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Dacheng Xiu

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. Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & David Abad-Díaz & Menachem Abudy & To, 2021. "Non-Standard Errors," Working Paper Series, Social and Economic Sciences 2021-11, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neussüs & Michael Razen & Utz Weitzel & Christian Brownlees & 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 Neusüss & Michael Razen & Utz Weitzel & Edwin Baidoo & Michael Frömmel & 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).
    • Moinas, Sophie & Declerck, Fany & Menkveld, Albert J. & Dreber, Anna, 2023. "Non-Standard Errors," TSE Working Papers 23-1451, Toulouse School of Economics (TSE).
    • 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.
    • Ferrara, Gerardo & Jurkatis, Simon, 2021. "Non-standard errors," Bank of England working papers 955, Bank of England.
    • 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.
    • Ciril Bosch-Rosa & Bernhard Kassner, 2023. "Non-Standard Errors," Rationality and Competition Discussion Paper Series 385, CRC TRR 190 Rationality and Competition.
    • Menkveld, A. & Dreber, A. & Holzmeister, F. & Huber, J. & Johannesson, M. & Kirchler, M. & Neusüss, S. & Razen, M. & Neusüss, S. & Neusüss, S., 2021. "Non-Standard Errors," Janeway Institute Working Papers 2112, Faculty of Economics, University of Cambridge.
    • 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, C.E.P.R. Discussion Papers.
    • 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. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    2. 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.
    3. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Collaboration, Management Science Reproducibility, 2023. "Reproducibility in Management Science," OSF Preprints mydzv, Center for Open Science.
    4. Christoph Huber & Christian König-Kersting, 2022. "Experimenting with Financial Professionals," Working Papers 2022-07, Faculty of Economics and Statistics, Universität Innsbruck.
    5. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    6. Breznau, Nate & Rinke, Eike Mark & Wuttke, Alexander & Nguyen, Hung H. V. & Adem, Muna & Adriaans, Jule & Alvarez-Benjumea, Amalia & Andersen, Henrik K. & Auer, Daniel & Azevedo, Flavio & Bahnsen, Oke, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 119(44), pages 1-8.
    7. Müller, Isabella & Noth, Felix & Tonzer, Lena, 2022. "A note on the use of syndicated loan data," IWH Discussion Papers 17/2022, Halle Institute for Economic Research (IWH).
    8. 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.

  2. Giglio, Stefano & Xiu, Dacheng & Zhang, Dake, 2021. "Test Assets and Weak Factors," CEPR Discussion Papers 16307, C.E.P.R. Discussion Papers.

    Cited by:

    1. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    2. Jungjun Choi & Ming Yuan, 2024. "High Dimensional Factor Analysis with Weak Factors," Papers 2402.05789, arXiv.org.
    3. Jozef Barunik & Matej Nevrla, 2022. "Common Idiosyncratic Quantile Risk," Papers 2208.14267, arXiv.org, revised Jun 2023.
    4. Cisil Sarisoy & Bas J.M. Werker, 2024. "Linear Factor Models and the Estimation of Expected Returns," Finance and Economics Discussion Series 2024-014, Board of Governors of the Federal Reserve System (U.S.).
    5. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    6. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).

  3. Leland Bybee & Bryan T. Kelly & Asaf Manela & Dacheng Xiu, 2021. "Business News and Business Cycles," NBER Working Papers 29344, National Bureau of Economic Research, Inc.

    Cited by:

    1. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    2. Biktimirov, Ernest N. & Biktimirova, Liana E., 2023. "All topics are not created equal: Sentiment and hype of business media topics and the bitcoin market," Economics Letters, Elsevier, vol. 231(C).
    3. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.
    4. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
    5. Pablo Ottonello & Wenting Song, 2022. "Financial Intermediaries and the Macroeconomy: Evidence from a High-Frequency Identification," NBER Working Papers 29638, National Bureau of Economic Research, Inc.
    6. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.

  4. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.

