, Elsevier, vol. 54(C).
    62. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    63. Wang, Dieter & van Lelyveld, Iman & Schaumburg, Julia, 2019. "Do information contagion and business model similarities explain bank credit risk commonalities?," ESRB Working Paper Series 94, European Systemic Risk Board.
    64. Bellavite Pellegrini, Carlo & Meoli, Michele & Urga, Giovanni, 2017. "Money market funds, shadow banking and systemic risk in United Kingdom," Finance Research Letters, Elsevier, vol. 21(C), pages 163-171.
    65. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    66. Foglia, Matteo & Di Tommaso, Caterina & Wang, Gang-Jin & Pacelli, Vincenzo, 2024. "Interconnectedness between stock and credit markets: The role of European G-SIBs in a multilayer perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    67. Jose Arreola Hernandez & Sang Hoon Kang & Ron P. McIver & Seong-Min Yoon, 2021. "Network Interdependence and Optimization of Bank Portfolios from Developed and Emerging Asia Pacific Countries," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 613-647, December.
    68. Chen, Bin-xia & Sun, Yan-lin, 2023. "Extreme risk contagion between international crude oil and China's energy-intensive sectors: New evidence from quantile Granger causality and spillover methods," Energy, Elsevier, vol. 285(C).
    69. Li, Wenwei & Hommel, Ulrich & Paterlini, Sandra, 2018. "Network topology and systemic risk: Evidence from the Euro Stoxx market," Finance Research Letters, Elsevier, vol. 27(C), pages 105-112.
    70. Mengting Fan & Zan Mo & Qizhi Zhao & Yi Liang & Shaoyang Guo & Yanjun Chen, 2024. "RETRACTED ARTICLE: Unraveling Double Shocks: An In-Depth Analysis of Risk Contagion in China’s Inter-Bank Market," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 20251-20298, December.
    71. Dai, Zhifeng & Zhang, Xiaotong & Yin, Zhujia, 2023. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 118(C).
    72. Huang, Zhigang & Zhang, Weilan, 2024. "Exploring the Spillover effects of tail risk fluctuations in the RMB exchange rate—The time-frequency and quantile connectivity perspective," Research in International Business and Finance, Elsevier, vol. 72(PB).
    73. Lin, Boqiang & Zhang, Zongyou, 2025. "Extreme spillovers among green finance, energy, and energy metals markets in China: Evidence under the dilemma of energy transition," Renewable Energy, Elsevier, vol. 241(C).
    74. Huang, Shoujun & Gubareva, Mariya & Teplova, Tamara & Bossman, Ahmed, 2024. "African forex markets: Modeling their predictability and the asymmetric effects of oil and geopolitical risk," Energy Economics, Elsevier, vol. 136(C).
    75. Walid Mensi & Ismail O. Fasanya & Xuan Vinh Vo & Sang Hoon Kang, 2025. "Dynamics of extreme spillovers across European sustainability markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(1), pages 225-258, March.
    76. Zhu, Bo & Liu, Jiahao & Lin, Renda & Chevallier, Julien, 2021. "Cross-border systemic risk spillovers in the global oil system: Does the oil trade pattern matter?," Energy Economics, Elsevier, vol. 101(C).
    77. Sui, Xin & Li, Liang & Chen, Xiaohui, 2020. "Risk contagion caused by interactions between credit and guarantee networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    78. Ahmed Khalif & Massimiliano Caporin & Michele Costola & Shawkat Hammoudeh, 2021. "Systemic Risk for Financial Institutions in the Major Petroleum-based Economies: The Role of Oil," The Energy Journal, , vol. 42(6), pages 247-274, November.
    79. Apostolakis, George N. & Floros, Christos & Giannellis, Nikolaos, 2022. "On bank return and volatility spillovers: Identifying transmitters and receivers during crisis periods," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 156-176.
    80. Foglia, Matteo & Addi, Abdelhamid & Wang, Gang-Jin & Angelini, Eliana, 2022. "Bearish Vs Bullish risk network: A Eurozone financial system analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    81. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 678-696, January.
    82. Ghosh, Bikramaditya & Gubareva, Mariya & Ghosh, Anandita & Papadas, Dimitrios & Vo, Xuan Vinh, 2025. "Food, harvesting and interest rate nexus: Quantile investigation about dependencies and spillover," International Economics, Elsevier, vol. 182(C).
    83. Mensi, Walid & El Khoury, Rim & Al-Kharusi, Sami & Kang, Sang Hoon, 2024. "Extreme dynamic connectedness and hedging strategy across commodity, bond, currency, and stock markets: Evidence from Asian Pacific, Canada, Mexico, and US countries," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    84. Yaling Chen & Qinnan Jiang & Zhifeng Dai, 2026. "Systemic Risk Spillover of Oil, Gold to China Financial Market: New Evidence From a Copula-CoVaR-MODWT Approach," Evaluation Review, , vol. 50(3), pages 384-422, June.
    85. Su, Xianfang, 2020. "Measuring extreme risk spillovers across international stock markets: A quantile variance decomposition analysis," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    86. Khalfaoui, Rabeh & Hammoudeh, Shawkat & Rehman, Mohd Ziaur, 2023. "Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network," Emerging Markets Review, Elsevier, vol. 54(C).
    87. Brownlees, Christian & Hans, Christina & Nualart, Eulalia, 2021. "Bank credit risk networks: Evidence from the Eurozone," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 585-599.
    88. Shouwei Li & Shihang Wen, 2017. "Multiplex Networks of the Guarantee Market: Evidence from China," Complexity, Hindawi, vol. 2017, pages 1-7, July.
    89. Xiaoye Jin, 2024. "Salience theory value spillovers between China’s systemically important banks: evidence from quantile connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-39, December.
    90. Foglia, Matteo & Addi, Abdelhamid & Angelini, Eliana, 2022. "The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness," Global Finance Journal, Elsevier, vol. 51(C).
    91. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    92. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    93. Nadal De Simone, Francisco, 2021. "Measuring the deadly embrace: Systemic and sovereign risks," Research in International Business and Finance, Elsevier, vol. 56(C).
    94. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.
    95. Dong, Xiyong & Yoon, Seong-Min, 2023. "Effect of weather and environmental attentions on financial system risks: Evidence from Chinese high- and low-carbon assets," Energy Economics, Elsevier, vol. 121(C).
    96. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    97. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Extreme spillovers among fossil energy, clean energy, and metals markets: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 107(C).

  14. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.

  15. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," SFB 649 Discussion Papers 2014-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Bibinger, Markus & Neely, Christopher & Winkelmann, Lars, 2018. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," IRTG 1792 Discussion Papers 2018-055, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Yasin Simsek, 2025. "Spot Regressions with Candlesticks," Papers 2510.12911, arXiv.org, revised Apr 2026.
    3. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    4. Mustafayeva, Konul & Wang, Weining, 2020. "Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data," IRTG 1792 Discussion Papers 2020-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    6. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.
    7. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
    8. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    9. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    10. Chae-Deug, Yi, 2024. "Realized normal volatility and maximum outlying jumps in high frequency returns for Korean won–US Dollar," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    11. Kim Christensen & Ulrich Hounyo & Zhi Liu, 2024. "A nonparametric test for diurnal variation in spot correlation processes," Papers 2408.02757, arXiv.org, revised Jan 2026.
    12. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.
    13. Yucheng Sun, 2024. "Testing for jumps with robust spot volatility estimators," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 79-104, February.
    14. Bibinger, Markus & Madensoy, Mehmet, 2019. "Change-point inference on volatility in noisy Itô semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 4878-4925.
    15. Todorov, Viktor & Zhang, Yang, 2023. "Bias reduction in spot volatility estimation from options," Journal of Econometrics, Elsevier, vol. 234(1), pages 53-81.
    16. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org, revised Aug 2025.
    17. Jacod, Jean & Mykland, Per A., 2015. "Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2910-2936.
    18. Jir^o Akahori & Nien-Lin Liu & Maria Elvira Mancino & Tommaso Mariotti & Yukie Yasuda, 2023. "Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix," Papers 2304.04372, arXiv.org.
    19. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    20. Hautsch, Nikolaus & Horvath, Akos, 2019. "How effective are trading pauses?," Journal of Financial Economics, Elsevier, vol. 131(2), pages 378-403.
    21. Zhang, Congshan & Li, Jia & Bollerslev, Tim, 2022. "Occupation density estimation for noisy high-frequency data," Journal of Econometrics, Elsevier, vol. 227(1), pages 189-211.
    22. Jakob Albers & Mihai Cucuringu & Sam Howison & Alexander Y. Shestopaloff, 2021. "Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets," Papers 2108.09750, arXiv.org.
    23. Laurent, Sébastien & Renò, Roberto & Shi, Shuping, 2026. "Realized drift," Journal of Econometrics, Elsevier, vol. 254(PA).
    24. Torben G. Andersen & Martin Thyrsgaard & Viktor Todorov, 2021. "Recalcitrant betas: Intraday variation in the cross‐sectional dispersion of systematic risk," Quantitative Economics, Econometric Society, vol. 12(2), pages 647-682, May.
    25. Ulrich Hounyo & Zhi Liu & Rasmus T. Varneskov, 2023. "Bootstrapping Laplace transforms of volatility," Quantitative Economics, Econometric Society, vol. 14(3), pages 1059-1103, July.

  16. Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    3. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    4. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    5. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    7. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    8. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    9. Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    10. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling and Estimation," Papers 2206.14275, arXiv.org, revised Jan 2025.
    11. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    12. Taras Bodnar & Mathias Lindholm & Vilhelm Niklasson & Erik Thors'en, 2020. "Bayesian Quantile-Based Portfolio Selection," Papers 2012.01819, arXiv.org.
    13. Lai, Yu-Sheng, 2023. "Economic evaluation of dynamic hedging strategies using high-frequency data," Finance Research Letters, Elsevier, vol. 57(C).
    14. Alfelt, Gustav & Bodnar, Taras & Javed, Farrukh & Tyrcha, Joanna, 2020. "Singular conditional autoregressive Wishart model for realized covariance matrices," Working Papers 2021:1, Örebro University, School of Business.
    15. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
    16. Pirino, Davide & Pollastri, Alessandro & Trapin, Luca, 2025. "Testing liquidity: A statistical theory based on asset staleness," Econometrics and Statistics, Elsevier, vol. 35(C), pages 23-40.
    17. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023. "The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
    18. Li, Yifan & Nolte, Ingmar & Vasios, Michalis & Voev, Valeri & Xu, Qi, 2022. "Weighted Least Squares Realized Covariation Estimation," Journal of Banking & Finance, Elsevier, vol. 137(C).
    19. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    20. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    21. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    22. Lai, Yu-Sheng, 2025. "Trading-hour and nontrading-hour volatility in crude oil and U.S. dollar markets and its implications for portfolio optimization," Journal of Commodity Markets, Elsevier, vol. 38(C).
    23. Taras Bodnar & Nestor Parolya & Erik Thorsen, 2021. "Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio," Papers 2106.02131, arXiv.org, revised Nov 2021.
    24. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    25. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    26. Roland Weigand, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," Working Papers 144, Bavarian Graduate Program in Economics (BGPE).
    27. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    28. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    29. Bodnar, Taras & Lindholm, Mathias & Niklasson, Vilhelm & Thorsén, Erik, 2022. "Bayesian portfolio selection using VaR and CVaR," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    30. Laura Capera Romero & Anne Opschoor, 2024. "Realized Variances vs. Correlations: Unlocking the Gains in Multivariate Volatility Forecasting," Tinbergen Institute Discussion Papers 24-059/III, Tinbergen Institute.
    31. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    32. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    33. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.
    34. Oh, Dong Hwan & Patton, Andrew J., 2016. "High-dimensional copula-based distributions with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 349-366.
    35. Yu-Sheng Lai, 2018. "Dynamic hedging with futures: a copula-based GARCH model with high-frequency data," Review of Derivatives Research, Springer, vol. 21(3), pages 307-329, October.

  17. Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    3. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    4. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    5. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    7. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    8. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    9. Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    10. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling and Estimation," Papers 2206.14275, arXiv.org, revised Jan 2025.
    11. Dong Hwan Oh & Andrew J. Patton, 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    12. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    13. Taras Bodnar & Mathias Lindholm & Vilhelm Niklasson & Erik Thors'en, 2020. "Bayesian Quantile-Based Portfolio Selection," Papers 2012.01819, arXiv.org.
    14. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    15. Lai, Yu-Sheng, 2023. "Economic evaluation of dynamic hedging strategies using high-frequency data," Finance Research Letters, Elsevier, vol. 57(C).
    16. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    17. Alfelt, Gustav & Bodnar, Taras & Javed, Farrukh & Tyrcha, Joanna, 2020. "Singular conditional autoregressive Wishart model for realized covariance matrices," Working Papers 2021:1, Örebro University, School of Business.
    18. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
    19. Pirino, Davide & Pollastri, Alessandro & Trapin, Luca, 2025. "Testing liquidity: A statistical theory based on asset staleness," Econometrics and Statistics, Elsevier, vol. 35(C), pages 23-40.
    20. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023. "The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
    21. Li, Yifan & Nolte, Ingmar & Vasios, Michalis & Voev, Valeri & Xu, Qi, 2022. "Weighted Least Squares Realized Covariation Estimation," Journal of Banking & Finance, Elsevier, vol. 137(C).
    22. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    23. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    24. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    25. Lai, Yu-Sheng, 2025. "Trading-hour and nontrading-hour volatility in crude oil and U.S. dollar markets and its implications for portfolio optimization," Journal of Commodity Markets, Elsevier, vol. 38(C).
    26. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    27. Taras Bodnar & Nestor Parolya & Erik Thorsen, 2021. "Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio," Papers 2106.02131, arXiv.org, revised Nov 2021.
    28. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    29. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    30. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    31. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    32. Bodnar, Taras & Lindholm, Mathias & Niklasson, Vilhelm & Thorsén, Erik, 2022. "Bayesian portfolio selection using VaR and CVaR," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    33. Laura Capera Romero & Anne Opschoor, 2024. "Realized Variances vs. Correlations: Unlocking the Gains in Multivariate Volatility Forecasting," Tinbergen Institute Discussion Papers 24-059/III, Tinbergen Institute.
    34. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    35. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    36. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    37. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.
    38. Yu-Sheng Lai, 2018. "Dynamic hedging with futures: a copula-based GARCH model with high-frequency data," Review of Derivatives Research, Springer, vol. 21(3), pages 307-329, October.

  18. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2013. "Forecasting systemic impact in financial networks," SFB 649 Discussion Papers 2013-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    3. Simone Varotto & Lei Zhao, 2014. "Systemic Risk and Bank Size," ICMA Centre Discussion Papers in Finance icma-dp2014-17, Henley Business School, University of Reading.
    4. Paolo Giudici & Shatha Hashem, 2015. "Systemic risk of Islamic Banks," DEM Working Papers Series 103, University of Pavia, Department of Economics and Management.
    5. Huaming Du & Cancan Feng & Yuqian Lei & Chenyang Zhang & Guisong Liu & Gang Kou & Carl Yang & Yu Zhao, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective," Papers 2211.14997, arXiv.org, revised Mar 2026.
    6. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    7. Shan, Yuan George & Wang, Yirui & Wu, Wuqing & Zhen, Weihao, 2023. "Does the Achilles heel of guarantee networks drive financial distress?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Kleinow, Jacob & Moreira, Fernando, 2016. "Systemic risk among European banks: A copula approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 27-42.
    9. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    10. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
    11. van de Leur, Michiel C.W. & Lucas, André & Seeger, Norman J., 2017. "Network, market, and book-based systemic risk rankings," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 84-90.
    12. Hong Fan & Allan Alvin Lee Lukaya Amalia & Qian Qian Gao, 2018. "The Assessment of Systemic Risk in the Kenyan Banking Sector," Complexity, Hindawi, vol. 2018, pages 1-15, January.
    13. Linh H. Nguyen & Linh X. D. Nguyen & Linzhi Tan, 2021. "Tail risk connectedness between US industries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3624-3650, July.
    14. Barigozzi, Matteo & Hallin, Marc, 2017. "A network analysis of the volatility of high-dimensionalfinancial series," LSE Research Online Documents on Economics 67456, London School of Economics and Political Science, LSE Library.
    15. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    16. Jacob Kleinow & Tobias Nell, 2015. "Determinants of systemically important banks: the case of Europe," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 7(4), pages 446-476, November.
    17. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2025. "Quantile graphical models: Prediction and conditional independence with applications to systemic risk," Journal of Econometrics, Elsevier, vol. 252(PA).
    18. Naeem, Muhammad Abubakr, 2024. "Navigating median and extreme volatility in stock markets: Implications for portfolio strategies," International Review of Economics & Finance, Elsevier, vol. 95(C).
    19. Petre Caraiani, 2020. "Forecasting Financial Networks," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 983-997, March.
    20. Chen, Jia & Li, Degui & Li, Yu-Ning & Linton, Oliver, 2025. "Estimating time-varying networks for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 249(PC).
    21. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2016. "Systemic risk spillovers in the European banking and sovereign network," Working Paper Series in Economics 79, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    22. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    23. Wang, Gang-Jin & Jiang, Zhi-Qiang & Lin, Min & Xie, Chi & Stanley, H. Eugene, 2018. "Interconnectedness and systemic risk of China's financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 1-18.
    24. Constantin, Andreea & Peltonen, Tuomas A. & Sarlin, Peter, 2018. "Network linkages to predict bank distress," Journal of Financial Stability, Elsevier, vol. 35(C), pages 226-241.
    25. Pham, Thach N. & Powell, Robert & Bannigidadmath, Deepa, 2024. "Tail risk network analysis of Asian banks," Global Finance Journal, Elsevier, vol. 62(C).
    26. Matteo Foglia & Eliana Angelini, 2021. "The triple (T3) dimension of systemic risk: Identifying systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 7-26, January.
    27. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    28. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    29. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    30. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    31. Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    32. Arnold Polanski & Evarist Stoja, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England Staff Working Paper series 660, Bank of England.
    33. Bellavite Pellegrini, Carlo & Cincinelli, Peter & Meoli, Michele & Urga, Giovanni, 2022. "The contribution of (shadow) banks and real estate to systemic risk in China," Journal of Financial Stability, Elsevier, vol. 60(C).
    34. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    35. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    36. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    37. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    38. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).

  19. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2013. "Estimating the quadratic covariation matrix from noisy observations: Local method of moments and efficiency," SFB 649 Discussion Papers 2013-017, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Bibinger, Markus & Neely, Christopher & Winkelmann, Lars, 2018. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," IRTG 1792 Discussion Papers 2018-055, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    3. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    4. Donggyu Kim & Xinyu Song & Yazhen Wang, 2020. "Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency," Papers 2006.12039, arXiv.org.
    5. Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020. "A dynamic conditional approach to portfolio weights forecasting," Papers 2004.12400, arXiv.org.
    6. Reiß, Markus & Todorov, Viktor & Tauchen, George, 2015. "Nonparametric test for a constant beta between Itô semi-martingales based on high-frequency data," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 2955-2988.
    7. Hautsch, Nikolaus & Horvath, Akos, 2017. "How effective are trading pauses?," CFS Working Paper Series 571, Center for Financial Studies (CFS).
    8. Altmeyer, Randolf & Bibinger, Markus, 2014. "Functional stable limit theorems for efficient spectral covolatility estimators," SFB 649 Discussion Papers 2014-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Jean Jacod, 2019. "Estimation of volatility in a high-frequency setting: a short review," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 351-385, December.
    10. Andersen, Torben G. & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2017. "Volatility, information feedback and market microstructure noise: A tale of two regimes," CFS Working Paper Series 569, Center for Financial Studies (CFS).
    11. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    12. Altmeyer, Randolf & Bibinger, Markus, 2015. "Functional stable limit theorems for quasi-efficient spectral covolatility estimators," Stochastic Processes and their Applications, Elsevier, vol. 125(12), pages 4556-4600.
    13. Richard Y. Chen & Per A. Mykland, 2015. "Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data," Papers 1512.06159, arXiv.org, revised Oct 2018.
    14. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    15. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    16. Laurini, Márcio Poletti & Ohashi, Alberto, 2015. "A noisy principal component analysis for forward rate curves," European Journal of Operational Research, Elsevier, vol. 246(1), pages 140-153.
    17. Lars Winkelmann & Markus Bibinger & Tobias Linzert, 2016. "ECB Monetary Policy Surprises: Identification Through Cojumps in Interest Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 613-629, June.
    18. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    19. Ogihara, Teppei & Yoshida, Nakahiro, 2014. "Quasi-likelihood analysis for nonsynchronously observed diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 124(9), pages 2954-3008.
    20. Jianqing Fan & Alex Furger & Dacheng Xiu, 2016. "Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 489-503, October.
    21. Fan, Jianqing & Kim, Donggyu, 2019. "Structured volatility matrix estimation for non-synchronized high-frequency financial data," Journal of Econometrics, Elsevier, vol. 209(1), pages 61-78.
    22. Bibinger, Markus & Winkelmann, Lars, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers 2013-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    24. Linzert, Tobias & Winkelmann, Lars & Bibinger, Markus, 2014. "ECB monetary policy surprises: identification through cojumps in interest rates," Working Paper Series 1674, European Central Bank.
    25. Richard Y. Chen, 2018. "Inference for Volatility Functionals of Multivariate It\^o Semimartingales Observed with Jump and Noise," Papers 1810.04725, arXiv.org, revised Nov 2019.
    26. Friedrich Poeschel, 2013. "Assortative matching through signals," 2013 Papers ppo178, Job Market Papers.
    27. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," Cambridge Working Papers in Economics 1464, Faculty of Economics, University of Cambridge.
    28. Alberto Ohashi & Alexandre B Simas, 2015. "Principal Components Analysis for Semimartingales and Stochastic PDE," Papers 1503.05909, arXiv.org, revised Mar 2016.