    Cited by:

    1. Wolfgang Breuer & Jannis Bischof & Christian Hofmann & Jochen Hundsdoerfer & Hans-Ulrich Küpper & Marko Sarstedt & Philipp Schreck & Tim Weitzel & Peter Witt, 2023. "Recent developments in Business Economics," Journal of Business Economics, Springer, vol. 93(6), pages 989-1013, August.
    2. Bandi, Federico M. & Chaudhuri, Shomesh E. & Lo, Andrew W. & Tamoni, Andrea, 2021. "Spectral factor models," Journal of Financial Economics, Elsevier, vol. 142(1), pages 214-238.
    3. Alex Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2022. "A New Test of Risk Factor Relevance," Journal of Finance, American Finance Association, vol. 77(4), pages 2183-2238, August.
    4. Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    5. Cujean, Julien & Andrei, Daniel & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    6. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    7. Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
    8. David A. Mascio & Marat Molyboga & Frank J. Fabozzi, 2023. "The battle of the factors: Macroeconomic variables or investor sentiment?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2280-2291, December.
    9. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    10. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    11. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    12. Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    13. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    14. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    15. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    16. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    17. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    18. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    19. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    20. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    21. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 119289, London School of Economics and Political Science, LSE Library.
    22. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    23. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
    24. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    25. Chi-Ming Ho, 2023. "Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    26. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Jan 2024.
    27. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    28. Solène Collot & Tobias Hemauer, 2021. "A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 77-100, March.
    29. Andrew Detzel & Robert Novy‐Marx & Mihail Velikov, 2023. "Model Comparison with Transaction Costs," Journal of Finance, American Finance Association, vol. 78(3), pages 1743-1775, June.
    30. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    31. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    32. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    33. Abhimanyu Gupta & Myung Hwan Seo, 2023. "Robust Inference on Infinite and Growing Dimensional Time‐Series Regression," Econometrica, Econometric Society, vol. 91(4), pages 1333-1361, July.
    34. Thuy Duong Dang & Fabian Hollstein & Marcel Prokopczuk & Zhiguo He, 2023. "Which Factors for Corporate Bond Returns?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(4), pages 615-652.
    35. Weijia Peng & Chun Yao, 2023. "Sector-level equity returns predictability with machine learning and market contagion measure," Empirical Economics, Springer, vol. 65(4), pages 1761-1798, October.
    36. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers 2107.05201, arXiv.org, revised Oct 2021.
    37. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
    38. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    39. Pablo Solórzano-Taborga & Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Data Envelopment Analysis and Multifactor Asset Pricing Models," IJFS, MDPI, vol. 8(2), pages 1-18, April.
    40. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    41. Frank Kleibergen & Zhaoguo Zhan, 2022. "Misspecification and Weak Identification in Asset Pricing," Papers 2206.13600, arXiv.org.
    42. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    43. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2020. "Models for Expected Returns with Statistical Factors," JRFM, MDPI, vol. 13(12), pages 1-17, December.
    44. Alessi, Lucia & Balduzzi, Pierluigi & Savona, Roberto, 2019. "Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data," Working Papers 2019-03, Joint Research Centre, European Commission.
    45. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    46. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    47. Sang Il Lee & Seong Joon Yoo, 2019. "Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets," Papers 1903.06478, arXiv.org, revised Sep 2019.
    48. Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
    49. Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
    50. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    51. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    52. Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