  20. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2013. "Forecasting systemic impact in financial networks," SFB 649 Discussion Papers 2013-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ben Craig & Martín Saldías, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers 2016/184, International Monetary Fund.
    2. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Matteo Barigozzi & Christian T. Brownlees, 2013. "Nets: Network estimation for time series," Economics Working Papers 1391, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    5. Simone Varotto & Lei Zhao, 2014. "Systemic Risk and Bank Size," ICMA Centre Discussion Papers in Finance icma-dp2014-17, Henley Business School, University of Reading.
    6. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2013. "Financial network systemic risk contributions," CFS Working Paper Series 2013/20, Center for Financial Studies (CFS).
    7. Paolo Giudici & Shatha Hashem, 2015. "Systemic risk of Islamic Banks," DEM Working Papers Series 103, University of Pavia, Department of Economics and Management.
    8. Huaming Du & Cancan Feng & Yuqian Lei & Chenyang Zhang & Guisong Liu & Gang Kou & Carl Yang & Yu Zhao, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective," Papers 2211.14997, arXiv.org, revised Mar 2026.
    9. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    10. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    11. Shan, Yuan George & Wang, Yirui & Wu, Wuqing & Zhen, Weihao, 2023. "Does the Achilles heel of guarantee networks drive financial distress?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    12. Kleinow, Jacob & Moreira, Fernando, 2016. "Systemic risk among European banks: A copula approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 27-42.
    13. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    14. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
    15. van de Leur, Michiel C.W. & Lucas, André & Seeger, Norman J., 2017. "Network, market, and book-based systemic risk rankings," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 84-90.
    16. Hong Fan & Allan Alvin Lee Lukaya Amalia & Qian Qian Gao, 2018. "The Assessment of Systemic Risk in the Kenyan Banking Sector," Complexity, Hindawi, vol. 2018, pages 1-15, January.
    17. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    18. Linh H. Nguyen & Linh X. D. Nguyen & Linzhi Tan, 2021. "Tail risk connectedness between US industries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3624-3650, July.
    19. Barigozzi, Matteo & Hallin, Marc, 2017. "A network analysis of the volatility of high-dimensionalfinancial series," LSE Research Online Documents on Economics 67456, London School of Economics and Political Science, LSE Library.
    20. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    21. Jacob Kleinow & Tobias Nell, 2015. "Determinants of systemically important banks: the case of Europe," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 7(4), pages 446-476, November.
    22. Lumsdaine, R.L. & Rockmore, D.N. & Foti, N.J. & Leibon, G. & Farmer, J.D., 2021. "The intrafirm complexity of systemically important financial institutions," Journal of Financial Stability, Elsevier, vol. 52(C).
    23. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2025. "Quantile graphical models: Prediction and conditional independence with applications to systemic risk," Journal of Econometrics, Elsevier, vol. 252(PA).
    24. Naeem, Muhammad Abubakr, 2024. "Navigating median and extreme volatility in stock markets: Implications for portfolio strategies," International Review of Economics & Finance, Elsevier, vol. 95(C).
    25. Petre Caraiani, 2020. "Forecasting Financial Networks," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 983-997, March.
    26. Chen, Jia & Li, Degui & Li, Yu-Ning & Linton, Oliver, 2025. "Estimating time-varying networks for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 249(PC).
    27. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2016. "Systemic risk spillovers in the European banking and sovereign network," Working Paper Series in Economics 79, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    28. Ms. Franziska L Ohnsorge & Marcin Wolski & Ms. Yuanyan S Zhang, 2014. "Safe Havens, Feedback Loops, and Shock Propagation in Global Asset Prices," IMF Working Papers 2014/081, International Monetary Fund.
    29. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    30. Wang, Gang-Jin & Jiang, Zhi-Qiang & Lin, Min & Xie, Chi & Stanley, H. Eugene, 2018. "Interconnectedness and systemic risk of China's financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 1-18.
    31. Constantin, Andreea & Peltonen, Tuomas A. & Sarlin, Peter, 2018. "Network linkages to predict bank distress," Journal of Financial Stability, Elsevier, vol. 35(C), pages 226-241.
    32. Pham, Thach N. & Powell, Robert & Bannigidadmath, Deepa, 2024. "Tail risk network analysis of Asian banks," Global Finance Journal, Elsevier, vol. 62(C).
    33. Matteo Foglia & Eliana Angelini, 2021. "The triple (T3) dimension of systemic risk: Identifying systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 7-26, January.
    34. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    35. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    36. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    37. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    38. Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    39. Arnold Polanski & Evarist Stoja, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England Staff Working Paper series 660, Bank of England.
    40. Bellavite Pellegrini, Carlo & Cincinelli, Peter & Meoli, Michele & Urga, Giovanni, 2022. "The contribution of (shadow) banks and real estate to systemic risk in China," Journal of Financial Stability, Elsevier, vol. 60(C).
    41. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    42. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Papers 1510.05118, arXiv.org, revised Jul 2016.
    43. Friedrich Poeschel, 2013. "Assortative matching through signals," 2013 Papers ppo178, Job Market Papers.
    44. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    45. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    46. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    47. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).

  21. Bodnar, Taras & Hautsch, Nikolaus, 2012. "Copula-based dynamic conditional correlation multiplicative error processes," SFB 649 Discussion Papers 2012-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," CFS Working Paper Series 450, Center for Financial Studies (CFS).

  22. Hautsch, Nikolaus & Huang, Ruihong, 2012. "On the dark side of the market: Identifying and analyzing hidden order placements," SFB 649 Discussion Papers 2012-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    2. Arzandeh, Mehdi & Frank, Julieta, "undated". "Price Discovery in Agricultural Futures Markets: Should We Look Beyond the Best Bid-Ask Spread?," Annual Meeting, 2017, June 18-21, Montreal, Canada 259344, Canadian Agricultural Economics Society.
    3. Quanbiao Shang & Teresa Serra & Philip Garcia & Mindy Mallory, 2021. "Looking under the surface: An analysis of iceberg orders in the U.S. agricultural futures markets," Agricultural Economics, International Association of Agricultural Economists, vol. 52(4), pages 679-699, July.
    4. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    5. Garvey, Ryan & Huang, Tao & Wu, Fei, 2016. "Why do traders choose dark markets?," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 12-28.
    6. Degryse, Hans & Karagiannis, Nikolaos & Tombeur, Geoffrey & Wuyts, Gunther, 2021. "Two shades of opacity: Hidden orders and dark trading," Journal of Financial Intermediation, Elsevier, vol. 47(C).
    7. Hagströmer, Björn & Nordén, Lars, 2013. "The diversity of high-frequency traders," Journal of Financial Markets, Elsevier, vol. 16(4), pages 741-770.
    8. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    9. Arzandeh, Mehdi & Frank, Julieta, 2017. "The Information Content of the Limit Order Book," 7th Annual Canadian Agri-Food Policy Conference, January 11-13, 2017, Ottawa, ON 253251, Canadian Agricultural Economics Society.
    10. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.

  23. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Local adaptive multiplicative error models for high-frequency forecasts," SFB 649 Discussion Papers 2012-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Klochkov, Yegor & Härdle, Wolfgang Karl & Xu, Xiu, 2019. "Localizing Multivariate CAViaR," IRTG 1792 Discussion Papers 2019-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Gillmann, Niels & Okhrin, Ostap, 2025. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Economic Modelling, Elsevier, vol. 148(C).
    3. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    4. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, vol. 5(4), pages 1-29, December.
    5. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    6. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    7. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    8. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the term structure of option implied volatility: The power of an adaptive method," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 157-177.
    9. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
    10. Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
    12. Shen, Zhiwei, "undated". "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
    13. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    14. Meihui Guo & Yi-Ting Guo & Chi-Jeng Wang & Liang-Ching Lin, 2015. "Assessing influential trade effects via high-frequency market reactions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1458-1471, July.
    15. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    16. Khowaja Kainat & Saef Danial & Sizov Sergej & Härdle Wolfgang Karl, 2024. "Scenario based merger & acquisition forecasting," Management & Marketing, Sciendo, vol. 19(4), pages 579-600.
    17. Chen, Ying & Chua, Wee Song & Härdle, Wolfgang Karl, 2016. "Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics," SFB 649 Discussion Papers 2016-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.

  24. Hautsch, Nikolaus & Huang, Ruihong, 2012. "On the dark side of the market: Identifying and analyzing hidden order placements," SFB 649 Discussion Papers 2012-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    2. Arzandeh, Mehdi & Frank, Julieta, "undated". "Price Discovery in Agricultural Futures Markets: Should We Look Beyond the Best Bid-Ask Spread?," Annual Meeting, 2017, June 18-21, Montreal, Canada 259344, Canadian Agricultural Economics Society.
    3. Quanbiao Shang & Teresa Serra & Philip Garcia & Mindy Mallory, 2021. "Looking under the surface: An analysis of iceberg orders in the U.S. agricultural futures markets," Agricultural Economics, International Association of Agricultural Economists, vol. 52(4), pages 679-699, July.
    4. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    5. Garvey, Ryan & Huang, Tao & Wu, Fei, 2016. "Why do traders choose dark markets?," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 12-28.
    6. Degryse, Hans & Karagiannis, Nikolaos & Tombeur, Geoffrey & Wuyts, Gunther, 2021. "Two shades of opacity: Hidden orders and dark trading," Journal of Financial Intermediation, Elsevier, vol. 47(C).
    7. Hagströmer, Björn & Nordén, Lars, 2013. "The diversity of high-frequency traders," Journal of Financial Markets, Elsevier, vol. 16(4), pages 741-770.
    8. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    9. Arzandeh, Mehdi & Frank, Julieta, 2017. "The Information Content of the Limit Order Book," 7th Annual Canadian Agri-Food Policy Conference, January 11-13, 2017, Ottawa, ON 253251, Canadian Agricultural Economics Society.
    10. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.

  25. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Local adaptive multiplicative error models for high-frequency forecasts," SFB 649 Discussion Papers 2012-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Klochkov, Yegor & Härdle, Wolfgang Karl & Xu, Xiu, 2019. "Localizing Multivariate CAViaR," IRTG 1792 Discussion Papers 2019-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Gillmann, Niels & Okhrin, Ostap, 2025. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Economic Modelling, Elsevier, vol. 148(C).
    3. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    4. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, vol. 5(4), pages 1-29, December.
    5. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    6. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    7. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    8. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the term structure of option implied volatility: The power of an adaptive method," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 157-177.
    9. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
    10. Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
    12. Shen, Zhiwei, "undated". "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
    13. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Chasing volatility - A persistent multiplicative error model with jumps," CREATES Research Papers 2014-29, Department of Economics and Business Economics, Aarhus University.
    14. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    15. Meihui Guo & Yi-Ting Guo & Chi-Jeng Wang & Liang-Ching Lin, 2015. "Assessing influential trade effects via high-frequency market reactions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1458-1471, July.
    16. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    17. Khowaja Kainat & Saef Danial & Sizov Sergej & Härdle Wolfgang Karl, 2024. "Scenario based merger & acquisition forecasting," Management & Marketing, Sciendo, vol. 19(4), pages 579-600.
    18. Chen, Ying & Chua, Wee Song & Härdle, Wolfgang Karl, 2016. "Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics," SFB 649 Discussion Papers 2016-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.

  26. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "Predicting bid-ask spreads using long memory autoregressive conditional poisson models," SFB 649 Discussion Papers 2011-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    2. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    3. Weiß, Gregor N.F. & Supper, Hendrik, 2013. "Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3334-3350.
    4. Anne Michaels & Michael Grüning, 2017. "Relationship of corporate social responsibility disclosure on information asymmetry and the cost of capital," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 28(3), pages 251-274, October.
    5. Bagnara, Matteo & Jappelli, Ruggero, 2022. "Liquidity derivatives," SAFE Working Paper Series 358, Leibniz Institute for Financial Research SAFE.
    6. Carl Chiarella & Xue-Zhong He & Lei Shi & Lijian Wei, 2014. "A Behavioural Model of Investor Sentiment in Limit Order Markets," Research Paper Series 342, Quantitative Finance Research Centre, University of Technology, Sydney.
    7. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    8. Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    9. Gianbiagio Curato & Fabrizio Lillo, 2013. "Modeling the coupled return-spread high frequency dynamics of large tick assets," Papers 1310.4539, arXiv.org.

  27. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2011. "Financial network systemic risk contributions," SFB 649 Discussion Papers 2011-072, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gu, Qinen & Li, Shaofang & Qin, Jiaying, 2025. "Enhanced volatility spillover network prediction of Chinese financial institutions using GCN-LSTM model," Finance Research Letters, Elsevier, vol. 85(PC).
    2. Naeem, Muhammad Abubakr & Yousaf, Imran & Karim, Sitara & Yarovaya, Larisa & Ali, Shoaib, 2023. "Tail-event driven NETwork dependence in emerging markets," Emerging Markets Review, Elsevier, vol. 55(C).
    3. Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2016. "The information in systemic risk rankings," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 461-475.
    4. Somnath Chatterjee & Marea Sing, 2021. "Measuring Systemic Risk in South African Banks," Working Papers 11004, South African Reserve Bank.
    5. Craig, Ben & Karamysheva, Madina & Salakhova, Dilyara, 2023. "Do market-based networks reflect true exposures between banks?," Working Paper Series 2867, European Central Bank.
    6. Yu, Jia-Wei & Xie, Wen-Jie & Jiang, Zhi-Qiang, 2018. "Early warning model based on correlated networks in global crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1335-1343.
    7. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    8. He, Zhipeng & Zhang, Shuguang, 2024. "Risk contagion and diversification among sovereign CDS, stock, foreign exchange and commodity markets: Fresh evidence from G7 and BRICS countries," Finance Research Letters, Elsevier, vol. 62(PB).
    9. Mengting Li & Qifa Xu & Cuixia Jiang & Yezheng Liu, 2024. "The role of long‐ and short‐run correlation networks in international portfolio selection," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3147-3176, July.
    10. Muzi Chen & Yuhang Wang & Boyao Wu & Difang Huang, 2024. "Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy," Papers 2403.19439, arXiv.org.
    11. Nguyen, Linh Hoang & Chevapatrakul, Thanaset & Yao, Kai, 2020. "Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 333-355.
    12. Chang, Carolyn W. & Li, Xiaodan & Lin, Edward M.H. & Yu, Min-Teh, 2018. "Systemic risk, interconnectedness, and non-core activities in Taiwan insurance industry," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 273-284.
    13. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Huang, Wei-Qiang & Wang, Dan, 2018. "A return spillover network perspective analysis of Chinese financial institutions’ systemic importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 405-421.
    15. Robert Engle & Eric Jondeau & Michael Rockinger, 2015. "Systemic Risk in Europe," Review of Finance, European Finance Association, vol. 19(1), pages 145-190.
    16. Li, Kelong & Xie, Chi & Ouyang, Yingbo & Mo, Tingcheng & Feng, Yusen, 2024. "Tail risk spillovers in the stock and forex markets at the major emergencies: Evidence from the G20 countries," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    17. Daning Hu & Gerhard Schwabe & Xiao Li, 2015. "Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-9, December.
    18. Ahelegbey, Daniel Felix & Giudici, Paolo & Hashem, Shatha Qamhieh, 2021. "Network VAR models to measure financial contagion," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    19. Bluhm, Marcel & Krahnen, Jan Pieter, 2014. "Systemic risk in an interconnected banking system with endogenous asset markets," Journal of Financial Stability, Elsevier, vol. 13(C), pages 75-94.
    20. Dinger, Valeriya & Grundke, Peter & Rohde, Kai, 2025. "On the risk commonality of US tech firms: Relevance and determinants," Technological Forecasting and Social Change, Elsevier, vol. 217(C).
    21. Tatsuru Kikuchi, 2025. "Dynamic Spatial Treatment Effects and Network Fragility: Theory and Evidence from the 2008 Financial Crisis," Papers 2511.08602, arXiv.org.
    22. Zhang, Xingmin & Zhang, Shuai & Lu, Liping, 2022. "The banking instability and climate change: Evidence from China," Energy Economics, Elsevier, vol. 106(C).
    23. Guo, Peng & Jiang, Fuwei & Li, Mengru & Liu, Yumin, 2024. "Managerial macroeconomic perception and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    24. Maghyereh, Aktham & Abdoh, Hussein, 2024. "Tail risk connectedness among GCC banks episodes from the Global Financial Crisis to COVID-19 pandemic," The Quarterly Review of Economics and Finance, Elsevier, vol. 96(C).
    25. Samuel Ronnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org, revised Jul 2015.
    26. Tatsuru Kikuchi, 2025. "General Equilibrium Amplification and Crisis Vulnerability: Cross-Crisis Evidence from Global Banks," Papers 2510.24775, arXiv.org, revised Jan 2026.
    27. Paolo Giudici & Laura Parisi, 2016. "Bail in or Bail out? The Atlante example from a systemic risk perspective," DEM Working Papers Series 124, University of Pavia, Department of Economics and Management.
    28. Matteo Barigozzi & Christian T. Brownlees, 2013. "Nets: Network estimation for time series," Economics Working Papers 1391, Department of Economics and Business, Universitat Pompeu Fabra.
    29. Daniele Bianchi & Monica Billio & Roberto Casarin & Massimo Guidolin, 2018. "Modeling Systemic Risk with Markov Switching Graphical SUR Models," Working Papers 626, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    30. Peltonen, Tuomas A. & Sarlin, Peter & Piloiu, Andreea, 2015. "Network linkages to predict bank distress," Working Paper Series 1828, European Central Bank.
    31. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    32. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    33. Zhou, Xuewei & Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe, 2024. "Multilayer network analysis of idiosyncratic volatility connectedness: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    34. Daniel Felix Ahelegbey, 2022. "Statistical Modelling of Downside Risk Spillovers," FinTech, MDPI, vol. 1(2), pages 1-10, April.
    35. Liu, Zixin & Hu, Jun & Zhang, Shuguang & He, Zhipeng, 2024. "Risk spillovers among oil, gold, stock, and foreign exchange markets: Evidence from G20 economies," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    36. Xin Qi & Tianyu Zhao, 2025. "Risk formulation mechanism among top global energy companies under large shocks," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-40, May.
    37. Hai-Chuan Xu & Fredj Jawadi & Jie Zhou & Wei-Xing Zhou, 2023. "Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework," Empirical Economics, Springer, vol. 65(1), pages 93-110, July.
    38. Shouwei Li, 2016. "Does Diversification Affect Banking Systemic Risk?," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    39. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    40. Borghesi, Simone & Flori, Andrea, 2018. "EU ETS facets in the net: Structure and evolution of the EU ETS network," Energy Economics, Elsevier, vol. 75(C), pages 602-635.
    41. Castro, Carlos & Ferrari, Stijn, 2014. "Measuring and testing for the systemically important financial institutions," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 1-14.
    42. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    43. Deng, Yang & Zhang, Ziqing & Zhu, Li, 2021. "A model-based index for systemic risk contribution measurement in financial networks," Economic Modelling, Elsevier, vol. 95(C), pages 35-48.
    44. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
    45. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
    46. Ouyang, Zisheng & Zhou, Xuewei & Lu, Min & Liu, Ke, 2024. "Imported financial risk in global stock markets: Evidence from the interconnected network," Research in International Business and Finance, Elsevier, vol. 69(C).
    47. Zhou, Dong-hai & Liu, Xiao-xing, 2024. "Does systemic risk in the fund markets predict future economic downturns?," International Review of Financial Analysis, Elsevier, vol. 92(C).
    48. Ahelegbey, Daniel Felix & Billio, Monica & Casarin, Roberto, 2024. "Modeling Turning Points in the Global Equity Market," Econometrics and Statistics, Elsevier, vol. 30(C), pages 60-75.
    49. Kraft, Holger & Schmidt, Alexander, 2013. "Systemic risk in the financial sector: What can se learn from option markets?," SAFE Working Paper Series 25, Leibniz Institute for Financial Research SAFE.
    50. Joerg Osterrieder & Stephen Chan & Jeffrey Chu & Yuanyuan Zhang & Branka Hadji Misheva & Codruta Mare, 2024. "Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques," Papers 2402.11231, arXiv.org.
    51. Nistor Simona, 2017. "Banks’ Vulnerability and Financial Openness across Central and Eastern Europe," Studia Universitatis Babeș-Bolyai Oeconomica, Paradigm, vol. 62(3), pages 47-66, December.
    52. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    53. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    54. Wang, Wei & Xu, Huifu & Ma, Tiejun, 2023. "Optimal scenario-dependent multivariate shortfall risk measure and its application in risk capital allocation," European Journal of Operational Research, Elsevier, vol. 306(1), pages 322-347.
    55. Geert Mesters & Christian Brownlees, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona School of Economics.
    56. Yang Deng & Chenyin Gao, 2023. "Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market," Pacific Economic Review, Wiley Blackwell, vol. 28(2), pages 167-190, May.
    57. Xin Yang & Shan Chen & Hong Liu & Xiaoguang Yang & Chuangxia Huang, 2023. "Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1201-1213, April.
    58. Brownlees, Christian & Engle, Robert F., 2017. "SRISK: a conditional capital shortfall measure of systemic risk," ESRB Working Paper Series 37, European Systemic Risk Board.
    59. Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023. "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, vol. 157(C).
    60. Wang, Ze & Gao, Xiangyun & Huang, Shupei & Sun, Qingru & Chen, Zhihua & Tang, Renwu & Di, Zengru, 2022. "Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    61. Bierth, Christopher & Irresberger, Felix & Weiß, Gregor N.F., 2015. "Systemic risk of insurers around the globe," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 232-245.
    62. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    63. Arnold Polanski & Evarist Stoja, 2015. "Extreme risk interdependence," Bank of England Staff Working Paper series 563, Bank of England.
    64. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
    65. Chen, Ning & Li, Shaofang & Lu, Shuai, 2023. "The extreme risk connectedness of the global financial system: G7 and BRICS evidence," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    66. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    67. Yan, Guan & Liu, Zhidong, 2023. "Interconnectedness of financial institutions based on pledged shares in China," Finance Research Letters, Elsevier, vol. 57(C).
    68. Hong Fan & Allan Alvin Lee Lukaya Amalia & Qian Qian Gao, 2018. "The Assessment of Systemic Risk in the Kenyan Banking Sector," Complexity, Hindawi, vol. 2018, pages 1-15, January.
    69. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    70. Grzegorz Halaj & Ruben Hipp, 2024. "Decomposing Systemic Risk: The Roles of Contagion and Common Exposures," Staff Working Papers 24-19, Bank of Canada.
    71. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    72. Paolo Giudici & Laura Parisi, 2016. "CoRisk: measuring systemic risk through default probability contagion," DEM Working Papers Series 116, University of Pavia, Department of Economics and Management.
    73. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    74. Wu, JunFeng & Zhang, Chao & Chen, Yun, 2022. "Analysis of risk correlations among stock markets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    75. Hualu Shao & Baicheng Zhou & Di Wang & Zirong An, 2025. "RETRACTED ARTICLE: Navigating Uncertainty: The Micro-Level Dynamics of Economic Policy Uncertainty and Systemic Financial Risk in China’s Financial Institutions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 5831-5861, June.
    76. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    77. Yaya Su & Zhehao Huang & Benjamin M. Drakeford, 2019. "Monetary Policy, Industry Heterogeneity and Systemic Risk—Based on a High Dimensional Network Analysis," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    78. Rui Ding & Siwei Shen & Yuqi Zhu & Linyu Du & Shihui Chen & Juan Liang & Kexing Wang & Wenqian Xiao & Yuxuan Hong, 2023. "Evolution, Forecasting, and Driving Mechanisms of the Digital Financial Network: Evidence from China," Sustainability, MDPI, vol. 15(22), pages 1-18, November.
    79. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2019. "Effectiveness of policy and regulation in European sovereign credit risk markets: a network analysis," ESRB Working Paper Series 90, European Systemic Risk Board.
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  30. Hautsch, Nikolaus & Huang, Ruihong, 2011. "Limit order flow, market impact and optimal order sizes: Evidence from NASDAQ TotalView-ITCH data," SFB 649 Discussion Papers 2011-056, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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    1. Anastasia Bugaenko, 2020. "Empirical Study of Market Impact Conditional on Order-Flow Imbalance," Papers 2004.08290, arXiv.org, revised Apr 2020.
    2. Xuefeng Gao & S. J. Deng, 2014. "Hydrodynamic limit of order book dynamics," Papers 1411.7502, arXiv.org, revised Feb 2016.
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  31. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2011. "Financial network systemic risk contributions," SFB 649 Discussion Papers 2011-072, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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    1. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    2. Anne Michaels & Michael Grüning, 2017. "Relationship of corporate social responsibility disclosure on information asymmetry and the cost of capital," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 28(3), pages 251-274, October.
    3. Bagnara, Matteo & Jappelli, Ruggero, 2022. "Liquidity derivatives," SAFE Working Paper Series 358, Leibniz Institute for Financial Research SAFE.
    4. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    5. Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