    53. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    54. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    55. Konan Chan & Mei‐Xuan Li & Chu‐Bin Lin & Yanzhi Wang, 2022. "Organization capital effect in stock returns—The role of R&D," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(7-8), pages 1237-1263, July.
    56. Alla Petukhina & Simon Trimborn & Wolfgang Karl Härdle & Hermann Elendner, 2021. "Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies," Quantitative Finance, Taylor & Francis Journals, vol. 21(11), pages 1825-1853, November.
    57. Pascal Böni & Heinz Zimmermann, 2021. "Are stock prices driven by expected growth rather than discount rates? Evidence based on the COVID-19 crisis," Risk Management, Palgrave Macmillan, vol. 23(1), pages 1-29, June.
    58. Dichev, Ilia & Huang, Xinyi & Lee, Donald K.K & Zhao, Jianxin, 2023. "You have a point - but a point is not enough: The case for distributional forecasts of earnings," SocArXiv 4b2y8, Center for Open Science.
    59. R. Douglas Martin & Daniel Z. Xia, 2022. "Efficient bias robust regression for time series factor models," Journal of Asset Management, Palgrave Macmillan, vol. 23(3), pages 215-234, May.
    60. Zacharias Sautner & Laurence Van Lent & Grigory Vilkov & Ruishen Zhang, 2023. "Firm‐Level Climate Change Exposure," Journal of Finance, American Finance Association, vol. 78(3), pages 1449-1498, June.
    61. 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.
    62. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2021. "How to Explain the Cross-Section of Equity Returns through Common Principal Components," Mathematics, MDPI, vol. 9(9), pages 1-22, April.
    63. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    64. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    65. Jie Fang & Jianwu Lin & Shutao Xia & Yong Jiang & Zhikang Xia & Xiang Liu, 2020. "Neural Network-based Automatic Factor Construction," Papers 2008.06225, arXiv.org, revised Oct 2020.
    66. Baumeister, Christiane, 2021. "Measuring Market Expectations," CEPR Discussion Papers 16520, C.E.P.R. Discussion Papers.
    67. Manuel Ammann & Mathis Mörke, 2019. "Credit Variance Risk Premiums," Working Papers on Finance 1908, University of St. Gallen, School of Finance.
    68. Jushan Bai & Jiangtao Duan & Xu Han, 2022. "Likelihood ratio test for structural changes in factor models," Papers 2206.08052, arXiv.org, revised Dec 2023.
    69. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    70. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    71. Rama K. Malladi, 2024. "Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1021-1045, March.
    72. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factors’ CPDAG Roots and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_18, University of São Paulo (FEA-USP).
    73. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    74. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    75. Chen, Ding & Guo, Biao & Zhou, Guofu, 2023. "Firm fundamentals and the cross-section of implied volatility shapes," Journal of Financial Markets, Elsevier, vol. 63(C).
    76. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    77. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    78. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    79. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    80. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
    81. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    82. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    83. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    84. Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
    85. Paul Schneider & Christian Wagner & Josef Zechner, 2019. "Low Risk Anomalies?," Swiss Finance Institute Research Paper Series 19-50, Swiss Finance Institute.
    86. Gospodinov, Nikolay & Robotti, Cesare, 2021. "Common pricing across asset classes: Empirical evidence revisited," Journal of Financial Economics, Elsevier, vol. 140(1), pages 292-324.
    87. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    88. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    89. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    90. Yu, Hsin-Yi & Chen, Li-Wen & Chen, Chang-Yi, 2022. "The profitability effect: An evaluation of alternative explanations," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    91. Zaremba, Adam & Kizys, Renatas & Tzouvanas, Panagiotis & Aharon, David Y. & Demir, Ender, 2021. "The quest for multidimensional financial immunity to the COVID-19 pandemic: Evidence from international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    92. Cueto, José Manuel & Grané Chávez, Aurea & Cascos Fernández, Ignacio, 2019. "Models for expected returns with statistical factors," DES - Working Papers. Statistics and Econometrics. WS 28776, Universidad Carlos III de Madrid. Departamento de Estadística.
    93. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    94. Alexander M. Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2020. "Necessary Evidence For A Risk Factor’s Relevance," NBER Working Papers 27227, National Bureau of Economic Research, Inc.