  33. Hautsch, Nikolaus & Malec, Peter & Schienle, Melanie, 2010. "Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes," SFB 649 Discussion Papers 2010-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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    1. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    2. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    3. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    4. Sucarrat, Genaro, 2020. "Identification of Volatility Proxies as Expectations of Squared Financial Return," MPRA Paper 101953, University Library of Munich, Germany.
    5. Hiroyuki Kawakatsu, 2019. "Jointly Modeling Autoregressive Conditional Mean and Variance of Non-Negative Valued Time Series," Econometrics, MDPI, vol. 7(4), pages 1-19, December.
    6. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
    7. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    8. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
    9. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    10. Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
    11. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    12. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    13. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    14. Mariana Rodrigues-Motta & Johannes Forkman, 2022. "Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 201-221, June.
    15. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    16. Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.
    17. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    18. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    19. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
    20. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    21. Carol Alexander & Daniel Heck & Andreas Kaeck, 2021. "The Role of Binance in Bitcoin Volatility Transmission," Papers 2107.00298, arXiv.org, revised Aug 2021.
    22. Sucarrat, Genaro & Grønneberg, Steffen, 2016. "Models of Financial Return With Time-Varying Zero Probability," MPRA Paper 68931, University Library of Munich, Germany.
    23. Andreea Röthig & Andreas Röthig & Carl Chiarella, 2015. "On Candlestick-based Trading Rules Profitability Analysis via Parametric Bootstraps and Multivariate Pair-Copula based Models," Research Paper Series 362, Quantitative Finance Research Centre, University of Technology, Sydney.
    24. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
    25. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    26. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Local adaptive multiplicative error models for high-frequency forecasts," SFB 649 Discussion Papers 2012-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    27. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.
    28. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    29. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.

  34. Hautsch, Nikolaus & Podolskij, Mark, 2010. "Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence," SFB 649 Discussion Papers 2010-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    2. Oh, Minseog & Kim, Donggyu & Wang, Yazhen, 2026. "Robust realized integrated beta estimator with application to dynamic analysis of integrated beta," Journal of Econometrics, Elsevier, vol. 254(PA).
    3. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    4. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Leibniz Institute for Financial Research SAFE.
    5. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    6. Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai, 2020. "A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1025-1034, November.
    7. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    8. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    9. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    10. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," SFB 649 Discussion Papers 2014-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    12. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    13. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    14. Li, Z. Merrick & Laeven, Roger J.A. & Vellekoop, Michel H., 2020. "Dependent microstructure noise and integrated volatility estimation from high-frequency data," Journal of Econometrics, Elsevier, vol. 215(2), pages 536-558.
    15. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    16. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    17. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    18. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    19. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    20. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    21. Danial Saef & Odett Nagy & Sergej Sizov & Wolfgang Karl Härdle, 2024. "Understanding temporal dynamics of jumps in cryptocurrency markets: evidence from tick-by-tick data," Digital Finance, Springer, vol. 6(4), pages 605-638, December.
    22. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    23. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, Department of Economics and Business Economics, Aarhus University.
    24. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    25. Li, M. Z. & Linton, O., 2021. "Robust Estimation of Integrated and Spot Volatility," Cambridge Working Papers in Economics 2115, Faculty of Economics, University of Cambridge.
    26. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Oct 2025.
    27. Saef, Danial & Nagy, Odett & Sizov, Sergej & Härdle, Wolfgang, 2021. "Understanding jumps in high frequency digital asset markets," IRTG 1792 Discussion Papers 2021-019, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    28. Mathias Pohl & Alexander Ristig & Walter Schachermayer & Ludovic Tangpi, 2018. "Theoretical and empirical analysis of trading activity," Papers 1803.04892, arXiv.org, revised Oct 2018.
    29. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    30. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    31. Li, Qiyuan & Li, Yifan & Nolte, Ingmar & Nolte, Sandra & Yu, Shifan, 2026. "Testing for jumps in a discretely observed price process with endogenous sampling times," Journal of Econometrics, Elsevier, vol. 254(PA).
    32. Massimiliano Caporin & Aleksey Kolokolov & Roberto RenoÕ, 2014. "Multi-jumps," "Marco Fanno" Working Papers 0185, Dipartimento di Scienze Economiche "Marco Fanno".
      • Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.
    33. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    34. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    35. Carol Alexander & Daniel Heck & Andreas Kaeck, 2021. "The Role of Binance in Bitcoin Volatility Transmission," Papers 2107.00298, arXiv.org, revised Aug 2021.
    36. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    37. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    38. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    39. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    40. Fernandes, Marcelo & Pereira, Murilo A.P., 2025. "Forecasting realized volatility using news flow," The Quarterly Review of Economics and Finance, Elsevier, vol. 104(C).
    41. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    42. Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024. "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
    43. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    44. Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
    45. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    46. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  35. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    2. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    3. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    4. Márcio Poletti Laurini & Armênio Dias Westin Neto, 2014. "Arbitrage In The Term Structure Of Interest Rates: A Bayesian Approach," International Econometric Review (IER), Economic Research Association, vol. 6(2), pages 77-99, September.
    5. Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    6. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).
    7. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    8. Sourish Das, 2018. "Modeling Nelson-Siegel Yield Curve using Bayesian Approach," Papers 1809.06077, arXiv.org, revised Oct 2018.
    9. Hong, Zhiwu & Niu, Linlin & Zhang, Chen, 2022. "Affine arbitrage-free yield net models with application to the euro debt crisis," Journal of Econometrics, Elsevier, vol. 230(1), pages 201-220.

  36. Nikolaus Hautsch & Dieter Hess & David Veredas, 2010. "The impact of macroeconomic news on quote adjustments, noise and informational volatility," Working Papers ECARES 2010-004, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Adam Clements & Neda Todorova, 2014. "The impact of information flow and trading activity on gold and oil futures volatility," NCER Working Paper Series 102, National Centre for Econometric Research.
    2. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    3. Makoto Takahashi, 2025. "Returns and Order Flow Imbalances: Intraday Dynamics and Macroeconomic News Effects," Papers 2508.06788, arXiv.org, revised Oct 2025.
    4. Martin Hauptfleisch, 2019. "Financial Decision-Making Using Data," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 6-2019, January-A.
    5. Han, Liyan & Xu, Yang & Yin, Libo, 2017. "Does investor attention matter? The attention-return relation in gold futures market," Economics Discussion Papers 2017-37, Kiel Institute for the World Economy.
    6. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
    7. Christoph Schmidhammer & Sebastian Lobe & Klaus Röder, 2014. "The real benchmark of DAX index products and the influence of information dissemination: A natural experiment," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 129-149, April.
    8. Alexander Kurov & Alessio Sancetta & Georg H. Strasser & Marketa Halova Wolfe, 2015. "Price Drift before U.S. Macroeconomic News: Private Information about Public Announcements?," Boston College Working Papers in Economics 881, Boston College Department of Economics, revised 29 Jul 2015.
    9. Banerjee, Ameet Kumar & Pradhan, H.K. & Akhtaruzzaman, Md & Sensoy, Ahmet & Dann, Susan, 2024. "Anatomy of sovereign yield behaviour using textual news," Research in International Business and Finance, Elsevier, vol. 71(C).
    10. Adam E. Clements & Neda Todorova, 2016. "Information Flow, Trading Activity and Commodity Futures Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(1), pages 88-104, January.
    11. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.
    12. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    13. Füss, Roland & Grabellus, Markus & Mager, Ferdinand & Stein, Michael, 2018. "Something in the air: Information density, news surprises, and price jumps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 50-75.
    14. Barbopoulos, Leonidas G. & Adra, Samer & Saunders, Anthony, 2020. "Macroeconomic news and acquirer returns in M&As: The impact of investor alertness," Journal of Corporate Finance, Elsevier, vol. 64(C).
    15. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    16. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    17. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    18. Lei Wu & Kuan Xu & Qingbin Meng, 2021. "Information flow and price discovery dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 329-367, January.
    19. Erdemlioglu, Deniz & Laurent, Sébastien & Neely, Christopher J., 2015. "Which continuous-time model is most appropriate for exchange rates?," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 256-268.
    20. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.
    21. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Forecasting the CNY-CNH pricing differential: The role of investor attention," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 232-247.
    22. Zhang, Junru & Zhang, Zhaoyong, 2021. "CSR, Media and Stock Illiquidity: Evidence from Chinese Listed Financial Firms," Finance Research Letters, Elsevier, vol. 41(C).
    23. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    24. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    25. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
    26. Walid Ben Omrane & Khaled Guesmi & Qi Qianru & Samir Saadi, 2023. "The high-frequency impact of macroeconomic news on jumps and co-jumps in the cryptocurrency markets," Annals of Operations Research, Springer, vol. 330(1), pages 177-209, November.

  37. Nikolaus Hautsch & Mark Podolskij, 2010. "Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," CREATES Research Papers 2010-29, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    2. Oh, Minseog & Kim, Donggyu & Wang, Yazhen, 2026. "Robust realized integrated beta estimator with application to dynamic analysis of integrated beta," Journal of Econometrics, Elsevier, vol. 254(PA).
    3. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    4. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Leibniz Institute for Financial Research SAFE.
    5. Jacod, Jean & Li, Yingying & Zheng, Xinghua, 2019. "Estimating the integrated volatility with tick observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 80-100.
    6. Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai, 2020. "A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1025-1034, November.
    7. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    8. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    9. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    10. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," SFB 649 Discussion Papers 2014-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    12. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    13. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    14. Li, Z. Merrick & Laeven, Roger J.A. & Vellekoop, Michel H., 2020. "Dependent microstructure noise and integrated volatility estimation from high-frequency data," Journal of Econometrics, Elsevier, vol. 215(2), pages 536-558.
    15. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    16. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    17. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    18. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    19. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    20. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    21. Danial Saef & Odett Nagy & Sergej Sizov & Wolfgang Karl Härdle, 2024. "Understanding temporal dynamics of jumps in cryptocurrency markets: evidence from tick-by-tick data," Digital Finance, Springer, vol. 6(4), pages 605-638, December.
    22. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    23. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, Department of Economics and Business Economics, Aarhus University.
    24. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    25. Li, M. Z. & Linton, O., 2021. "Robust Estimation of Integrated and Spot Volatility," Cambridge Working Papers in Economics 2115, Faculty of Economics, University of Cambridge.
    26. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Oct 2025.
    27. Saef, Danial & Nagy, Odett & Sizov, Sergej & Härdle, Wolfgang, 2021. "Understanding jumps in high frequency digital asset markets," IRTG 1792 Discussion Papers 2021-019, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    28. Mathias Pohl & Alexander Ristig & Walter Schachermayer & Ludovic Tangpi, 2018. "Theoretical and empirical analysis of trading activity," Papers 1803.04892, arXiv.org, revised Oct 2018.
    29. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    30. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    31. Li, Qiyuan & Li, Yifan & Nolte, Ingmar & Nolte, Sandra & Yu, Shifan, 2026. "Testing for jumps in a discretely observed price process with endogenous sampling times," Journal of Econometrics, Elsevier, vol. 254(PA).
    32. Massimiliano Caporin & Aleksey Kolokolov & Roberto RenoÕ, 2014. "Multi-jumps," "Marco Fanno" Working Papers 0185, Dipartimento di Scienze Economiche "Marco Fanno".
      • Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.
    33. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    34. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    35. Carol Alexander & Daniel Heck & Andreas Kaeck, 2021. "The Role of Binance in Bitcoin Volatility Transmission," Papers 2107.00298, arXiv.org, revised Aug 2021.
    36. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    37. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    38. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    39. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    40. Fernandes, Marcelo & Pereira, Murilo A.P., 2025. "Forecasting realized volatility using news flow," The Quarterly Review of Economics and Finance, Elsevier, vol. 104(C).
    41. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    42. Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024. "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
    43. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    44. Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
    45. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    46. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  38. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    2. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    3. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    4. Márcio Laurini, 2012. "Dynamic Functional Data Analysis with Nonparametric State Space Models," IBMEC RJ Economics Discussion Papers 2012-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    5. Márcio Poletti Laurini & Armênio Dias Westin Neto, 2014. "Arbitrage In The Term Structure Of Interest Rates: A Bayesian Approach," International Econometric Review (IER), Economic Research Association, vol. 6(2), pages 77-99, September.
    6. Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    7. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).
    8. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    9. Sourish Das, 2018. "Modeling Nelson-Siegel Yield Curve using Bayesian Approach," Papers 1809.06077, arXiv.org, revised Oct 2018.
    10. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.
    11. Hong, Zhiwu & Niu, Linlin & Zhang, Chen, 2022. "Affine arbitrage-free yield net models with application to the euro debt crisis," Journal of Econometrics, Elsevier, vol. 230(1), pages 201-220.

  39. Hautsch, Nikolaus & Malec, Peter & Schienle, Melanie, 2010. "Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes," SFB 649 Discussion Papers 2010-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    2. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    3. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    4. Sucarrat, Genaro, 2020. "Identification of Volatility Proxies as Expectations of Squared Financial Return," MPRA Paper 101953, University Library of Munich, Germany.
    5. Hiroyuki Kawakatsu, 2019. "Jointly Modeling Autoregressive Conditional Mean and Variance of Non-Negative Valued Time Series," Econometrics, MDPI, vol. 7(4), pages 1-19, December.
    6. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
    7. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    8. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
    9. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    10. Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
    11. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
    12. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    13. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    14. Mariana Rodrigues-Motta & Johannes Forkman, 2022. "Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 201-221, June.
    15. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    16. Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.
    17. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    18. Nguyen, Giang & Engle, Robert & Fleming, Michael & Ghysels, Eric, 2020. "Liquidity and volatility in the U.S. Treasury market," Journal of Econometrics, Elsevier, vol. 217(2), pages 207-229.
    19. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
    20. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    21. Carol Alexander & Daniel Heck & Andreas Kaeck, 2021. "The Role of Binance in Bitcoin Volatility Transmission," Papers 2107.00298, arXiv.org, revised Aug 2021.
    22. Sucarrat, Genaro & Grønneberg, Steffen, 2016. "Models of Financial Return With Time-Varying Zero Probability," MPRA Paper 68931, University Library of Munich, Germany.
    23. Andreea Röthig & Andreas Röthig & Carl Chiarella, 2015. "On Candlestick-based Trading Rules Profitability Analysis via Parametric Bootstraps and Multivariate Pair-Copula based Models," Research Paper Series 362, Quantitative Finance Research Centre, University of Technology, Sydney.
    24. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
    25. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    26. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Local adaptive multiplicative error models for high-frequency forecasts," SFB 649 Discussion Papers 2012-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    27. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.
    28. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    29. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.