    95. Xin Zhang & Lan Wu & Zhixue Chen, 2021. "Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm," Papers 2104.12484, arXiv.org.
    96. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    97. Cueto, José Manuel & Grané Chávez, Aurea & Cascos Fernández, Ignacio, 2021. "How to explain the cross-section of equity returns through Common Principal Components," DES - Working Papers. Statistics and Econometrics. WS 32258, Universidad Carlos III de Madrid. Departamento de Estadística.
    98. Avis Devine & Andrew Sanderford & Chongyu Wang, 2024. "Sustainability and Private Equity Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 161-187, February.
    99. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    100. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    101. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    102. Jorge M. Uribe & Montserrat Guillen, 2020. "Generalized Market Uncertainty Measurement in European Stock Markets in Real Time," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    103. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
    104. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    105. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
    106. Andre Guettler & Mahvish Naeem & Lars Norden & Bernardus Van Doornik, 2024. "Pre-Publication Revisions of Bank Financial Statements: a novel way to monitor banks?," Working Papers Series 590, Central Bank of Brazil, Research Department.
    107. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    108. Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
    109. Maryam Movahedifar & Hossein Hassani & Masoud Yarmohammadi & Mahdi Kalantari & Rangan Gupta, 2021. "A robust approach for outlier imputation: Singular Spectrum Decomposition," Working Papers 202164, University of Pretoria, Department of Economics.
    110. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    111. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    112. Niels Joachim Gormsen & Eben Lazarus, 2023. "Duration‐Driven Returns," Journal of Finance, American Finance Association, vol. 78(3), pages 1393-1447, June.
    113. Dong, Dayong & Wu, Keke & Fang, Jianchun & Gozgor, Giray & Yan, Cheng, 2022. "Investor attention factors and stock returns: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    114. Lu, Yueliang (Jacques) & Tian, Weidong, 2023. "An on-line machine learning return prediction," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    115. Gabriele D'Acunto & Paolo Bajardi & Francesco Bonchi & Gianmarco De Francisci Morales, 2021. "The Evolving Causal Structure of Equity Risk Factors," Papers 2111.05072, arXiv.org.
    116. Kazuki Amagai & Tomoya Suzuki, 2023. "Long-Term Modeling of Financial Machine Learning for Active Portfolio Management," Papers 2301.12346, arXiv.org.
    117. Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).
    118. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    119. Luo, Di, 2022. "ESG, liquidity, and stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    120. Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020. "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics 130, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    121. Hai Lin & Pengfei Liu & Cheng Zhang, 2023. "The trend premium around the world: Evidence from the stock market," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 317-358, June.
    122. Mark H.A. Davis & Sébastien Lleo, 2021. "Risk‐sensitive benchmarked asset management with expert forecasts," Mathematical Finance, Wiley Blackwell, vol. 31(4), pages 1162-1189, October.
    123. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    124. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factor Centrality and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_17, University of São Paulo (FEA-USP).
    125. Liu, Tingting & Lu, Zhongjin (Gene) & Shu, Tao & Wei, Fengrong, 2022. "Unique bidder-target relatedness and synergies creation in mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 73(C).
    126. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.
    127. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    128. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    129. Ma, Xiuli & Zhang, Xindong & Liu, Weimin, 2021. "Further tests of asset pricing models: Liquidity risk matters," Economic Modelling, Elsevier, vol. 95(C), pages 255-273.
    130. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    131. Xiao, Yuewen & Zhao, Jing, 2021. "Price dynamics of individual stocks: Jumps and information," Finance Research Letters, Elsevier, vol. 38(C).
    132. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
    133. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    134. Martha López & Eduardo Sarmiento G., 2023. "Excess Asset Returns Predictability in an Emerging Economy: The Case of Colombia," Borradores de Economia 1243, Banco de la Republica de Colombia.
    135. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.