  40. Hautsch, Nikolaus & Huang, Ruihong, 2009. "The market impact of a limit order," SFB 649 Discussion Papers 2009-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gabriel Yergeau, 2016. "Profitability and Market Quality of High Frequency Market-makers: An Empirical Investigation," Working Papers 16-3, HEC Montreal, Canada Research Chair in Risk Management.
    2. Fabrizio Pomponio & Frédéric Abergel, 2013. "Multiple-limit trades : empirical facts and application to lead-lag measures," Post-Print hal-00745317, HAL.
    3. Mircea BAHNA & Cosmin-Octavian CEPOI & Bogdan Andrei DUMITRESCU & Virgil DAMIAN, 2018. "Estimating the Price Impact of Market Orders on the Bucharest Stock Exchange," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 120-133, December.
    4. Jianchang Zhu & Leilei Zhang & Xuchu Sun, 2024. "Optimal liquidation using extended trading close for multiple trading days," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-33, December.
    5. M. Fr Mmel & X. Han & F. Van Gysegem, 2013. "News, Liquidity Dynamics and Intraday Jumps: Evidence from the HUF/EUR market," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/848, Ghent University, Faculty of Economics and Business Administration.
    6. Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
    7. Fleming, Michael J. & Mizrach, Bruce & Nguyen, Giang, 2018. "The microstructure of a U.S. Treasury ECN: The BrokerTec platform," Journal of Financial Markets, Elsevier, vol. 40(C), pages 2-22.
    8. Hautsch, Nikolaus & Podolskij, Mark, 2010. "Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence," CFS Working Paper Series 2010/17, Center for Financial Studies (CFS).
    9. Makoto Takahashi, 2025. "Returns and Order Flow Imbalances: Intraday Dynamics and Macroeconomic News Effects," Papers 2508.06788, arXiv.org, revised Oct 2025.
    10. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    11. Alain P. Chaboud & Erik Hjalmarsson & Filip Zikes, 2020. "The Evolution of Price Discovery in an Electronic Market," Finance and Economics Discussion Series 2020-051, Board of Governors of the Federal Reserve System (U.S.).
    12. Johannes Bleher & Michael Bleher & Thomas Dimpfl, 2020. "From orders to prices: A stochastic description of the limit order book to forecast intraday returns," Papers 2004.11953, arXiv.org, revised May 2021.
    13. Alex Frino & Ognjen Kovačević & Vito Mollica, 2019. "Depths and spreads in futures markets: Relationship with order execution, submission, and cancellation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(5), pages 590-599, May.
    14. Erdinc Akyildirim & Shaen Corbet & Guzhan Gulay & Duc Khuong Nguyen & Ahmet Sensoy, 2019. "Order Flow Persistence in Equity Spot and Futures Markets: Evidence from a Dynamic Emerging Market," Working Papers 2019-011, Department of Research, Ipag Business School.
    15. Przemys{l}aw Rola, 2025. "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets," Papers 2507.09734, arXiv.org.
    16. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    17. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid-Ask Spread?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1482-1498.
    18. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Frédéric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Post-Print hal-01561128, HAL.
    19. Francesco Calvori & Fabrizio Cipollini & Giampiero M. Gallo, 2014. "Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares," Econometrics Working Papers Archive 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    20. Fabrizio Pomponio & Frederic Abergel, 2012. "Multiple-limit trades: empirical facts and application to lead--lag measures," Quantitative Finance, Taylor & Francis Journals, vol. 13(5), pages 783-793, September.
    21. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    22. Lo, Danny K. & Hall, Anthony D., 2015. "Resiliency of the limit order book," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 222-244.
    23. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    24. Ying Chen & Ulrich Horst & Hoang Hai Tran, 2026. "Optimal Trade Execution Under Endogenous Order Flow," Operations Research, INFORMS, vol. 74(1), pages 72-92, January.
    25. Cebiroğlu, Gökhan & Horst, Ulrich, 2015. "Optimal order display in limit order markets with liquidity competition," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 81-100.
    26. Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    27. Będowska-Sójka, Barbara, 2020. "Do aggressive orders affect liquidity? An evidence from an emerging market," Research in International Business and Finance, Elsevier, vol. 54(C).
    28. Zoltán Eisler & Jean-Philippe Bouchaud & Julien Kockelkoren, 2012. "The price impact of order book events: market orders, limit orders and cancellations," Quantitative Finance, Taylor & Francis Journals, vol. 12(9), pages 1395-1419, September.
    29. Robert Engle & Michael J. Fleming & Eric Ghysels & Giang Nguyen, 2012. "Liquidity and volatility in the U.S. treasury market," Staff Reports 590, Federal Reserve Bank of New York.
    30. Zhao, Chaoyi & Chen, Yufan & Wu, Lintong & Dai, Yuehao & Chen, Ermo & Wu, Lan & Zhang, Ruixun, 2025. "High-frequency liquidity in the Chinese stock market: Measurements, patterns, and determinants," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    31. Timoth'ee Fabre & Vincent Ragel, 2023. "Interpretable ML for High-Frequency Execution," Papers 2307.04863, arXiv.org, revised Sep 2024.
    32. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2010. "The Price Impact of Order Book Events," Papers 1011.6402, arXiv.org, revised Apr 2011.
    33. Cebiroglu, Gökhan & Hautsch, Nikolaus & Horst, Ulrich, 2014. "Order exposure and liquidity coordination: Does hidden liquidity harm price efficiency?," CFS Working Paper Series 468, Center for Financial Studies (CFS).
    34. Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    35. Zhicheng Li & Haipeng Xing & Xinyun Chen, 2019. "A multifactor regime-switching model for inter-trade durations in the limit order market," Papers 1912.00764, arXiv.org.
    36. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    37. Antonio Figueiredo & Pankaj Jain & Suchismita Mishra, 2023. "The role of fleeting orders on option expiration days," Quantitative Finance, Taylor & Francis Journals, vol. 23(10), pages 1511-1529, October.
    38. Chanaka Edirisinghe & Jingnan Chen & Jaehwan Jeong, 2023. "Optimal Leveraged Portfolio Selection Under Quasi-Elastic Market Impact," Operations Research, INFORMS, vol. 71(5), pages 1558-1576, September.
    39. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    40. Xintong Wang & Christopher Hoang & Yevgeniy Vorobeychik & Michael P. Wellman, 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis," Games, MDPI, vol. 12(2), pages 1-43, May.
    41. Han, SeungOh, 2024. "Price clustering on cryptocurrency order books at a US-based exchange," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    42. Forsyth, P.A. & Kennedy, J.S. & Tse, S.T. & Windcliff, H., 2012. "Optimal trade execution: A mean quadratic variation approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1971-1991.
    43. Xinyue He & Teresa Serra & Philip Garcia, 2021. "Resilience in “Flash Events” in the Corn and Lean Hog Futures Markets," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 743-764, March.
    44. Jose Blanchet & Xinyun Chen, 2013. "Continuous-time Modeling of Bid-Ask Spread and Price Dynamics in Limit Order Books," Papers 1310.1103, arXiv.org.
    45. Chen, Yuanyuan & Gao, Xuefeng & Li, Duan, 2018. "Optimal order execution using hidden orders," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 89-116.
    46. Neil A. Chriss, 2024. "Optimal position-building strategies in competition," Papers 2409.03586, arXiv.org, revised Nov 2024.
    47. Ulrich Horst & Dorte Kreher, 2015. "A weak law of large numbers for a limit order book model with fully state dependent order dynamics," Papers 1502.04359, arXiv.org, revised May 2016.
    48. Zhu, Hongyu & Yamamoto, Ryuichi, 2022. "Order submission, information asymmetry, and tick size," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    49. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Fr'ed'eric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Papers 1802.08502, arXiv.org, revised May 2022.
    50. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015, January-A.
    51. Thomas A. P. de Boer & Cornelis Gardebroek & Joost M. E. Pennings & Andres Trujillo‐Barrera, 2022. "Intraday liquidity in soybean complex futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1189-1211, July.
    52. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.
    53. Alexandre Aidov & Olesya Lobanova, 2021. "The Relation between Intraday Limit Order Book Depth and Spread," IJFS, MDPI, vol. 9(4), pages 1-13, November.
    54. Xue, Yi & Gençay, Ramazan, 2012. "Hierarchical information and the rate of information diffusion," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1372-1401.
    55. Yi Cao & Jia Zhai, 2022. "Estimating price impact via deep reinforcement learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3954-3970, October.
    56. Jia Zhai & Yi Cao & Xuemei Ding, 2018. "Data analytic approach for manipulation detection in stock market," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 897-932, April.
    57. Amaya, Diego & Filbien, Jean-Yves & Okou, Cédric & Roch, Alexandre F., 2018. "Distilling liquidity costs from limit order books," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 16-34.
    58. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    59. Carlos Jorge Lenczewski Martins, 2019. "Market and limit orders and their role in the price discovery process," Bank i Kredyt, Narodowy Bank Polski, vol. 50(6), pages 551-570.
    60. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    61. Mehdi Arzandeh & Julieta Frank & Justin Daniels, 2025. "Real‐Time Tracking of Public Announcements in the Limit Order Book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(6), pages 569-599, June.
    62. Çağlayan-Gümüş, Ayşe & Karahan, Cenk C., 2024. "Information content of the limit order book: A cross-sectional analysis in Borsa Istanbul," Global Finance Journal, Elsevier, vol. 62(C).

  41. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," SFB 649 Discussion Papers 2009-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Zhi-Qiang Jiang & Wen-Jie Xie & Xiong Xiong & Wei Zhang & Yong-Jie Zhang & W. -X. Zhou, 2012. "Trading networks, abnormal motifs and stock manipulation," Papers 1301.0007, arXiv.org.

  42. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," SFB 649 Discussion Papers 2009-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Zhi-Qiang Jiang & Wen-Jie Xie & Xiong Xiong & Wei Zhang & Yong-Jie Zhang & W. -X. Zhou, 2012. "Trading networks, abnormal motifs and stock manipulation," Papers 1301.0007, arXiv.org.

  43. Hautsch, Nikolaus & Kyj, Lada M. & Oomen, Roel C.A., 2009. "A blocking and regularization approach to high dimensional realized covariance estimation," SFB 649 Discussion Papers 2009-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    3. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    5. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    6. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    7. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    9. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    10. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    11. Taras Bodnar & Nikolaus Hautsch & Yarema Okhrin & Nestor Parolya, 2024. "Consistent Estimation of the High-Dimensional Efficient Frontier," Papers 2409.15103, arXiv.org.
    12. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    13. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    14. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    15. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    16. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    17. Jin, Xin & Maheu, John M, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," MPRA Paper 60102, University Library of Munich, Germany.
    18. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
    19. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    20. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    21. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    22. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    23. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
    24. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, Department of Economics and Business Economics, Aarhus University.
    25. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    26. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    27. Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
    28. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    29. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
    30. Pirino, Davide & Pollastri, Alessandro & Trapin, Luca, 2025. "Testing liquidity: A statistical theory based on asset staleness," Econometrics and Statistics, Elsevier, vol. 35(C), pages 23-40.
    31. Manabu Asai & Michael McAleer, 2015. "The Impact of Jumps and Leverage in Forecasting Co-Volatility," Documentos de Trabajo del ICAE 2015-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    32. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023. "The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
    33. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    34. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    35. Barnes, George & Ramgoolam, Sanjaye & Stephanou, Michael, 2024. "Permutation invariant Gaussian matrix models for financial correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).
    36. Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.
    37. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    38. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    39. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    40. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    41. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2021. "High-dimensional estimation of quadratic variation based on penalized realized variance," Papers 2103.03237, arXiv.org, revised Jan 2026.
    42. Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012. "Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation," Economics Working Paper Series 1202, University of St. Gallen, School of Economics and Political Science.
    43. Peter Reinhard Hansen & Guillaume Horel & Asger Lunde & Ilya Archakov, 2015. "A Markov Chain Estimator of Multivariate Volatility from High Frequency Data," CREATES Research Papers 2015-19, Department of Economics and Business Economics, Aarhus University.
    44. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    45. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    46. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    47. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.
    48. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    49. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
    50. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.

  44. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2009. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," CFS Working Paper Series 2009/18, Center for Financial Studies (CFS).

    Cited by:

    1. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    2. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
    3. Siikanen, Milla & Kanniainen, Juho & Luoma, Arto, 2017. "What drives the sensitivity of limit order books to company announcement arrivals?," Economics Letters, Elsevier, vol. 159(C), pages 65-68.
    4. Chen Cathy Yi-Hsuan & Härdle Wolfgang Karl, 2017. "Data science and digital society," Proceedings of the International Conference on Business Excellence, Paradigm, vol. 11(1), pages 669-675, July.
    5. Siikanen, Milla & Kanniainen, Juho & Valli, Jaakko, 2017. "Limit order books and liquidity around scheduled and non-scheduled announcements: Empirical evidence from NASDAQ Nordic," Finance Research Letters, Elsevier, vol. 21(C), pages 264-271.
    6. Hautsch, Nikolaus & Huang, Ruihong, 2009. "The market impact of a limit order," CFS Working Paper Series 2009/23, Center for Financial Studies (CFS).
    7. Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    8. Robert Engle & Michael J. Fleming & Eric Ghysels & Giang Nguyen, 2012. "Liquidity and volatility in the U.S. treasury market," Staff Reports 590, Federal Reserve Bank of New York.
    9. Härdle, Wolfgang Karl & Majer, Piotr, 2012. "Yield curve modeling and forecasting using semiparametric factor dynamics," SFB 649 Discussion Papers 2012-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2017. "Dynamic semiparametric factor model with a common break," SFB 649 Discussion Papers 2017-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
    12. Choros-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2013. "CDO surfaces dynamics," SFB 649 Discussion Papers 2013-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Meihui Guo & Yi-Ting Guo & Chi-Jeng Wang & Liang-Ching Lin, 2015. "Assessing influential trade effects via high-frequency market reactions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1458-1471, July.
    14. Geir H. Bjønnes & Carol L. Osler & Dagfinn Rime, 2021. "Price discovery in two‐tier markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3109-3133, April.
    15. Choroś-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2016. "A semiparametric factor model for CDO surfaces dynamics," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 151-163.
    16. Hongxuan Yan & Gareth W. Peters & Guillaume Bagnarosa & Jennifer Chan, 2025. "Futures Open Interest and Speculative Pressure Dynamics via Bayesian Models of Long‐Memory Count Processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(7), pages 2252-2276, November.
    17. Chen, Ying & Chua, Wee Song & Härdle, Wolfgang Karl, 2016. "Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics," SFB 649 Discussion Papers 2016-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Hai-Chuan Xu & Wei Chen & Xiong Xiong & Wei Zhang & Wei-Xing Zhou & H Eugene Stanley, 2016. "Limit-order book resiliency after effective market orders: Spread, depth and intensity," Papers 1602.00731, arXiv.org, revised Feb 2017.

  45. Hautsch, Nikolaus & Ou, Yangguoyi, 2009. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," CFS Working Paper Series 2009/03, Center for Financial Studies (CFS).

    Cited by:

    1. Kleppe, Tore Selland & Liesenfeld, Roman & Moura, Guilherme Valle & Oglend, Atle, 2022. "Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility," Econometrics and Statistics, Elsevier, vol. 23(C), pages 105-127.
    2. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    3. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2014. "Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?," Working Paper Series 2014-3, Federal Reserve Bank of San Francisco.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    5. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    6. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    7. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    8. Marius Acatrinei, 2017. "Macroeconomic fundamentals and latent factor of the EU yield curve," EIOPA Financial Stability Report - Thematic Articles 11, EIOPA, Risks and Financial Stability Department.
    9. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.
    10. Afzol Husain & Kwang-Jing Yii & Chorng Yuan Fung & Richard Busulwa, 2025. "Portfolio risk of cryptocurrency inclusion: a comparison among conventional cryptocurrencies and asset-backed cryptocurrencies," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(3), pages 687-739, September.
    11. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

  46. Hautsch, Nikolaus & Ou, Yangguoyi, 2009. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," CFS Working Paper Series 2009/03, Center for Financial Studies (CFS).

    Cited by:

    1. Kleppe, Tore Selland & Liesenfeld, Roman & Moura, Guilherme Valle & Oglend, Atle, 2022. "Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility," Econometrics and Statistics, Elsevier, vol. 23(C), pages 105-127.
    2. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    3. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2014. "Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?," Working Paper Series 2014-3, Federal Reserve Bank of San Francisco.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    5. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    6. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    7. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    8. Marius Acatrinei, 2017. "Macroeconomic fundamentals and latent factor of the EU yield curve," EIOPA Financial Stability Report - Thematic Articles 11, EIOPA, Risks and Financial Stability Department.
    9. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper series 59_12, Rimini Centre for Economic Analysis, revised Sep 2012.
    10. Afzol Husain & Kwang-Jing Yii & Chorng Yuan Fung & Richard Busulwa, 2025. "Portfolio risk of cryptocurrency inclusion: a comparison among conventional cryptocurrencies and asset-backed cryptocurrencies," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(3), pages 687-739, September.
    11. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

  47. Hautsch, Nikolaus & Kyj, Lada M. & Oomen, Roel C.A., 2009. "A blocking and regularization approach to high dimensional realized covariance estimation," SFB 649 Discussion Papers 2009-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    3. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    4. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    6. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    7. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    8. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    10. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    11. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    12. Taras Bodnar & Nikolaus Hautsch & Yarema Okhrin & Nestor Parolya, 2024. "Consistent Estimation of the High-Dimensional Efficient Frontier," Papers 2409.15103, arXiv.org.
    13. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    14. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    15. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    16. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    17. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    18. Jin, Xin & Maheu, John M, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," MPRA Paper 60102, University Library of Munich, Germany.
    19. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
    20. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    21. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    22. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    23. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    24. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
    25. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, Department of Economics and Business Economics, Aarhus University.
    26. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    27. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    28. Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
    29. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    30. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
    31. Pirino, Davide & Pollastri, Alessandro & Trapin, Luca, 2025. "Testing liquidity: A statistical theory based on asset staleness," Econometrics and Statistics, Elsevier, vol. 35(C), pages 23-40.
    32. Manabu Asai & Michael McAleer, 2015. "The Impact of Jumps and Leverage in Forecasting Co-Volatility," Documentos de Trabajo del ICAE 2015-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    33. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023. "The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
    34. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    35. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    36. Barnes, George & Ramgoolam, Sanjaye & Stephanou, Michael, 2024. "Permutation invariant Gaussian matrix models for financial correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).
    37. Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.
    38. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    39. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    40. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    41. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    42. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2021. "High-dimensional estimation of quadratic variation based on penalized realized variance," Papers 2103.03237, arXiv.org, revised Jan 2026.
    43. Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012. "Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation," Economics Working Paper Series 1202, University of St. Gallen, School of Economics and Political Science.
    44. Peter Reinhard Hansen & Guillaume Horel & Asger Lunde & Ilya Archakov, 2015. "A Markov Chain Estimator of Multivariate Volatility from High Frequency Data," CREATES Research Papers 2015-19, Department of Economics and Business Economics, Aarhus University.
    45. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    46. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    47. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    48. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.
    49. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    50. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
    51. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.

  48. Hautsch, Nikolaus & Huang, Ruihong, 2009. "The market impact of a limit order," SFB 649 Discussion Papers 2009-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gabriel Yergeau, 2016. "Profitability and Market Quality of High Frequency Market-makers: An Empirical Investigation," Working Papers 16-3, HEC Montreal, Canada Research Chair in Risk Management.
    2. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    3. Fabrizio Pomponio & Frédéric Abergel, 2013. "Multiple-limit trades : empirical facts and application to lead-lag measures," Post-Print hal-00745317, HAL.
    4. Mircea BAHNA & Cosmin-Octavian CEPOI & Bogdan Andrei DUMITRESCU & Virgil DAMIAN, 2018. "Estimating the Price Impact of Market Orders on the Bucharest Stock Exchange," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 120-133, December.
    5. Jianchang Zhu & Leilei Zhang & Xuchu Sun, 2024. "Optimal liquidation using extended trading close for multiple trading days," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-33, December.
    6. M. Fr Mmel & X. Han & F. Van Gysegem, 2013. "News, Liquidity Dynamics and Intraday Jumps: Evidence from the HUF/EUR market," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/848, Ghent University, Faculty of Economics and Business Administration.
    7. Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
    8. Fleming, Michael J. & Mizrach, Bruce & Nguyen, Giang, 2018. "The microstructure of a U.S. Treasury ECN: The BrokerTec platform," Journal of Financial Markets, Elsevier, vol. 40(C), pages 2-22.
    9. Makoto Takahashi, 2025. "Returns and Order Flow Imbalances: Intraday Dynamics and Macroeconomic News Effects," Papers 2508.06788, arXiv.org, revised Oct 2025.
    10. Hautsch, Nikolaus & Podolskij, Mark, 2010. "Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence," CFS Working Paper Series 2010/17, Center for Financial Studies (CFS).
    11. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    12. Alain P. Chaboud & Erik Hjalmarsson & Filip Zikes, 2020. "The Evolution of Price Discovery in an Electronic Market," Finance and Economics Discussion Series 2020-051, Board of Governors of the Federal Reserve System (U.S.).
    13. Johannes Bleher & Michael Bleher & Thomas Dimpfl, 2020. "From orders to prices: A stochastic description of the limit order book to forecast intraday returns," Papers 2004.11953, arXiv.org, revised May 2021.
    14. Alex Frino & Ognjen Kovačević & Vito Mollica, 2019. "Depths and spreads in futures markets: Relationship with order execution, submission, and cancellation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(5), pages 590-599, May.
    15. Erdinc Akyildirim & Shaen Corbet & Guzhan Gulay & Duc Khuong Nguyen & Ahmet Sensoy, 2019. "Order Flow Persistence in Equity Spot and Futures Markets: Evidence from a Dynamic Emerging Market," Working Papers 2019-011, Department of Research, Ipag Business School.
    16. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    17. Przemys{l}aw Rola, 2025. "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets," Papers 2507.09734, arXiv.org.
    18. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    19. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid-Ask Spread?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1482-1498.
    20. Esen Onur & John S. Roberts & Tugkan Tuzun, 2017. "Trader Positions and Marketwide Liquidity Demand," Finance and Economics Discussion Series 2017-103, Board of Governors of the Federal Reserve System (U.S.).
    21. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Frédéric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Post-Print hal-01561128, HAL.
    22. Francesco Calvori & Fabrizio Cipollini & Giampiero M. Gallo, 2014. "Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares," Econometrics Working Papers Archive 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    23. Fabrizio Pomponio & Frederic Abergel, 2012. "Multiple-limit trades: empirical facts and application to lead--lag measures," Quantitative Finance, Taylor & Francis Journals, vol. 13(5), pages 783-793, September.
    24. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    25. Lo, Danny K. & Hall, Anthony D., 2015. "Resiliency of the limit order book," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 222-244.
    26. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    27. Ying Chen & Ulrich Horst & Hoang Hai Tran, 2026. "Optimal Trade Execution Under Endogenous Order Flow," Operations Research, INFORMS, vol. 74(1), pages 72-92, January.
    28. Cebiroğlu, Gökhan & Horst, Ulrich, 2015. "Optimal order display in limit order markets with liquidity competition," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 81-100.
    29. Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    30. Hautsch, Nikolaus & Huang, Ruihong, 2012. "On the dark side of the market: Identifying and analyzing hidden order placements," CFS Working Paper Series 2012/04, Center for Financial Studies (CFS).
    31. Będowska-Sójka, Barbara, 2020. "Do aggressive orders affect liquidity? An evidence from an emerging market," Research in International Business and Finance, Elsevier, vol. 54(C).
    32. Zoltán Eisler & Jean-Philippe Bouchaud & Julien Kockelkoren, 2012. "The price impact of order book events: market orders, limit orders and cancellations," Quantitative Finance, Taylor & Francis Journals, vol. 12(9), pages 1395-1419, September.
    33. Robert Engle & Michael J. Fleming & Eric Ghysels & Giang Nguyen, 2012. "Liquidity and volatility in the U.S. treasury market," Staff Reports 590, Federal Reserve Bank of New York.
    34. Zhao, Chaoyi & Chen, Yufan & Wu, Lintong & Dai, Yuehao & Chen, Ermo & Wu, Lan & Zhang, Ruixun, 2025. "High-frequency liquidity in the Chinese stock market: Measurements, patterns, and determinants," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    35. Timoth'ee Fabre & Vincent Ragel, 2023. "Interpretable ML for High-Frequency Execution," Papers 2307.04863, arXiv.org, revised Sep 2024.
    36. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2010. "The Price Impact of Order Book Events," Papers 1011.6402, arXiv.org, revised Apr 2011.
    37. Cebiroglu, Gökhan & Hautsch, Nikolaus & Horst, Ulrich, 2014. "Order exposure and liquidity coordination: Does hidden liquidity harm price efficiency?," CFS Working Paper Series 468, Center for Financial Studies (CFS).
    38. Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    39. Zhicheng Li & Haipeng Xing & Xinyun Chen, 2019. "A multifactor regime-switching model for inter-trade durations in the limit order market," Papers 1912.00764, arXiv.org.
    40. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    41. Antonio Figueiredo & Pankaj Jain & Suchismita Mishra, 2023. "The role of fleeting orders on option expiration days," Quantitative Finance, Taylor & Francis Journals, vol. 23(10), pages 1511-1529, October.
    42. Chanaka Edirisinghe & Jingnan Chen & Jaehwan Jeong, 2023. "Optimal Leveraged Portfolio Selection Under Quasi-Elastic Market Impact," Operations Research, INFORMS, vol. 71(5), pages 1558-1576, September.
    43. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    44. Xintong Wang & Christopher Hoang & Yevgeniy Vorobeychik & Michael P. Wellman, 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis," Games, MDPI, vol. 12(2), pages 1-43, May.
    45. Han, SeungOh, 2024. "Price clustering on cryptocurrency order books at a US-based exchange," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    46. Forsyth, P.A. & Kennedy, J.S. & Tse, S.T. & Windcliff, H., 2012. "Optimal trade execution: A mean quadratic variation approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1971-1991.
    47. Xinyue He & Teresa Serra & Philip Garcia, 2021. "Resilience in “Flash Events” in the Corn and Lean Hog Futures Markets," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 743-764, March.
    48. Jose Blanchet & Xinyun Chen, 2013. "Continuous-time Modeling of Bid-Ask Spread and Price Dynamics in Limit Order Books," Papers 1310.1103, arXiv.org.
    49. Chen, Yuanyuan & Gao, Xuefeng & Li, Duan, 2018. "Optimal order execution using hidden orders," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 89-116.
    50. Neil A. Chriss, 2024. "Optimal position-building strategies in competition," Papers 2409.03586, arXiv.org, revised Nov 2024.
    51. Ulrich Horst & Dorte Kreher, 2015. "A weak law of large numbers for a limit order book model with fully state dependent order dynamics," Papers 1502.04359, arXiv.org, revised May 2016.
    52. Zhu, Hongyu & Yamamoto, Ryuichi, 2022. "Order submission, information asymmetry, and tick size," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    53. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Fr'ed'eric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Papers 1802.08502, arXiv.org, revised May 2022.
    54. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015, January-A.
    55. Thomas A. P. de Boer & Cornelis Gardebroek & Joost M. E. Pennings & Andres Trujillo‐Barrera, 2022. "Intraday liquidity in soybean complex futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1189-1211, July.
    56. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.
    57. Alexandre Aidov & Olesya Lobanova, 2021. "The Relation between Intraday Limit Order Book Depth and Spread," IJFS, MDPI, vol. 9(4), pages 1-13, November.
    58. Xue, Yi & Gençay, Ramazan, 2012. "Hierarchical information and the rate of information diffusion," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1372-1401.
    59. Yi Cao & Jia Zhai, 2022. "Estimating price impact via deep reinforcement learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3954-3970, October.
    60. Jia Zhai & Yi Cao & Xuemei Ding, 2018. "Data analytic approach for manipulation detection in stock market," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 897-932, April.
    61. Amaya, Diego & Filbien, Jean-Yves & Okou, Cédric & Roch, Alexandre F., 2018. "Distilling liquidity costs from limit order books," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 16-34.
    62. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    63. Carlos Jorge Lenczewski Martins, 2019. "Market and limit orders and their role in the price discovery process," Bank i Kredyt, Narodowy Bank Polski, vol. 50(6), pages 551-570.
    64. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    65. Mehdi Arzandeh & Julieta Frank & Justin Daniels, 2025. "Real‐Time Tracking of Public Announcements in the Limit Order Book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(6), pages 569-599, June.
    66. Çağlayan-Gümüş, Ayşe & Karahan, Cenk C., 2024. "Information content of the limit order book: A cross-sectional analysis in Borsa Istanbul," Global Finance Journal, Elsevier, vol. 62(C).

  49. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Zhang, Zhengjun & Zhu, Bin, 2016. "Copula structured M4 processes with application to high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 231-241.

  50. Hautsch, Nikolaus & Hess, Dieter E. & Müller, Christoph, 2008. "Price adjustment to news with uncertain precision," SFB 649 Discussion Papers 2008-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    2. Ying, Shan & Sheen, Jeffrey & Gu, Xin & Wang, Ben Zhe, 2025. "Does monetary policy uncertainty moderate the transmission of policy shocks to government bond yields?," Journal of International Money and Finance, Elsevier, vol. 154(C).
    3. Pervaiz Alam & Xiaoling Pu & Barry Hettler & Hai Lin, 2020. "The pricing of accruals quality in credit default swap spreads," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(3), pages 1943-1977, September.
    4. Peter Reinhard Hansen & Chen Tong, 2026. "Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models," Papers 2605.01665, arXiv.org, revised May 2026.
    5. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers 11-13, University of Cologne, Centre for Financial Research (CFR).

  51. Hautsch, Nikolaus & Hess, Dieter E. & Müller, Christoph, 2008. "Price adjustment to news with uncertain precision," SFB 649 Discussion Papers 2008-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    2. Ying, Shan & Sheen, Jeffrey & Gu, Xin & Wang, Ben Zhe, 2025. "Does monetary policy uncertainty moderate the transmission of policy shocks to government bond yields?," Journal of International Money and Finance, Elsevier, vol. 154(C).
    3. Pervaiz Alam & Xiaoling Pu & Barry Hettler & Hai Lin, 2020. "The pricing of accruals quality in credit default swap spreads," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(3), pages 1943-1977, September.
    4. Peter Reinhard Hansen & Chen Tong, 2026. "Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models," Papers 2605.01665, arXiv.org, revised May 2026.
    5. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers 11-13, University of Cologne, Centre for Financial Research (CFR).

  52. Hautsch, Nikolaus & Ou, Yangguoyi, 2008. "Discrete-time stochastic volatility models and MCMC-based statistical inference," SFB 649 Discussion Papers 2008-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Saranya, K. & Prasanna, P. Krishna, 2018. "Estimating stochastic volatility with jumps and asymmetry in Asian markets," Finance Research Letters, Elsevier, vol. 25(C), pages 145-153.

  53. Hautsch, Nikolaus & Ou, Yangguoyi, 2008. "Yield curve factors, term structure volatility, and bond risk premia," SFB 649 Discussion Papers 2008-053, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Nath, Golaka, 2012. "Estimating term structure changes using principal component analysis in Indian sovereign bond market," MPRA Paper 39229, University Library of Munich, Germany.
    2. Anna Cieslak & Pavol Povala, 2016. "Information in the Term Structure of Yield Curve Volatility," Journal of Finance, American Finance Association, vol. 71(3), pages 1393-1436, June.
    3. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.

  54. Hautsch, Nikolaus, 2007. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," CFS Working Paper Series 2007/25, Center for Financial Studies (CFS).

    Cited by:

    1. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    3. David German & Henry Schellhorn, 2012. "A No-Arbitrage Model of Liquidity in Financial Markets involving Brownian Sheets," Papers 1206.4804, arXiv.org.
    4. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    5. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    6. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    7. Ng, F.C. & Li, W.K. & Yu, Philip L.H., 2016. "Diagnostic checking of the vector multiplicative error model," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 86-97.
    8. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    9. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    10. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    11. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    12. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," SFB 649 Discussion Papers 2009-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Sergey Lototsky & Henry Schellhorn & Ran Zhao, 2016. "A String Model of Liquidity in Financial Markets," Papers 1608.05900, arXiv.org, revised Apr 2018.
    14. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    15. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    16. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    17. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    18. Bodnar, Taras & Hautsch, Nikolaus, 2013. "Copula-based dynamic conditional correlation multiplicative error processes," CFS Working Paper Series 2013/19, Center for Financial Studies (CFS).
    19. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    20. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    22. Kul B. Luintel & Yongdeng Xu, 2017. "Testing weak exogeneity in multiplicative error models," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1617-1630, October.
    23. Vahidin Jeleskovic, 2024. "An Empirical Analysis of Scam Tokens on Ethereum Blockchain," Papers 2402.19399, arXiv.org, revised Mar 2024.
    24. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.
    25. Kyungsub Lee, 2026. "Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process," Papers 2604.00346, arXiv.org.

  55. Bauwens, Luc & Hautsch, Nikolaus, 2007. "Modelling financial high frequency data using point processes," SFB 649 Discussion Papers 2007-066, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Anatoliy Swishchuk & Aiden Huffman, 2020. "General Compound Hawkes Processes in Limit Order Books," Risks, MDPI, vol. 8(1), pages 1-25, March.
    2. Herrera, Rodrigo & Schipp, Bernhard, 2013. "Value at risk forecasts by extreme value models in a conditional duration framework," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 33-47.
    3. Hamza Bodor & Laurent Carlier, 2024. "A Novel Approach to Queue-Reactive Models: The Importance of Order Sizes," Papers 2405.18594, arXiv.org.
    4. Dungey, Mardi & Henry, Olan & McKenzie, Michael, 2010. "From Trade-to-Trade in US Treasuries," Working Papers 10446, University of Tasmania, Tasmanian School of Business and Economics, revised 01 May 2010.
    5. Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2018. "State-dependent Hawkes processes and their application to limit order book modelling," Papers 1809.08060, arXiv.org, revised Sep 2021.
    6. Ban Zheng & François Roueff & Frédéric Abergel, 2014. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Post-Print hal-00777941, HAL.
    7. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723.
    8. Adam Clements & Yin Liao, 2014. "The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index," NCER Working Paper Series 101, National Centre for Econometric Research.
    9. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.
    10. Giada Adelfio & Arianna Agosto & Marcello Chiodi & Paolo Giudici, 2021. "Financial contagion through space-time point processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 665-688, June.
    11. Bacry, E. & Delattre, S. & Hoffmann, M. & Muzy, J.F., 2013. "Some limit theorems for Hawkes processes and application to financial statistics," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2475-2499.
    12. Angelos Dassios & Jiwook Jang & Hongbiao Zhao, 2019. "A Generalised CIR Process with Externally-Exciting and Self-Exciting Jumps and Its Applications in Insurance and Finance," Risks, MDPI, vol. 7(4), pages 1-18, October.
    13. V. Filimonov & D. Sornette, 2015. "Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1293-1314, August.
    14. Steffen Volkenand & Günther Filler & Martin Odening, 2020. "Price Discovery and Market Reflexivity in Agricultural Futures Contracts with Different Maturities," Risks, MDPI, vol. 8(3), pages 1-17, July.
    15. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Risks, MDPI, vol. 8(3), pages 1-20, September.
    16. Siakoulis, Vasilios, 2015. "Modeling bank default intensity in the USA using autoregressive duration models," MPRA Paper 64526, University Library of Munich, Germany.
    17. Hainaut, Donatien, 2016. "A bivariate Hawkes process for interest rate modeling," Economic Modelling, Elsevier, vol. 57(C), pages 180-196.
    18. Pietro Fodra & Huy^en Pham, 2013. "High frequency trading and asymptotics for small risk aversion in a Markov renewal model," Papers 1310.1756, arXiv.org, revised Jan 2015.
    19. Hainaut, Donatien, 2016. "Impact of volatility clustering on equity indexed annuities," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 367-381.
    20. Henryk Gurgul & Robert Syrek & Christoph Mitterer, 2016. "Price duration versus trading volume in high-frequency data for selected DAX companies," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(2), pages 241-260.
    21. Álvaro Cartea, 2013. "Derivatives pricing with marked point processes using tick-by-tick data," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 111-123, January.
    22. Aman Ullah & Mardi Dungey & Xiangdong Long & Yun Wang, 2014. "A Semiparametric Conditional Duration Model," Working Papers 201408, University of California at Riverside, Department of Economics.
    23. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    24. Fan, Lina & Yang, Hao & Zhai, Jia & Zhang, Xiaotao, 2023. "Forecasting stock volatility during the stock market crash period: The role of Hawkes process," Finance Research Letters, Elsevier, vol. 55(PA).
    25. Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang, 2021. "Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 921-941, August.
    26. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2011. "Modeling microstructure noise with mutually exciting point processes," Papers 1101.3422, arXiv.org.
    27. Roueff, Francois & von Sachs, Rainer & Sansonnet, Laure, 2015. "Time-frequency analysis of locally stationary Hawkes processes," LIDAM Discussion Papers ISBA 2015011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    28. D’Amico, Guglielmo & Petroni, Filippo, 2018. "Copula based multivariate semi-Markov models with applications in high-frequency finance," European Journal of Operational Research, Elsevier, vol. 267(2), pages 765-777.
    29. Clements, Adam & Liao, Yin, 2017. "Forecasting the variance of stock index returns using jumps and cojumps," International Journal of Forecasting, Elsevier, vol. 33(3), pages 729-742.
    30. Zhang, Yaohua & Zou, Jian & Ravishanker, Nalini & Thavaneswaran, Aerambamoorthy, 2019. "Modeling financial durations using penalized estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 145-158.
    31. Alessio Sancetta, 2017. "Estimation for the Prediction of Point Processes with Many Covariates," Papers 1702.05315, arXiv.org.
    32. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
    33. Li, Jie & Li, Guangzhong & Zhou, Yinggang, 2015. "Do securitized real estate markets jump? International evidence," Pacific-Basin Finance Journal, Elsevier, vol. 31(C), pages 13-35.
    34. Jang, Jiwook & Dassios, Angelos, 2013. "A bivariate shot noise self-exciting process for insurance," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 524-532.
    35. Donatien Hainaut, 2016. "A bivariate Hawkes process based model, for interest rates," Post-Print hal-01458162, HAL.
    36. Zhu, Lingjiong, 2013. "Moderate deviations for Hawkes processes," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 885-890.
    37. Ioane Muni Toke, 2011. ""Market making" behaviour in an order book model and its impact on the bid-ask spread," Post-Print hal-01705266, HAL.
    38. Liu, Chenguang, 2020. "Statistical inference for a partially observed interacting system of Hawkes processes," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5636-5694.
    39. Cavaliere, Giuseppe & Lu, Ye & Rahbek, Anders & Stærk-Østergaard, Jacob, 2023. "Bootstrap inference for Hawkes and general point processes," Journal of Econometrics, Elsevier, vol. 235(1), pages 133-165.
    40. Fei Xie & Yang Liu & Changlong Hu & Shenbao Liang, 2025. "Dynamic Modeling of Limit Order Book and Market Maker Strategy Optimization Based on Markov Queue Theory," Mathematics, MDPI, vol. 13(5), pages 1-27, February.
    41. Chavez-Demoulin, V. & McGill, J.A., 2012. "High-frequency financial data modeling using Hawkes processes," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3415-3426.
    42. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    43. Donatien Hainaut, 2016. "A model for interest rates with clustering effects," Post-Print hal-01393994, HAL.
    44. Jiwook Jang & Rosy Oh, 2020. "A Bivariate Compound Dynamic Contagion Process for Cyber Insurance," Papers 2007.04758, arXiv.org.
    45. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    46. Anatoliy Swishchuk & Aiden Huffman, 2018. "General Compound Hawkes Processes in Limit Order Books," Papers 1812.02298, arXiv.org.
    47. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
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    66. Anatoliy Swishchuk, 2017. "General Compound Hawkes Processes in Limit Order Books," Papers 1706.07459, arXiv.org, revised Jun 2017.
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    72. Wheatley, Spencer & Filimonov, Vladimir & Sornette, Didier, 2016. "The Hawkes process with renewal immigration & its estimation with an EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 120-135.
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    74. Anatoliy Swishchuk & Bruno Remillard & Robert Elliott & Jonathan Chavez-Casillas, 2017. "Compound Hawkes Processes in Limit Order Books," Papers 1712.03106, arXiv.org.
    75. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
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    77. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.
    78. Marcello Rambaldi & Emmanuel Bacry & Fabrizio Lillo, 2016. "The role of volume in order book dynamics: a multivariate Hawkes process analysis," Papers 1602.07663, arXiv.org.
    79. Mariko I Ito & Yudai Honma & Takaaki Ohnishi & Tsutomu Watanabe & Kazuyuki Aihara, 2024. "Exogenous and endogenous factors affecting stock market transactions: A Hawkes process analysis of the Tokyo Stock Exchange during the COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
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    81. Adam E Clements & Yin Liao, 2013. "Modeling and forecasting realized volatility: getting the most out of the jump component," NCER Working Paper Series 93, National Centre for Econometric Research.
    82. Ulrich Horst & Wei Xu, 2024. "Functional Limit Theorems for Hawkes Processes," Papers 2401.11495, arXiv.org, revised Dec 2024.
    83. Angelos Dassios & Xin Dong, 2014. "Stationarity of Bivariate Dynamic Contagion Processes," Papers 1405.5842, arXiv.org.
    84. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Papers 2008.00124, arXiv.org.
    85. E. Bacry & J. F Muzy, 2013. "Hawkes model for price and trades high-frequency dynamics," Papers 1301.1135, arXiv.org.
    86. Monira Essa Aloud, 2016. "Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 55-64.
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    88. Anton Golub & Gregor Chliamovitch & Alexandre Dupuis & Bastien Chopard, 2014. "Multi-scale Representation of High Frequency Market Liquidity," Papers 1402.2198, arXiv.org.
    89. Zhuohan Wang & Carmine Ventre, 2025. "DiffVolume: Diffusion Models for Volume Generation in Limit Order Books," Papers 2508.08698, arXiv.org.
    90. Anatoliy Swishchuk, 2021. "Modelling of Limit Order Books by General Compound Hawkes Processes with Implementations," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 399-428, March.
    91. Ketelbuters, John John & Hainaut, Donatien, 2021. "Time-Consistent Evaluation of Credit Risk with Contagion," LIDAM Discussion Papers ISBA 2021004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    92. Ban Zheng & Franc{c}ois Roueff & Fr'ed'eric Abergel, 2013. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Papers 1301.5007, arXiv.org, revised Feb 2014.
    93. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    94. Hainaut, Donatien, 2019. "Fractional Hawkes processes," LIDAM Discussion Papers ISBA 2019016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    95. Lucio Maria Calcagnile & Giacomo Bormetti & Michele Treccani & Stefano Marmi & Fabrizio Lillo, 2015. "Collective synchronization and high frequency systemic instabilities in financial markets," Papers 1505.00704, arXiv.org.
    96. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    97. Adam Clements & Yin Liao, 2013. "The dynamics of co-jumps, volatility and correlation," NCER Working Paper Series 91, National Centre for Econometric Research.
    98. Alexander Saichev & Thomas Maillart & Didier Sornette, 2013. "Hierarchy of temporal responses of multivariate self-excited epidemic processes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(4), pages 1-19, April.
    99. Alessio Sancetta, 2023. "Intraday Trades Profile Estimation: An Intensity Approach," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 651-677.
    100. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, May.
    101. Ulrich Horst & Wei Xu & Rouyi Zhang, 2023. "Convergence of Heavy-Tailed Hawkes Processes and the Microstructure of Rough Volatility," Papers 2312.08784, arXiv.org, revised Mar 2026.
    102. Marcello Rambaldi & Vladimir Filimonov & Fabrizio Lillo, 2016. "Detection of intensity bursts using Hawkes processes: an application to high frequency financial data," Papers 1610.05383, arXiv.org.
    103. Kyungsub Lee, 2026. "Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process," Papers 2604.00346, arXiv.org.
    104. Filimonov, Vladimir & Bicchetti, David & Maystre, Nicolas & Sornette, Didier, 2014. "Quantification of the high level of endogeneity and of structural regime shifts in commodity markets," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 174-192.