  5. Leland Bybee & Bryan T. Kelly & Asaf Manela & Dacheng Xiu, 2020. "The Structure of Economic News," NBER Working Papers 26648, National Bureau of Economic Research, Inc.

    Cited by:

    1. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. J. Daniel Aromí, 2022. "Medición de Incertidumbre Económica en Redes Sociales en Base a Modelos de Procesamiento de Lenguaje Natural," Working Papers 179, Red Nacional de Investigadores en Economía (RedNIE).
    4. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    5. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    6. Elliott Ash & Germain Gauthier & Philine Widmer, 2021. "RELATIO: Text Semantics Capture Political and Economic Narratives," Papers 2108.01720, arXiv.org, revised Apr 2022.
    7. Laura Battaglia & Timothy Christensen & Stephen Hansen & Szymon Sacher, 2024. "Inference for Regression with Variables Generated from Unstructured Data," Papers 2402.15585, arXiv.org, revised Mar 2024.
    8. Perico Ortiz, Daniel & Schnaubelt, Matthias & Seifert, Oleg, 2023. "A topic modeling perspective on investor uncertainty," FAU Discussion Papers in Economics 04/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.
    10. Ashani Amarasinghe, 2020. "Diverting Domestic Turmoil," Monash Economics Working Papers 04-20, Monash University, Department of Economics.
    11. Daniel Perico Ortiz, 2023. "Economic policy statements, social media, and stock market uncertainty: An analysis of Donald Trump’s tweets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 333-367, June.
    12. Kim Ristolainen & Tomi Roukka & Henri Nyberg, 2021. "A Thousand Words Tell More Than Just Numbers: Financial Crises and Historical Headlines," Discussion Papers 149, Aboa Centre for Economics.
    13. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    14. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    15. Łukasz Baszczak, 2023. "Ekonomia narracji – początki nowego nurtu," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 66-81.
    16. Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
    17. Philine Widmer & Sergio Galletta & Elliott Ash, 2022. "Media Slant is Contagious," Papers 2202.07269, arXiv.org, revised Apr 2023.
    18. Venditti, Fabrizio & Veronese, Giovanni, 2020. "Global financial markets and oil price shocks in real time," Working Paper Series 2472, European Central Bank.
    19. Joelle Noailly; Laura Nowzohour; Matthias van den Heuvel, 2021. "Heard the News? Environmental Policy and Clean Investments," CIES Research Paper series 70-2021, Centre for International Environmental Studies, The Graduate Institute.
    20. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
    21. Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
    22. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
    23. Kumar, Alok & Lei, Zicheng & Zhang, Chendi, 2022. "Dividend sentiment, catering incentives, and return predictability," Journal of Corporate Finance, Elsevier, vol. 72(C).

  6. Yacine Aït-Sahalia & Jean Jacod & Dacheng Xiu, 2020. "Inference on Risk Premia in Continuous-Time Asset Pricing Models," NBER Working Papers 28140, National Bureau of Economic Research, Inc.

    Cited by:

    1. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Jozef Barunik & Matej Nevrla, 2022. "Common Idiosyncratic Quantile Risk," Papers 2208.14267, arXiv.org, revised Jun 2023.

  7. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.

    Cited by:

    1. 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).
    2. 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.
    3. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2023.
    4. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    5. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    6. Paul M. Anglin & Yanmin Gao, 2023. "Value of Communication and Social Media: An Equilibrium Theory of Messaging," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 861-903, May.
    7. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    8. Massimo Ferrari & Frederik Kurcz & Maria Sole Pagliari, 2021. "Do Words Hurt More Than Actions? The Impact of Trade Tensions on Financial Markets," Working papers 802, Banque de France.
    9. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2020. "Separating the signal from the noise – Financial machine learning for Twitter," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    10. Mengda Li & Charles-Albert Lehalle, 2021. "Do Word Embeddings Really Understand Loughran-McDonald's Polarities?," Papers 2103.09813, arXiv.org.
    11. Jianfei Zhang & Mathieu Rosenbaum, 2023. "Towards systematic intraday news screening: a liquidity-focused approach," Papers 2304.05115, arXiv.org.
    12. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    13. Yanan Yan & Yuehan Yang, 2023. "Community detection for New York stock market by SCORE-CCD," Computational Statistics, Springer, vol. 38(3), pages 1255-1282, September.
    14. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    15. Simon Fritzsch & Philipp Scharner & Gregor Weiß, 2021. "Estimating the relation between digitalization and the market value of insurers," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 529-567, September.
    16. Hansen, Stephen & Davis, Steven & Seminario-Amez, Cristhian, 2020. "Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19," CEPR Discussion Papers 15314, C.E.P.R. Discussion Papers.
    17. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    18. Christopher A. Hollrah & Steven A. Sharpe & Nitish R. Sinha, 2020. "The Power of Narratives in Economic Forecasts," Finance and Economics Discussion Series 2020-001, Board of Governors of the Federal Reserve System (U.S.).
    19. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    20. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    21. Charles W. Calomiris & Nida Çakır Melek & Harry Mamaysky, 2021. "Predicting the Oil Market," NBER Working Papers 29379, National Bureau of Economic Research, Inc.
    22. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
    23. Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Esg Investing: A Sentiment Analysis Approach," Working Papers halshs-03917335, HAL.
    24. Zheng, Hannan & Schwenkler, Gustavo, 2020. "The network of firms implied by the news," ESRB Working Paper Series 108, European Systemic Risk Board.
    25. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    26. García, Diego & Hu, Xiaowen & Rohrer, Maximilian, 2023. "The colour of finance words," Journal of Financial Economics, Elsevier, vol. 147(3), pages 525-549.
    27. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    28. Jeon, Yoontae & McCurdy, Thomas H. & Zhao, Xiaofei, 2022. "News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies," Journal of Financial Economics, Elsevier, vol. 145(2), pages 1-17.
    29. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    30. Qinkai Chen & Christian-Yann Robert, 2021. "Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data," Papers 2107.10941, arXiv.org, revised Dec 2021.
    31. Schnaubelt, Matthias & Seifert, Oleg, 2020. "Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements?," FAU Discussion Papers in Economics 04/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