  56. Hautsch, Nikolaus, 2007. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," SFB 649 Discussion Papers 2007-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    3. David German & Henry Schellhorn, 2012. "A No-Arbitrage Model of Liquidity in Financial Markets involving Brownian Sheets," Papers 1206.4804, arXiv.org.
    4. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    5. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    6. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    7. Ng, F.C. & Li, W.K. & Yu, Philip L.H., 2016. "Diagnostic checking of the vector multiplicative error model," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 86-97.
    8. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    9. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    10. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    11. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    12. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," SFB 649 Discussion Papers 2009-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Sergey Lototsky & Henry Schellhorn & Ran Zhao, 2016. "A String Model of Liquidity in Financial Markets," Papers 1608.05900, arXiv.org, revised Apr 2018.
    14. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    15. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    16. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    17. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    18. Bodnar, Taras & Hautsch, Nikolaus, 2013. "Copula-based dynamic conditional correlation multiplicative error processes," CFS Working Paper Series 2013/19, Center for Financial Studies (CFS).
    19. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    20. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    22. Kul B. Luintel & Yongdeng Xu, 2017. "Testing weak exogeneity in multiplicative error models," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1617-1630, October.
    23. Vahidin Jeleskovic, 2024. "An Empirical Analysis of Scam Tokens on Ethereum Blockchain," Papers 2402.19399, arXiv.org, revised Mar 2024.
    24. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.
    25. Kyungsub Lee, 2026. "Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process," Papers 2604.00346, arXiv.org.

  57. Frank Gerhard & Nikolaus Hautsch, 2006. "A Dynamic Semiparametric Proportional Hazard Model," FRU Working Papers 2006/05, University of Copenhagen. Department of Economics. Finance Research Unit.

    Cited by:

    1. Cosma, Antonio & Galli, Fausto, 2014. "A non parametric ACD model," MPRA Paper 53990, University Library of Munich, Germany.
    2. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.

  58. Nikolaus Hautsch, 2006. "Testing the Conditional Mean Function of Autoregressive Conditional Duration Models," FRU Working Papers 2006/06, University of Copenhagen. Department of Economics. Finance Research Unit.

    Cited by:

    1. Dungey, Mardi & Henry, Olan & McKenzie, Michael, 2010. "From Trade-to-Trade in US Treasuries," Working Papers 10446, University of Tasmania, Tasmanian School of Business and Economics, revised 01 May 2010.
    2. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    4. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
    6. Allen, David & Chan, Felix & McAleer, Michael & Peiris, Shelton, 2008. "Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks," Journal of Econometrics, Elsevier, vol. 147(1), pages 163-185, November.
    7. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Indeewara Perera & Javier Hidalgo & Mervyn J. Silvapulle, 2016. "A Goodness-of-Fit Test for a Class of Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1111-1141, June.
    9. Dungey, Mardi & Jeyasreedharan, Nagaratnam & Li, Tuo, 2010. "Modelling the Time Between Trades in the After-Hours Electronic Equity Futures Market," Working Papers 10451, University of Tasmania, Tasmanian School of Business and Economics, revised 30 May 2012.

  59. BAUWENS, Luc & HAUTSCH, Nikolaus, 2006. "Stochastic conditional intensity processes," LIDAM Reprints CORE 1937, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Florian Heiss, 2008. "Sequential numerical integration in nonlinear state space models for microeconometric panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 373-389.
    2. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
    3. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    4. Yogo Purwono & Irwan Adi Ekaputra & Zaäfri Ananto Husodo, 2018. "Estimation of Dynamic Mixed Hitting Time Model Using Characteristic Function Based Moments," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 295-321, February.
    5. Aneta Hryckiewicz & Piotr Mielus & Karolina Skorulska & Małgorzata Snarska, 2018. "Does a bank levy increase frictions on the interbank market?," KAE Working Papers 2018-033, Warsaw School of Economics, Collegium of Economic Analysis.
    6. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    7. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    8. Lange, Rutger-Jan, 2024. "Bellman filtering and smoothing for state–space models," Journal of Econometrics, Elsevier, vol. 238(2).
    9. Drew Creal & Siem Jan Koopman & André Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers 08-108/4, Tinbergen Institute.
    10. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
    11. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    12. Wing Lon NG, 2004. "Duration and Order Type Clusters," Econometric Society 2004 Far Eastern Meetings 730, Econometric Society.
    13. Kohler, Alexander & von Wyss, Rico, 2012. "Where does Information Processing in a Fragmented Market Take Place? – Evidence from the Swiss Stock Market after MiFID," Working Papers on Finance 1209, University of St. Gallen, School of Finance.
    14. Marco Minozzo & Silvia Centanni, 2012. "Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes," Working Papers 11/2012, University of Verona, Department of Economics.
    15. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2012. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," Tinbergen Institute Discussion Papers 12-020/4, Tinbergen Institute.
    16. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    17. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
    18. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    19. Wu, Zhengxiao, 2012. "On the intraday periodicity duration adjustment of high-frequency data," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 282-291.
    20. Bouezmarni, Taoufik & Rombouts, Jeroen V.K., 2010. "Nonparametric density estimation for positive time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 245-261, February.
    21. Blazsek, Szabolcs & Escribano, Alvaro, 2010. "Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors," Journal of Econometrics, Elsevier, vol. 159(1), pages 14-32, November.
    22. Herrera, Rodrigo & Schipp, Bernhard, 2011. "Extreme value models in a conditional duration intensity framework," SFB 649 Discussion Papers 2011-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    24. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2011. "Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers 11-057/4, Tinbergen Institute, revised 27 Jan 2012.
    25. Liesenfeld, Roman & Richard, Jean-François, 2008. "Improving MCMC, using efficient importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 272-288, December.
    26. Zhang, Xuan & Kim, Minjoo & Yan, Cheng & Zhao, Yang, 2024. "Default dependence in the insurance and banking sectors: A copula approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    27. Deniz Erer, 2023. "The Impact of News Related Covid-19 on Exchange Rate Volatility:A New Evidence From Generalized Autoregressive Score Model," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(38), pages 105-126, June.
    28. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Patent propensity, R&D and market competition: Dynamic spillovers of innovation leaders and followers," Journal of Econometrics, Elsevier, vol. 191(1), pages 145-163.
    29. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    30. Ouyang, Ruolan & Zhang, Xuan, 2020. "Financialization of agricultural commodities: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 381-389.
    31. Kien C. Tran & Panayotis G. Michaelides, 2025. "A class of generalized autoregressive score panel stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 64(3), pages 235-253, December.
    32. Katarzyna Bień-Barkowska, 2014. "Capturing Order Book Dynamics in the Interbank EUR/PLN Spot Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(1), pages 93-117, January.
    33. Nolte, Ingmar & Voev, Valeri, 2007. "Panel intensity models with latent factors: An application to the trading dynamics on the foreign exchange market," CoFE Discussion Papers 07/02, University of Konstanz, Center of Finance and Econometrics (CoFE).
    34. Monteiro, André A., 2009. "The econometrics of randomly spaced financial data: a survey," DES - Working Papers. Statistics and Econometrics. WS ws097924, Universidad Carlos III de Madrid. Departamento de Estadística.
    35. André A. Monteiro, 2008. "Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation," Tinbergen Institute Discussion Papers 08-021/2, Tinbergen Institute.
    36. Herrera, Rodrigo, 2013. "Energy risk management through self-exciting marked point process," Energy Economics, Elsevier, vol. 38(C), pages 64-76.
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    38. Djennad, Abdelmajid & Rigby, Robert & Stasinopoulos, Dimitrios & Voudouris, Vlasios & Eilers, Paul, 2015. "Beyond location and dispersion models: The Generalized Structural Time Series Model with Applications," MPRA Paper 62807, University Library of Munich, Germany.
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    1. Anatoliy Swishchuk & Aiden Huffman, 2020. "General Compound Hawkes Processes in Limit Order Books," Risks, MDPI, vol. 8(1), pages 1-25, March.
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    1. Tillmann, Peter, 2020. "Macroeconomic Surprises and the Demand for Information about Monetary Policy," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224545, Verein für Socialpolitik / German Economic Association.
    2. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
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    8. Carlos Madeira & João Madeira, 2019. "The Effect of FOMC Votes on Financial Markets," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 921-932, December.
    9. Pervaiz Alam & Xiaoling Pu & Barry Hettler & Hai Lin, 2020. "The pricing of accruals quality in credit default swap spreads," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(3), pages 1943-1977, September.
    10. Maryam Ahmadi & Niaz Bashiri Behmiri & Matteo Manera, 2020. "The theory of storage in the crude oil futures market, the role of financial conditions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1160-1175, July.
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    24. Peter Tillmann, 2023. "Macroeconomic Surprises and the Demand for Information about Monetary Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 19(2), pages 351-388, June.
    25. Marcello Pericoli & Giovanni Veronese, 2015. "Forecaster heterogeneity, surprises and financial markets," Temi di discussione (Economic working papers) 1020, Bank of Italy, Economic Research and International Relations Area.
    26. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers 11-13, University of Cologne, Centre for Financial Research (CFR).
    27. Jaehun CHOI & Hosung LIM & Rogelio Jr. MERCADO & Cyn-Young PARK, 2015. "Price Discovery and Foreign Participation in Korea's Government Bond Cash and Futures Markets," Working Papers 2015-8, Economic Research Institute, Bank of Korea.
    28. Tsionas, Mike G., 2023. "Bayesian learning in performance. Is there any?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 263-282.

  63. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit.

    Cited by:

    1. Hamza Bodor & Laurent Carlier, 2024. "A Novel Approach to Queue-Reactive Models: The Importance of Order Sizes," Papers 2405.18594, arXiv.org.
    2. Adam Ponzi & Fabrizio Lillo & Rosario N. Mantegna, 2006. "Market reaction to temporary liquidity crises and the permanent market impact," Papers physics/0608032, arXiv.org.
    3. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    4. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    5. Ingrid Lo & Stephen Sapp, 2011. "Belief Dispersion and Order Submission Strategies in the Foreign Exchange Market," Staff Working Papers 11-8, Bank of Canada.
    6. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    7. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.
    8. Katarzyna Bień-Barkowska, 2011. "Multistate asymmetric ACD model: an application to order dynamics in the EUR/PLN spot market," NBP Working Papers 104, Narodowy Bank Polski.
    9. Lo, Ingrid & Sapp, Stephen G., 2010. "Order aggressiveness and quantity: How are they determined in a limit order market?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(3), pages 213-237, July.
    10. Ellul, Andrew & Holden, Craig W. & Jain, Pankaj & Jennings, Robert, 2007. "Order dynamics: Recent evidence from the NYSE," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 636-661, December.
    11. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "Predicting bid-ask spreads using long memory autoregressive conditional poisson models," SFB 649 Discussion Papers 2011-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    13. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    14. Ming-Chang Wang & Lon-Ping Zu & Chau-Jung Kuo, 2010. "Risk aversion, order strategy and price formation," Applied Economics, Taylor & Francis Journals, vol. 42(5), pages 627-640.
    15. Johannes Prix & Otto Loistl & Michael Huetl, 2007. "Algorithmic Trading Patterns in Xetra Orders," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 717-739.
    16. Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
    17. Z. Sun & P. A. Hamill & Y. Li & Y. C. Yang & S. A. Vigne, 2019. "Did long-memory of liquidity signal the European sovereign debt crisis?," Annals of Operations Research, Springer, vol. 282(1), pages 355-377, November.
    18. Valenzuela, Marcela & Zer, Ilknur, 2013. "Competition, signaling and non-walking through the book: Effects on order choice," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5421-5435.
    19. Duong, Huu Nhan & Kalev, Petko S., 2013. "Anonymity and order submissions," Pacific-Basin Finance Journal, Elsevier, vol. 25(C), pages 101-118.
    20. Alexis Stenfors & Masayuki Susai, 2017. "Algorithmic Trading Behaviour and High-Frequency Liquidity Withdrawal in the FX Spot Market," Working Papers in Economics & Finance 2017-04, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    21. Kohler, Alexander & von Wyss, Rico, 2012. "Where does Information Processing in a Fragmented Market Take Place? – Evidence from the Swiss Stock Market after MiFID," Working Papers on Finance 1209, University of St. Gallen, School of Finance.
    22. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    23. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    24. Anh Tu Le & Thai-Ha Le & Wai-Man Liu & Kingsley Y. Fong, 2021. "Dynamic limit order placement strategies: survival analysis with a multiple-spell duration model," Annals of Operations Research, Springer, vol. 297(1), pages 241-275, February.
    25. Sperl, Miriam, 2008. "Quantifying the efficiency of the Xetra LOB market: Detailed recipe," CFS Working Paper Series 2008/21, Center for Financial Studies (CFS).
    26. Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical distributions of Chinese stock returns at different microscopic timescales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 495-502.
    27. Hautsch, Nikolaus & Huang, Ruihong, 2012. "On the dark side of the market: Identifying and analyzing hidden order placements," CFS Working Paper Series 2012/04, Center for Financial Studies (CFS).
    28. Gautam, Anil & Lepone, Grace, 2025. "Behind in time, behind in the game – time zone affects trading aggressiveness," International Review of Financial Analysis, Elsevier, vol. 97(C).
    29. Song, Song & Härdle, Wolfgang Karl & Ritov, Ya'acov, 2010. "High dimensional nonstationary time series modelling with generalized dynamic semiparametric factor model," SFB 649 Discussion Papers 2010-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    30. Stenfors, Alexis & Susai, Masayuki, 2019. "Liquidity withdrawal in the FX spot market: A cross-country study using high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 36-57.
    31. Grammig, Joachim G. & Theissen, Erik & Wünsche, Oliver, 2011. "Time and the price impact of a trade: A structural approach," CFS Working Paper Series 2011/08, Center for Financial Studies (CFS).
    32. Cebiroglu, Gökhan & Hautsch, Nikolaus & Horst, Ulrich, 2014. "Order exposure and liquidity coordination: Does hidden liquidity harm price efficiency?," CFS Working Paper Series 468, Center for Financial Studies (CFS).
    33. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    34. Yamamoto, Ryuichi, 2014. "An empirical analysis of non-execution and picking-off risks on the Tokyo Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 369-383.
    35. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    36. Chou, Robin K. & Wang, Yun-Yi, 2011. "A test of the different implications of the overconfidence and disposition hypotheses," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 2037-2046, August.
    37. Lien, Donald & Hung, Pi-Hsia & Lo, Hsiang-Yu, 2022. "Order Choices: An Intraday Analysis of the Taiwan Stock Exchange," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    38. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    39. Wei Cui & Anthony Brabazon & Michael O'Neill, 2011. "Dynamic trade execution: a grammatical evolution approach," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 4-31.
    40. Katarzyna Bień-Barkowska, 2014. "Capturing Order Book Dynamics in the Interbank EUR/PLN Spot Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(1), pages 93-117, January.
    41. Comerton-Forde, Carole & Tang, Kar Mei, 2009. "Anonymity, liquidity and fragmentation," Journal of Financial Markets, Elsevier, vol. 12(3), pages 337-367, August.
    42. Duong, Huu Nhan & Kalev, Petko S. & Krishnamurti, Chandrasekhar, 2009. "Order aggressiveness of institutional and individual investors," Pacific-Basin Finance Journal, Elsevier, vol. 17(5), pages 533-546, November.
    43. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2008. "Order Placement Strategies In A Pure Limit Order Book Market," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 31(2), pages 113-140, June.
    44. Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.
    45. Smales, Lee A., 2016. "Order aggressiveness of different broker-types in response to monetary policy news," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 367-383.
    46. Yamamoto, Ryuichi, 2020. "Limit order submission risks, order choice, and tick size," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    47. Hall, Anthony D. & Hautsch, Nikolaus, 2007. "Modelling the buy and sell intensity in a limit order book market," Journal of Financial Markets, Elsevier, vol. 10(3), pages 249-286, August.
    48. Kehrle, Kerstin & Peter, Franziska J., 2013. "Who moves first? An intensity-based measure for information flows across stock exchanges," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1629-1642.
    49. Song Song & Wolfgang K. Härdle & Ya'acov Ritov, 2014. "Generalized dynamic semi‐parametric factor models for high‐dimensional non‐stationary time series," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 101-131, June.
    50. Giampaoli, Iacopo & Ng, Wing Lon & Constantinou, Nick, 2009. "Analysis of ultra-high-frequency financial data using advanced Fourier transforms," Finance Research Letters, Elsevier, vol. 6(1), pages 47-53, March.
    51. Cumhur Ekinci, 2005. "Limit Order Book Reconstruction And Beyond: An Application To Istanbul Stock Exchange," Finance 0510025, University Library of Munich, Germany, revised 24 Oct 2005.

  64. Anthony D. Hall & Nikolaus Hautsch, 2004. "A Continuous-Time Measurement of the Buy-Sell Pressure in a Limit Order Book Market," Discussion Papers 04-07, University of Copenhagen. Department of Economics.

    Cited by:

    1. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.
    2. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
    3. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit.
    4. Asani Sarkar & Robert A. Schwartz, 2009. "Market Sidedness: Insights into Motives for Trade Initiation," Journal of Finance, American Finance Association, vol. 64(1), pages 375-423, February.
    5. Asani Sarkar & Robert A. Schwartz, 2006. "Two-sided markets and intertemporal trade clustering: insights into trading motives," Staff Reports 246, Federal Reserve Bank of New York.
    6. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    7. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2008. "Order Placement Strategies In A Pure Limit Order Book Market," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 31(2), pages 113-140, June.
    8. Voev, Valeri, 2006. "A trade-by-trade surprise measure and its relation to observed spreads on the NYSE," CoFE Discussion Papers 06/03, University of Konstanz, Center of Finance and Econometrics (CoFE).
    9. Andrea Consiglio & Valerio Lacagnina & Annalisa Russino, 2005. "A simulation analysis of the microstructure of an order driven financial market with multiple securities and portfolio choices," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 71-87.

  65. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Roman Liesenfeld & Jean-Francois Richard, 2006. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 335-360.
    2. Ban Zheng & François Roueff & Frédéric Abergel, 2014. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Post-Print hal-00777941, HAL.
    3. Siem Jan Koopman & André Lucas & André Monteiro, 2005. "The Multi-State Latent Factor Intensity Model for Credit Rating Transitions," Tinbergen Institute Discussion Papers 05-071/4, Tinbergen Institute, revised 04 Jul 2005.
    4. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.
    5. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
    6. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit.
    7. GRAMMIG, Joachim & HEINEN, Andréas & RENGIFO, Erick, 2004. "Trading activity and liquidity supply in a pure limit order book market," LIDAM Discussion Papers CORE 2004058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Omar Euch & Masaaki Fukasawa & Mathieu Rosenbaum, 2018. "The microstructural foundations of leverage effect and rough volatility," Finance and Stochastics, Springer, vol. 22(2), pages 241-280, April.
    9. Large, Jeremy, 2007. "Measuring the resiliency of an electronic limit order book," Journal of Financial Markets, Elsevier, vol. 10(1), pages 1-25, February.
    10. Gilles Zumbach, 2015. "Cross-sectional universalities in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1901-1912, December.
    11. Jean-Francois Richard & Roman Liesenfeld, 2007. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Working Paper 322, Department of Economics, University of Pittsburgh, revised Jan 2004.
    12. Grammig, Joachin & Heinen, Andreas & Rengifo, Erick, 2004. "Trading activity and liquidity supply in a pure limit order book market: An empirical analysis using a multivariate count data model," MPRA Paper 8115, University Library of Munich, Germany.
    13. Liesenfeld, Roman & Richard, Jean-François, 2006. "Improving MCMC Using Efficient Importance Sampling," Economics Working Papers 2006-05, Christian-Albrechts-University of Kiel, Department of Economics.
    14. Ban Zheng & Franc{c}ois Roueff & Fr'ed'eric Abergel, 2013. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Papers 1301.5007, arXiv.org, revised Feb 2014.
    15. Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," Umeå Economic Studies 657, Umeå University, Department of Economics.

  66. Hautsch, Nikolaus & Hess, Dieter, 2002. "The processing of non-anticipated information in financial markets: Analyzing the impact of surprises in the employment report," CoFE Discussion Papers 02/06, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Grothe, Magdalena, 2010. "Price and trading response to public information," Working Paper Series 1177, European Central Bank.
    2. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    3. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    4. Steiner, Christian & Groß, Anne & Entorf, Horst, 2009. "Return and Volatility Reactions to Monthly Announcements of Business Cycle Forecasts: An Event Study Based on High-Frequency Data," ZEW Discussion Papers 09-010, ZEW - Leibniz Centre for European Economic Research.
    5. Entorf Horst & Steiner Christian, 2007. "Makroökonomische Nachrichten und die Reaktion des 15-Sekunden-DAX: Eine Ereignisstudie zur Wirkung der ZEW-Konjunkturprognose / Announcement of Business Cycle Forecasts and the Reaction of the German Stock Market," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(1), pages 3-26, February.
    6. Neeraj J. Gupta & Vitaliy Strohush & Reilly White, 2019. "Investor reaction to simultaneous news releases: unemployment vs. earnings," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(4), pages 735-749, October.
    7. Hautsch, Nikolaus & Hess, Dieter, 2004. "Bayesian learning in financial markets: Testing for the relevance of information precision in price discovery," CFR Working Papers 04-10, University of Cologne, Centre for Financial Research (CFR).
    8. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," SFB 649 Discussion Papers 2009-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Gropp, Reint & Kadareja, Arjan, 2006. "Stale information, shocks and volatility," Working Paper Series 686, European Central Bank.
    10. Hautsch, Nikolaus & Hess, Dieter & Müller, Christoph, 2012. "Price adjustment to news with uncertain precision," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 337-355.
    11. Jordi Mondria & Thomas Wu, 2012. "Familiarity and Surprises in International Financial Markets: Bad news travels like wildfire, good news travels slow," 2012 Meeting Papers 50, Society for Economic Dynamics.
    12. Jérôme Coffinet & Sylvain Gouteron, 2010. "Euro‐Area Yield Curve Reaction to Monetary News," German Economic Review, Verein für Socialpolitik, vol. 11(2), pages 208-224, May.
    13. James Raad & Abhinav Sharma & Juan L Nicolau, 2022. "Will your majesty marry me? The effect of royal weddings on the tourism industry," Tourism Economics, , vol. 28(6), pages 1589-1604, September.
    14. Entorf, Horst & Steiner, Christian, 2006. "Makroökonomische Nachrichten und die Reaktion des 15-Sekunden-DAX: Eine Ereignisstudie zur Wirkung der ZEW-Konjunkturprognose," Darmstadt Discussion Papers in Economics 159, Darmstadt University of Technology, Department of Law and Economics.
    15. Mondria, Jordi & Wang, Xin & Wu, Thomas, 2021. "Familiarity and Surprises in International Financial Markets: Bad news travels like wildfire; good news travels slow," Journal of International Money and Finance, Elsevier, vol. 115(C).
    16. Chiou-Wei, Song-Zan & Chen, Sheng-Hung & Zhu, Zhen, 2020. "Natural gas price, market fundamentals and hedging effectiveness," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 321-337.
    17. Sylwia Nowak, 2008. "How Do Public Announcements Affect the Frequency of Trading in U.S. Airline Stocks?," CAMA Working Papers 2008-38, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Chiou-Wei, Song-Zan & Linn, Scott C. & Zhu, Zhen, 2014. "The response of U.S. natural gas futures and spot prices to storage change surprises: Fundamental information and the effect of escalating physical gas production," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 156-173.
    19. Cecilia Frale & David Veredas, 2008. "A Monthly Volatility Index for the US Economy," Working Papers ECARES 2008-008, ULB -- Universite Libre de Bruxelles.

  67. Hautsch, Nikolaus, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Papers 02/05, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.
    2. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    3. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    4. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    5. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    6. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.

  68. Hautsch, Nikolaus & Hess, Dieter E., 2001. "A mean variance king? Creation and resolution of uncertainty under the employment report's reign," ZEW Discussion Papers 01-60, ZEW - Leibniz Centre for European Economic Research.

    Cited by:

    1. Savaser, Tanseli, 2011. "Exchange rate response to macronews: Through the lens of microstructure," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 107-126, February.

  69. Hautsch, Nikolaus & Klotz, Stefan, 2001. "Estimating the Neighborhood Influence on Decision Makers: Theory and an Application on the Analysis of Innovation Decisions," CoFE Discussion Papers 01/04, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Fedorets, Alexandra & Stops, Michael & Lottmann, Franziska, 2017. "Job matching on connected regional and occupational labor markets," IAB-Discussion Paper 201735, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Johannes Rincke, 2005. "Neighborhood Influence and Political Change: Evidence from US School Districts," Public Economics 0511011, University Library of Munich, Germany.
    3. Schaffer, Axel & Düvelmeyer, Claudia, 2016. "Regional drivers of on-farm energy production in Bavaria," Energy Policy, Elsevier, vol. 95(C), pages 361-369.
    4. Edoardo Di Porto & Federico Revelli, 2009. "Central command, local hazard and the race to the top," Working Papers 2009/26, Institut d'Economia de Barcelona (IEB).
    5. Tomohiko Konno & Yannis M. Ioannides, 2018. "The Exact Solution of Spatial Logit Response Games," Discussion Papers Series, Department of Economics, Tufts University 0827, Department of Economics, Tufts University.
    6. Buettner, Thiess & von Schwerin, Axel, 2016. "Yardstick competition and partial coordination: Exploring the empirical distribution of local business tax rates," Journal of Economic Behavior & Organization, Elsevier, vol. 124(C), pages 178-201.
    7. Zhu, Fengxia & Zou, Shaoming & Xu, Hui, 2017. "Launching reverse-innovated product from emerging markets to MNC’s home market: A theoretical framework for MNC’s decisions," International Business Review, Elsevier, vol. 26(1), pages 156-163.
    8. Rincke, Johannes, 2005. "Policy Innovation in Local Jurisdictions: Testing the Neighborhood Influence Against the Free-Riding Hypothesis," ZEW Discussion Papers 05-08, ZEW - Leibniz Centre for European Economic Research.
    9. Johannes Rincke, 2005. "Policy Innovation in Local Jurisdictions: Testing the Neighborhood Influence Against the Free-Riding Hypothesis," Public Economics 0511017, University Library of Munich, Germany.
    10. Kihiu, Evelyne Nyathira, 2016. "Basic capability effect: Collective management of pastoral resources in southwestern Kenya," Ecological Economics, Elsevier, vol. 123(C), pages 23-34.
    11. Rincke, Johannes, 2005. "Neighborhood Influence and Political Change: Evidence from US School Districts," ZEW Discussion Papers 05-16, ZEW - Leibniz Centre for European Economic Research.
    12. Johannes Rincke, 2006. "Policy innovation in local jurisdictions: Testing for neighborhood influence in school choice policies," Public Choice, Springer, vol. 129(1), pages 189-200, October.
    13. Johannes Rincke, 2005. "Policy Innovation in Local Jurisdictions: Testing the Neighborhood Influence Against the Free-Riding Hypothesis," Public Economics 0511009, University Library of Munich, Germany.
    14. Elzbieta Antczak & Ewa Galecka-Burdziak & Robert Pater, 2016. "Spatial Labour Market Matching," CERGE-EI Working Papers wp578, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  70. Hautsch, Nikolaus & Lehmann, Erik & Warning, Susanne & Frick, Bernd, 2001. "Shirking or mismatch? Coach-team separation in German professional soccer," Discussion Papers, Series I 313, University of Konstanz, Department of Economics.

    Cited by:

    1. B Frick & R Simmons, 2005. "The impact of managerial quality on organizational performance: evidence from German soccer," Working Papers 566927, Lancaster University Management School, Economics Department.
    2. Claudio Lucifora & Rob Simmons, 2003. "Superstar Effects in Sport," Journal of Sports Economics, , vol. 4(1), pages 35-55, February.
    3. Bernd Frick, 2007. "The Football Players' Labor Market: Empirical Evidence From The Major European Leagues," Scottish Journal of Political Economy, Scottish Economic Society, vol. 54(3), pages 422-446, July.

  71. Hautsch, Nikolaus & Pohlmeier, Winfried, 2001. "Econometric Analysis of Financial Transaction Data: Pitfalls and Opportunities," CoFE Discussion Papers 01/05, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Daniel Kvíčala & Maria Králová & Petr Suchánek, 2026. "The impact of online purchase behaviour on customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 14(1), pages 120-137, March.

  72. Gerhard, Frank & Hautsch, Nikolaus, 2000. "Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model," CoFE Discussion Papers 00/20, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Iordanis Kalaitzoglou & Boulis Maher Ibrahim, 2010. "Does Order Flow in the European Carbon Allowances Market Reveal Information?," CFI Discussion Papers 1003, Centre for Finance and Investment, Heriot Watt University.
    2. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    3. Kalaitzoglou, Iordanis & Ibrahim, Boulis M., 2013. "Does order flow in the European Carbon Futures Market reveal information?," Journal of Financial Markets, Elsevier, vol. 16(3), pages 604-635.

  73. Frank Gerhard & Nikolaus Hautsch, 2000. "Determinants of Inter-Trade Durations Using Proportional Hazard ARMA Models," Econometric Society World Congress 2000 Contributed Papers 1082, Econometric Society.

    Cited by:

    1. Iordanis Kalaitzoglou & Boulis Maher Ibrahim, 2010. "Does Order Flow in the European Carbon Allowances Market Reveal Information?," CFI Discussion Papers 1003, Centre for Finance and Investment, Heriot Watt University.
    2. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    3. Kalaitzoglou, Iordanis & Ibrahim, Boulis M., 2013. "Does order flow in the European Carbon Futures Market reveal information?," Journal of Financial Markets, Elsevier, vol. 16(3), pages 604-635.

  74. Nikolaus Hautsch, 1999. "Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions," Finance 9904002, University Library of Munich, Germany.

    Cited by:

    1. Hautsch, Nikolaus, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Papers 02/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    2. Gerhard, Frank & Hautsch, Nikolaus, 2000. "Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model," CoFE Discussion Papers 00/20, University of Konstanz, Center of Finance and Econometrics (CoFE).
    3. P. Gagliardini & C. Gourieroux, 2008. "Duration time‐series models with proportional hazard," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 74-124, January.
    4. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    5. Bidisha Chakrabarty & Zhaohui Han & Konstantin Tyurin & Xiaoyong Zheng, 2006. "A Competing Risk Analysis of Executions and Cancellations in a Limit Order Market," CAEPR Working Papers 2006-015, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

  75. Gerhard, Frank & Hautsch, Nikolaus, 1999. "Volatility Estimation on the Basis of Price Intensities," CoFE Discussion Papers 99/19, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    2. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.
    3. Fei Wang & Wei Chao, 2018. "A New Perspective on Improving Hospital Energy Administration Based on Recurrence Interval Analysis," Energies, MDPI, vol. 11(5), pages 1-18, May.
    4. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    5. Veredas, David & Rodríguez Poo, Juan M. & Espasa, Antoni, 2001. "On the (intradaily) seasonality and dynamics of a financial point process: a semiparametric approach," DES - Working Papers. Statistics and Econometrics. WS ws013321, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    7. Chen, Kim Heng & Jandhyala, Venkata K. & Fotopoulos, Stergios B., 2005. "Nonlinear Properties of Multifactor Financial Models," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 1(2), pages 1-27.
    8. Vinicius Ratton Brandi, 2020. "Short-Term Predictability of Stock Market Indexes following Large Drawdowns and Drawups," Working Papers Series 529, Central Bank of Brazil, Research Department.
    9. Maillet, Bertrand & Michel, Thierry, 2002. "How deep was the September 2001 stock market crisis? putting recent events on the American and French markets into perspective with an index of market shocks," LSE Research Online Documents on Economics 24936, London School of Economics and Political Science, LSE Library.
    10. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    11. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    12. Thierry Chauveau & Sylvain Friederich & Jérôme Héricourt & Emmanuel Jurczenko & Catherine Lubochinsky & Bertrand Maillet & Christophe Moussu & Bogdan Négréa & Hélène Raymond-Feingold, 2004. "La volatilité des marchés augmente-t-elle ?," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 17-44.
    13. Li, Qiyuan & Li, Yifan & Nolte, Ingmar & Nolte, Sandra & Yu, Shifan, 2026. "Testing for jumps in a discretely observed price process with endogenous sampling times," Journal of Econometrics, Elsevier, vol. 254(PA).
    14. Fotopoulos, Stergios B. & Jandhyala, Venkata K. & Chen, Kim-Heng, 2007. "Non-linear properties of conditional returns under scale mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3041-3056, March.
    15. Song, Shijia & Li, Handong, 2023. "Is a co-jump in prices a sparse jump?," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    16. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, May.

  76. Frank Gerhard & Nikolaus Hautsch, "undated". "Semiparametric autoregressive conditional proportional hazard models," Economics Papers 2002-W2, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Monteiro, André A., 2009. "The econometrics of randomly spaced financial data: a survey," DES - Working Papers. Statistics and Econometrics. WS ws097924, Universidad Carlos III de Madrid. Departamento de Estadística.

Articles

  1. Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad‐Díaz & Menachem (Meni) Abudy , 2024. "Nonstandard Errors," Journal of Finance, American Finance Association, vol. 79(3), pages 2339-2390, June.
    • Utz Weitzel & Michael Razen & Sebastian Neussüs & Michael Kirchler & Magnus Johannesson & Juergen Huber & Felix Holzmeister & Anna Dreber & Albert J. Menkveld & Javier Gil-Bazo, 2021. "Non-Standard Errors," Working Papers 1303, Barcelona School of Economics.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," IWH Discussion Papers 11/2021, Halle Institute for Economic Research (IWH).
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neussüs & Michael Razen & Utz Weitzel & Christian T. Brownlees & Javier Gil-Baz, 2021. "Non-standard errors," Economics Working Papers 1807, Department of Economics and Business, Universitat Pompeu Fabra.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Mena, 2021. "Non-Standard Errors," Working Papers 2021:17, Lund University, Department of Economics.
    • Albert J. et al. Menkveld, 2021. "Non-Standard Errors," CESifo Working Paper Series 9453, CESifo.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Post-Print halshs-03500882, HAL.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neus ss & Michael Razen & Utz Weitzel & Edwin Baidoo & Michael Fr mmel & et al, 2021. "Non-Standard Errors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1032, Ghent University, Faculty of Economics and Business Administration.
    • Francesco Franzoni & Roxana Mihet & Markus Leippold & Per Ostberg & Olivier Scaillet & Norman Schürhoff & Oksana Bashchenko & Nicola Mano & Michele Pelli, 2022. "Non-Standard Errors," Swiss Finance Institute Research Paper Series 22-09, Swiss Finance Institute.
    • Menkveld, A. & Dreber, A. & Holzmeister, F. & Huber, J. & Johannesson, M. & Kirchler, M. & Neusüss, S. & Razen, M. & Neusüss, S. & Neusüss, S., 2021. "Non-Standard Errors," Cambridge Working Papers in Economics 2182, Faculty of Economics, University of Cambridge.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Hasse, Jean-Baptiste & e.a.,, 2023. "Non-Standard Errors," LIDAM Reprints LFIN 2023002, Université catholique de Louvain, Louvain Finance (LFIN).
    • Menkveld, Albert J. & Dreber, Anna & Declerck, Fany & Moinas, Sophie, 2023. "Non-Standard Errors," TSE Working Papers 23-1451, Toulouse School of Economics (TSE).
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüß, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Menac, 2024. "Nonstandard errors," LSE Research Online Documents on Economics 123002, London School of Economics and Political Science, LSE Library.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-04676112, HAL.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," SAFE Working Paper Series 327, Leibniz Institute for Financial Research SAFE.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & David Abad-Dí­az & Menachem Abudy & Tobi, 2021. "Non-Standard Errors," Working Papers 2021-31, Faculty of Economics and Statistics, Universität Innsbruck.
    • Gerardo Ferrara & Simon Jurkatis, 2021. "Non-standard errors," Bank of England Staff Working Paper series 955, Bank of England.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-05077550, HAL.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03500882, HAL.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05077550, HAL.
    • Ciril Bosch-Rosa & Bernhard Kassner, 2023. "Non-Standard Errors," Rationality and Competition Discussion Paper Series 385, CRC TRR 190 Rationality and Competition.
    • Wolff, Christian & Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüess, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-Standard Errors," CEPR Discussion Papers 16751, Centre for Economic Policy Research.
    • Albert J. Menkveld & Anna Dreber & Félix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard, 2021. "Non-Standard Errors," Documents de travail du Centre d'Economie de la Sorbonne 21033, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    See citations under working paper version above.
  2. Nikolaus Hautsch & Christoph Scheu & Stefan Voigt, 2024. "Building trust takes time: limits to arbitrage for blockchain-based assets," Review of Finance, European Finance Association, vol. 28(4), pages 1345-1381.
    See citations under working paper version above.
  3. Nikolaus Hautsch & Ostap Okhrin & Alexander Ristig, 2023. "Maximum-Likelihood Estimation Using the Zig-Zag Algorithm," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1346-1375.

    Cited by:

    1. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org, revised Sep 2025.

  4. Andersen, Torben G. & Archakov, Ilya & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2022. "Local mispricing and microstructural noise: A parametric perspective," Journal of Econometrics, Elsevier, vol. 230(2), pages 510-534.

    Cited by:

    1. Choi, Youngmin & Lee, Suzanne S., 2025. "On the efficiency contributions of analyst recommendations to financial markets," Journal of Financial Markets, Elsevier, vol. 75(C).
    2. Li, Z. Merrick & Yang, Xiye, 2026. "Multi-horizon test for market frictions," Journal of Econometrics, Elsevier, vol. 253(C).
    3. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    4. Flora, Maria & Renò, Roberto, 2025. "V-shapes," Journal of Banking & Finance, Elsevier, vol. 179(C).
    5. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org, revised Aug 2025.
    6. Queiroz, R.G.S. & Kristoufek, L. & David, S.A., 2024. "A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 652(C).
    7. Laurent, Sébastien & Renò, Roberto & Shi, Shuping, 2026. "Realized drift," Journal of Econometrics, Elsevier, vol. 254(PA).

  5. Torben Andersen & Ilya Archakov & Leon Grund & Nikolaus Hautsch & Yifan Li & Sergey Nasekin & Ingmar Nolte & Manh Cuong Pham & Stephen Taylor & Viktor Todorov, 2021. "A Descriptive Study of High-Frequency Trade and Quote Option Data [Stealth Trading in Options Markets]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 128-177.

    Cited by:

    1. Fengler, Matthias & Koeniger, Winfried & Minger, Stephan, 2025. "The Transmission of Monetary Policy to the Cost of Hedging," Economics Working Paper Series 2501, University of St. Gallen, School of Economics and Political Science.
    2. Li, Yifan & Nolte, Ingmar & Pham, Manh Cuong, 2024. "Parametric risk-neutral density estimation via finite lognormal-Weibull mixtures," Journal of Econometrics, Elsevier, vol. 241(2).
    3. Amir Ahmad Dar & Mohammad Shahfaraz Khan & Imran Azad & Amit Kumar Pathak & Gopu Jayaraman, 2024. "Literature review: options and its applications," SN Business & Economics, Springer, vol. 4(8), pages 1-26, August.
    4. Kanne, Stefan & Korn, Olaf & Uhrig-Homburg, Marliese, 2023. "Stock illiquidity and option returns," Journal of Financial Markets, Elsevier, vol. 63(C).

  6. Nikolaus Hautsch & Rodrigo Herrera, 2020. "Multivariate dynamic intensity peaks‐over‐threshold models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 248-272, March.
    See citations under working paper version above.
  7. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2019. "Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 419-435, July.
    See citations under working paper version above.
  8. Hautsch, Nikolaus & Horvath, Akos, 2019. "How effective are trading pauses?," Journal of Financial Economics, Elsevier, vol. 131(2), pages 378-403.
    See citations under working paper version above.
  9. Hautsch, Nikolaus & Voigt, Stefan, 2019. "Large-scale portfolio allocation under transaction costs and model uncertainty," Journal of Econometrics, Elsevier, vol. 212(1), pages 221-240.
    See citations under working paper version above.
  10. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2016. "Systemic risk spillovers in the European banking and sovereign network," Journal of Financial Stability, Elsevier, vol. 25(C), pages 206-224.
    See citations under working paper version above.
  11. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.

    Cited by:

    1. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula-based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics Working Papers Archive 2017_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    2. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    3. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    4. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    5. Nikolaus Hautsch & Ostap Okhrin & Alexander Ristig, 2023. "Maximum-Likelihood Estimation Using the Zig-Zag Algorithm," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1346-1375.
    6. Gu, Huaying & Liu, Zhixue & Weng, Yingliang, 2017. "Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 460-472.

  12. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
    See citations under working paper version above.
  13. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    See citations under working paper version above.
  14. Wolfgang K. Härdle & Nikolaus Hautsch & Andrija Mihoci, 2015. "Local Adaptive Multiplicative Error Models for High‐Frequency Forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 529-550, June.
    See citations under working paper version above.
  15. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2014. "Forecasting systemic impact in financial networks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 781-794.
    See citations under working paper version above.
  16. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2013. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121, December.
    See citations under working paper version above.
  17. Axel Groß‐KlußMann & Nikolaus Hautsch, 2013. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(8), pages 724-742, December.
    See citations under working paper version above.
  18. Nikolaus Hautsch & Mark Podolskij, 2013. "Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 165-183, April.
    See citations under working paper version above.
  19. Hautsch, Nikolaus & Hess, Dieter & Müller, Christoph, 2012. "Price adjustment to news with uncertain precision," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 337-355.
    See citations under working paper version above.
  20. Nikolaus Hautsch & Lada M. Kyj & Roel C. A. Oomen, 2012. "A blocking and regularization approach to high‐dimensional realized covariance estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 625-645, June.
    See citations under working paper version above.
  21. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    See citations under working paper version above.
  22. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    See citations under working paper version above.
  23. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
    See citations under working paper version above.
  24. Hautsch, Nikolaus & Huang, Ruihong, 2012. "The market impact of a limit order," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 501-522.
    See citations under working paper version above.
  25. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.

    Cited by:

    1. Adam Clements & Neda Todorova, 2014. "The impact of information flow and trading activity on gold and oil futures volatility," NCER Working Paper Series 102, National Centre for Econometric Research.
    2. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," IJFS, MDPI, vol. 1(4), pages 1-14, November.
    3. Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
    4. Jianfei Zhang & Mathieu Rosenbaum, 2023. "Towards systematic intraday news screening: a liquidity-focused approach," Papers 2304.05115, arXiv.org.
    5. Justina Deveikyte & Helyette Geman & Carlo Piccari & Alessandro Provetti, 2020. "A Sentiment Analysis Approach to the Prediction of Market Volatility," Papers 2012.05906, arXiv.org.
    6. Massa, Massimo & von Beschwitz, Bastian & Keim, Donald B, 2015. "First to ?Read? the News: News Analytics and Institutional Trading," CEPR Discussion Papers 10534, Centre for Economic Policy Research.
    7. Gupta, Kartick & Banerjee, Rajabrata, 2019. "Does OPEC news sentiment influence stock returns of energy firms in the United States?," Energy Economics, Elsevier, vol. 77(C), pages 34-45.
    8. Monika Bolek & Cezary Bolek, 2024. "Covid-19 Data Manipulation and Reaction of Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(1), pages 137-164, March.
    9. Apergis, Nicholas, 2015. "Forecasting Credit Default Swaps (CDSs) spreads with newswire messages: Evidence from European countries under financial distress," Economics Letters, Elsevier, vol. 136(C), pages 92-94.
    10. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    11. Bastian von Beschwitz & Donald B Keim & Massimo Massa, 2020. "First to “Read” the News: News Analytics and Algorithmic Trading," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(1), pages 122-178.
    12. Gao, Yang & Wang, Yaojun & Wang, Chao & Liu, Chao, 2018. "Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 802-811.
    13. Allen, D.E. & McAleer, M.J. & Singh, A.K., 2015. "Daily Market News Sentiment and Stock Prices," Econometric Institute Research Papers EI2015-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Social media bots and stock markets," Working Papers 2018-30, Swansea University, School of Management.
    15. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
    16. Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020. "Distillation of News Flow into Analysis of Stock Reactions," Papers 2009.10392, arXiv.org.
    17. Carlini, Federico & Farina, Vincenzo & Gufler, Ivan & Previtali, Daniele, 2024. "Do stress and overstatement in the news affect the stock market? Evidence from COVID-19 news in The Wall Street Journal," International Review of Financial Analysis, Elsevier, vol. 93(C).
    18. Daniel Martin Katz & Michael J Bommarito II & Tyler Soellinger & James Ming Chen, 2015. "Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making," Papers 1508.05751, arXiv.org, revised May 2017.
    19. Fabrizio Lillo & Salvatore Miccich`e & Michele Tumminello & Jyrki Piilo & Rosario Nunzio Mantegna, 2012. "How news affect the trading behavior of different categories of investors in a financial market," Papers 1207.3300, arXiv.org.
    20. Ignacio Arango & Diego A. Agudelo, 2017. "How does information disclosure affect liquidity?Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16990, Universidad EAFIT.
    21. Chinmoy Ghosh & Cristian Pinto‐Gutiérrez & Jaideep Shenoy, 2024. "Does negative news disclosure induce better decision‐making? Evidence from acquisitions," The Financial Review, Eastern Finance Association, vol. 59(2), pages 325-372, May.
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    29. Francisco Jareño & Ana Escribano & Zaghum Umar, 2023. "The impact of the COVID-19 outbreak on the connectedness of the BRICS’s term structure," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
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    41. Smales, Lee A., 2016. "News sentiment and bank credit risk," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 37-61.
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    44. Yuna Hao & Behrang Vand & Benjamin Manrique Delgado & Simone Baldi, 2023. "Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges," Energies, MDPI, vol. 16(4), pages 1-28, February.
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    48. Allen, D.E. & McAleer, M.J. & Singh, A.K., 2016. "An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Econometric Institute Research Papers EI2016-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    54. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
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    57. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Documentos de Trabajo del ICAE 2014-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    58. Pablo D. Azar & Sergio Olivas & Nish Sinha, 2025. "The Price of Processing: Information Frictions and Market Efficiency in DeFi," Staff Reports 1153, Federal Reserve Bank of New York.
    59. Diego A. Agudelo & Ignacio Arango, 2017. "How does information disclosure affect liquidity? Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16944, Universidad EAFIT.
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    62. Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
    63. Füss, Roland & Grabellus, Markus & Mager, Ferdinand & Stein, Michael, 2018. "Something in the air: Information density, news surprises, and price jumps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 50-75.
    64. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
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    67. Akihiro Omura & Neda Todorova, 2019. "The quantile dependence of commodity futures markets on news sentiment," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 818-837, July.
    68. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," CARF F-Series CARF-F-366, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    69. Fengler, Matthias & Polivka, Jeannine, 2021. "Proxy-identification of a structural MGARCH model for asset returns," Economics Working Paper Series 2103, University of St. Gallen, School of Economics and Political Science, revised Oct 2024.
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    71. Rognone, Lavinia & Hyde, Stuart & Zhang, S. Sarah, 2020. "News sentiment in the cryptocurrency market: An empirical comparison with Forex," International Review of Financial Analysis, Elsevier, vol. 69(C).
    72. Liu, Jun, 2026. "Stock Liquidity in the Context of Asset Pricing," Other publications TiSEM 5b29c7cf-6d49-44f8-92d6-3, Tilburg University, School of Economics and Management.
    73. Smales, Lee A., 2014. "News sentiment and the investor fear gauge," Finance Research Letters, Elsevier, vol. 11(2), pages 122-130.
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    75. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    76. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    77. Gillam, Robert A. & Guerard, John B. & Cahan, Rochester, 2015. "News volume information: Beyond earnings forecasting in a global stock selection model," International Journal of Forecasting, Elsevier, vol. 31(2), pages 575-581.
    78. Apergis, Nicholas, 2015. "Newswire messages and sovereign credit ratings: Evidence from European countries under austerity reform programmes," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 54-62.
    79. Bianconi, Marcelo & Hua, Xiaxin & Tan, Chih Ming, 2015. "Determinants of systemic risk and information dissemination," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 352-368.
    80. Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.
    81. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    82. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    83. Fengler, Matthias & Polivka, Jeanine, 2022. "Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model," VfS Annual Conference 2022 (Basel): Big Data in Economics 264010, Verein für Socialpolitik / German Economic Association.
    84. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    85. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    86. Rühl, Tobias R. & Stein, Michael, 2015. "The impact of ECB macro-announcements on bid–ask spreads of European blue chips," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 54-71.
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    88. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    89. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2016. "Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
    90. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
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    96. Zhu (Drew) Zhang & Jie Yuan & Amulya Gupta, 2024. "Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1400-1416, December.
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    99. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2016. "Common trends in global volatility," Journal of International Money and Finance, Elsevier, vol. 67(C), pages 194-214.

  26. Hautsch, Nikolaus & Hess, Dieter & Veredas, David, 2011. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2733-2746, October.
    See citations under working paper version above.
  27. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
    See citations under working paper version above.
  28. Hautsch, Nikolaus & Hess, Dieter, 2007. "Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(1), pages 189-208, March.
    See citations under working paper version above.
  29. Hall, Anthony D. & Hautsch, Nikolaus, 2007. "Modelling the buy and sell intensity in a limit order book market," Journal of Financial Markets, Elsevier, vol. 10(3), pages 249-286, August.

    Cited by:

    1. Bastian Felix & Oliver Woll & Christoph Weber, 2009. "Gas Storage Valuation Under Limited Market Liquidity: An Application In Germany," EWL Working Papers 0903, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2009.
    2. Katarzyna Bień-Barkowska, 2014. "“Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market," Bank i Kredyt, Narodowy Bank Polski, vol. 45(3), pages 197-224.
    3. Ingrid Lo & Stephen Sapp, 2011. "Belief Dispersion and Order Submission Strategies in the Foreign Exchange Market," Staff Working Papers 11-8, Bank of Canada.
    4. Foucault, Thierry & Kadan, Ohad & Kandel, Eugene, 2009. "Liquidity cycles and make/take fees in electronic markets," HEC Research Papers Series 920, HEC Paris.
    5. Lo, Ingrid & Sapp, Stephen G., 2010. "Order aggressiveness and quantity: How are they determined in a limit order market?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(3), pages 213-237, July.
    6. Roberto Pascual & David Veredas, 2010. "Does the Open Limit Order Book Matter in Explaining Informational Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 57-87, Winter.
    7. Anagnostidis, Panagiotis & Kanas, Angelos & Papachristou, George, 2015. "Information revelation in the Greek exchange opening call: Daily and intraday evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 167-184.
    8. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    9. Liu, Hsing & Liao, Chi-Yo & Ko, Jing-Yuan & Lih, Jiann-Shing, 2017. "Anchoring effect on first passage process in Taiwan financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 114-127.
    10. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    11. Jón Daníelsson & Richard Payne, 2012. "Liquidity determination in an order-driven market," The European Journal of Finance, Taylor & Francis Journals, vol. 18(9), pages 799-821, October.
    12. Ladley, Dan & Schenk-Hoppé, Klaus Reiner, 2009. "Do stylised facts of order book markets need strategic behaviour?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 817-831, April.
    13. Alexis Stenfors & Masayuki Susai, 2017. "Algorithmic Trading Behaviour and High-Frequency Liquidity Withdrawal in the FX Spot Market," Working Papers in Economics & Finance 2017-04, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    14. Alessio Sancetta, 2017. "Estimation for the Prediction of Point Processes with Many Covariates," Papers 1702.05315, arXiv.org.
    15. Kohler, Alexander & von Wyss, Rico, 2012. "Where does Information Processing in a Fragmented Market Take Place? – Evidence from the Swiss Stock Market after MiFID," Working Papers on Finance 1209, University of St. Gallen, School of Finance.
    16. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    17. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    18. Ioane Muni Toke, 2011. ""Market making" behaviour in an order book model and its impact on the bid-ask spread," Post-Print hal-01705266, HAL.
    19. Matthew C. Chang & Rebecca Chung-Fern Wu, 2013. "Informativeness and Influence of Limit Order Books on Order Submissions in Electronic Continuous Auction Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S3), pages 70-97, July.
    20. Henk Berkman & Carole Comerton‐Forde, 2011. "Market microstructure: A review from down under," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 51(1), pages 50-78, March.
    21. Będowska-Sójka, Barbara, 2020. "Do aggressive orders affect liquidity? An evidence from an emerging market," Research in International Business and Finance, Elsevier, vol. 54(C).
    22. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    23. Stenfors, Alexis & Susai, Masayuki, 2019. "Liquidity withdrawal in the FX spot market: A cross-country study using high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 36-57.
    24. Roberto Pascual & David Veredas, 2009. "What pieces of limit order book information matter in explaining order choice by patient and impatient traders?," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 527-545.
    25. Luca Mucciante & Alessio Sancetta, 2023. "Estimation of an Order Book Dependent Hawkes Process for Large Datasets," Papers 2307.09077, arXiv.org, revised May 2026.
    26. Oefele, Nico & Baur, Dirk G. & Smales, Lee A., 2025. "The effect of currency risk on crypto asset utilization in Türkiye," Emerging Markets Review, Elsevier, vol. 65(C).
    27. Ioane Muni Toke, 2010. ""Market making" behaviour in an order book model and its impact on the bid-ask spread," Papers 1003.3796, arXiv.org, revised Jun 2010.
    28. Moshirian, Fariborz & Nguyen, Huong Giang (Lily) & Pham, Peter Kien, 2012. "Overnight public information, order placement, and price discovery during the pre-opening period," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2837-2851.
    29. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    30. Miquel Montero, 2021. "Predator–prey model for stock market fluctuations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 29-57, January.
    31. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    32. Katarzyna Bień-Barkowska, 2014. "Capturing Order Book Dynamics in the Interbank EUR/PLN Spot Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(1), pages 93-117, January.
    33. Masashi Ieda, 2015. "A dynamic optimal execution strategy under stochastic price recovery," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-24, December.
    34. Tsung-Yu Hsieh, 2015. "Information disclosure and price manipulation during the pre-closing session: evidence from an order-driven market," Applied Economics, Taylor & Francis Journals, vol. 47(43), pages 4670-4684, September.
    35. Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.
    36. Hugh L. Christensen & Richard E. Turner & Simon I. Hill & Simon J. Godsill, 2013. "Rebuilding the limit order book: sequential Bayesian inference on hidden states," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1779-1799, November.
    37. Miquel Montero, 2008. "Predator-Prey Model for Stock Market Fluctuations," Papers 0810.4844, arXiv.org, revised Jul 2009.
    38. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    39. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).
    40. Yensen Ni & Yulu Liao & Paoyu Huang, 2017. "Foreign Institutional Investors, Shareholding Change, and Corporate Governance," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(4), pages 764-775, April.
    41. Masashi Ieda, 2015. "A dynamic optimal execution strategy under stochastic price recovery," Papers 1502.04521, arXiv.org.

  30. Gerhard Frank & Hautsch Nikolaus, 2007. "A Dynamic Semiparametric Proportional Hazard Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(2), pages 1-42, May.
    See citations under working paper version above.
  31. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.
    See citations under working paper version above.
  32. Anthony Hall & Nikolaus Hautsch, 2006. "Order aggressiveness and order book dynamics," Empirical Economics, Springer, vol. 30(4), pages 973-1005, January.
    See citations under working paper version above.
  33. Hautsch, Nikolaus & Klotz, Stefan, 2003. "Estimating the neighborhood influence on decision makers: theory and an application on the analysis of innovation decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 52(1), pages 97-113, September.
    See citations under working paper version above.
  34. Nikolaus Hautsch, 2003. "Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 189-215.

    Cited by:

    1. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Aneta Hryckiewicz & Piotr Mielus & Karolina Skorulska & Małgorzata Snarska, 2018. "Does a bank levy increase frictions on the interbank market?," KAE Working Papers 2018-033, Warsaw School of Economics, Collegium of Economic Analysis.
    3. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    4. Petra Tomanová & Vladimír Holý, 2021. "Clustering of arrivals in queueing systems: autoregressive conditional duration approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 859-874, September.
    5. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    6. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    7. Renault, Eric & Werker, Bas J.M., 2011. "Causality effects in return volatility measures with random times," Journal of Econometrics, Elsevier, vol. 160(1), pages 272-279, January.
    8. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    10. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    11. Hautsch, Nikolaus & Malec, Peter & Schienle, Melanie, 2010. "Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes," CFS Working Paper Series 2010/19, Center for Financial Studies (CFS).
    12. Kyungsub Lee, 2026. "Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process," Papers 2604.00346, arXiv.org.

  35. Nikolaus Hautsch & Joachim Inkmann, 2003. "Optimal hedging of the currency exchange risk exposure of dynamically balanced strategic asset allocations," Journal of Asset Management, Palgrave Macmillan, vol. 4(3), pages 173-198, September.

    Cited by:

    1. Mohini GUPTA & Purwa SRIVASTAVA & Amritkant MISHRA & Malayaranjan SAHOO, 2021. "Time-varying volatility spillover of foreign exchange rate in three Asian markets: Based on DCC-GARCH approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(629), W), pages 105-120, Winter.

  36. Nikolaus Hautsch & Dieter Hess, 2002. "The Processing of Non-Anticipated Information in Financial Markets: Analyzing the Impact of Surprises in the Employment Report," Review of Finance, European Finance Association, vol. 6(2), pages 133-161.
    See citations under working paper version above.
  37. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    See citations under working paper version above.

Chapters

  1. Luc Bauwens & Nikolaus Hautsch, 2009. "Modelling Financial High Frequency Data Using Point Processes," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 41, pages 953-979, Springer.
    See citations under working paper version above.
  2. Anthony D. Hall & Nikolaus Hautsch, 2008. "Order aggressiveness and order book dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 133-165, Springer.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

Books

  1. Nikolaus Hautsch, 2012. "Econometrics of Financial High-Frequency Data," Springer Books, Springer, number 978-3-642-21925-2, January.

    Cited by:

    1. Giuseppe Cavaliere & Thomas Mikosch & Anders Rahbek & Frederik Vilandt, 2022. "The Econometrics of Financial Duration Modeling," Papers 2208.02098, arXiv.org, revised Dec 2022.
    2. Cavaliere, Giuseppe & Mikosch, Thomas & Rahbek, Anders & Vilandt, Frederik, 2024. "Tail behavior of ACD models and consequences for likelihood-based estimation," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    4. Siakoulis, Vasilios, 2015. "Modeling bank default intensity in the USA using autoregressive duration models," MPRA Paper 64526, University Library of Munich, Germany.
    5. Hiroyuki Kawakatsu, 2019. "Jointly Modeling Autoregressive Conditional Mean and Variance of Non-Negative Valued Time Series," Econometrics, MDPI, vol. 7(4), pages 1-19, December.
    6. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    7. Herrera, Rodrigo & González, Nicolás, 2014. "The modeling and forecasting of extreme events in electricity spot markets," International Journal of Forecasting, Elsevier, vol. 30(3), pages 477-490.
    8. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    9. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    10. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2013. "Goodness-of-fit test for specification of semiparametric copula dependence models," SFB 649 Discussion Papers 2013-041, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Alexandre de Carvalho & Alberto Sanyuan Suen & Felippe Gallo, 2016. "Market Efficiency in Brazil: some evidence from high-frequency data," Working Papers Series 431, Central Bank of Brazil, Research Department.
    12. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
    13. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2025. "A beta prime ARMA model for positive time series," MPRA Paper 123873, University Library of Munich, Germany.
    14. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    15. Daniel W. Richards & Janette Rutterford & Devendra Kodwani & Mark Fenton-O'Creevy, 2017. "Stock market investors' use of stop losses and the disposition effect," The European Journal of Finance, Taylor & Francis Journals, vol. 23(2), pages 130-152, January.
    16. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2020. "A higher-order correct fast moving-average bootstrap for dependent data," Working Papers unige:129395, University of Geneva, Geneva School of Economics and Management.
    17. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    18. Virgil DAMIAN & Cosmin – Octavian CEPOI, 2016. "Volatility Estimators With High-Frequency Data From Bucharest Stock Exchange," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 247-264.
    19. Kara Karpman & Sumanta Basu & David Easley, 2022. "Learning Financial Networks with High-frequency Trade Data," Papers 2208.03568, arXiv.org.
    20. N. Balakrishna & H. L. Koul & M. Ossiander & L. Sakhanenko, 2019. "Fitting a pth Order Parametric Generalized Linear Autoregressive Multiplicative Error Model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 103-122, September.
    21. Richard Y. Chen & Per A. Mykland, 2015. "Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data," Papers 1512.06159, arXiv.org, revised Oct 2018.
    22. Simona ADASCALI?EI, 2014. "High Frequency Trading – A Revolutionary Concept On Financial Markets," THE YEARBOOK OF THE "GH. ZANE" INSTITUTE OF ECONOMIC RESEARCHES, Gheorghe Zane Institute for Economic and Social Research ( from THE ROMANIAN ACADEMY, JASSY BRANCH), vol. 23(1), pages 27-34.
    23. Gerardo-Giorda, Luca & Germano, Guido & Scalas, Enrico, 2015. "Large scale simulation of synthetic markets," LSE Research Online Documents on Economics 67563, London School of Economics and Political Science, LSE Library.
    24. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    25. Curato, Imma Valentina, 2019. "Estimation of the stochastic leverage effect using the Fourier transform method," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3207-3238.
    26. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.
    27. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    28. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Oct 2025.
    29. Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
    30. Aknouche, Abdelhakim & Francq, Christian, 2020. "Stationarity and ergodicity of Markov switching positive conditional mean models," MPRA Paper 102503, University Library of Munich, Germany.
    31. José Da Fonseca & Riadh Zaatour, 2017. "Correlation and Lead–Lag Relationships in a Hawkes Microstructure Model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(3), pages 260-285, March.
    32. Šárka Hudecová & Michal Pešta, 2025. "Copula hurdle GARCH models for multivariate non-negative time series," Statistical Papers, Springer, vol. 66(4), pages 1-19, June.
    33. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    34. Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.
    35. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2012. "Modeling time-varying dependencies between positive-valued high-frequency time series," SFB 649 Discussion Papers 2012-054, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    36. Yuanhua Feng & Sarah Forstinger & Christian Peitz, 2013. "On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations," Working Papers CIE 66, Paderborn University, CIE Center for International Economics.
    37. Giuseppe Cavaliere & Thomas Mikosch & Anders Rahbek & Frederik Vilandt, 2025. "Beyond the Mean: Limit Theory and Tests for Infinite-Mean Autoregressive Conditional Durations," Papers 2505.06190, arXiv.org.
    38. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
    39. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    40. Pan, Beier, 2023. "The asymmetric dynamics of stock–bond liquidity correlation in China: The role of macro-financial determinants," Economic Modelling, Elsevier, vol. 124(C).
    41. Erindi Allaj, 2017. "Implicit Transaction Costs And The Fundamental Theorems Of Asset Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-39, June.
    42. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2021. "Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series," MPRA Paper 110954, University Library of Munich, Germany, revised 06 Dec 2021.
    43. Lock, Eduardo & Winkelried, Diego, 2015. "Flujos de órdenes en el mercado cambiario y el valor intrínseco del Nuevo Sol," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 29, pages 33-54.
    44. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2020. "Periodic autoregressive conditional duration," MPRA Paper 101696, University Library of Munich, Germany, revised 08 Jul 2020.
    45. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.
    46. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    47. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2024. "Volatility models versus intensity models: analogy and differences," MPRA Paper 122528, University Library of Munich, Germany.
    48. Samuel Gingras & William J. McCausland, 2020. "A Flexible Stochastic Conditional Duration Model," Papers 2005.09166, arXiv.org.
    49. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.

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