  8. Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
    3. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Avramov, Doron & Li, Minwen & Wang, Hao, 2021. "Predicting corporate policies using downside risk: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 1-26.
    5. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
    6. Michael Pinelis & David Ruppert, 2020. "Machine Learning Portfolio Allocation," Papers 2003.00656, arXiv.org, revised Nov 2021.
    7. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
    8. Yujie Ding & Shuai Jia & Tianyi Ma & Bingcheng Mao & Xiuze Zhou & Liuliu Li & Dongming Han, 2023. "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction," Papers 2310.05627, arXiv.org.
    9. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
    10. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    11. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    12. 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).
    13. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    14. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
    15. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    16. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
    17. Christian Fieberg & Lars Hornuf & Gerrit Liedtke & Thorsten Poddig, 2020. "Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis," CESifo Working Paper Series 8377, CESifo.
    18. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    19. David A. Mascio & Marat Molyboga & Frank J. Fabozzi, 2023. "The battle of the factors: Macroeconomic variables or investor sentiment?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2280-2291, December.
    20. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    21. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    22. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    23. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    24. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
    25. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    26. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2021. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Reprints LFIN 2021014, Université catholique de Louvain, Louvain Finance (LFIN).
    27. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
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    317. Edeling, Alexander & Srinivasan, Shuba & Hanssens, Dominique M., 2021. "The marketing–finance interface: A new integrative review of metrics, methods, and findings and an agenda for future research," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 857-876.
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    327. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
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    333. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
    334. Zongwu Cai & Pixiong Chen, 2022. "New Online Investor Sentiment and Asset Returns," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202216, University of Kansas, Department of Economics, revised Nov 2022.
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  9. Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.

    Cited by:

    1. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    2. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
    3. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.
    4. G. Gopalakrishna, 2017. "Robust test of Long Run Risk and Valuation risk model," Working Papers wp1107, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Valentin Haddad & Serhiy Kozak & Shrihari Santosh, 2017. "Predicting Relative Returns," NBER Working Papers 23886, National Bureau of Economic Research, Inc.
    6. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    7. Gabriele D'Acunto & Paolo Bajardi & Francesco Bonchi & Gianmarco De Francisci Morales, 2021. "The Evolving Causal Structure of Equity Risk Factors," Papers 2111.05072, arXiv.org.
    8. Choi, Jaewon & Kim, Yongjun, 2018. "Anomalies and market (dis)integration," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 16-34.
    9. Bretscher, Lorenzo & Hsu, Alex & Tamoni, Andrea, 2020. "Fiscal policy driven bond risk premia," Journal of Financial Economics, Elsevier, vol. 138(1), pages 53-73.
    10. Max Schreder & Pawel Bilinski, 2022. "Information Quality and the Expected Rate of Return: A Structural Equation Modelling Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 139-170, June.

  10. KALNINA, Ilze & XIU, Dacheng, 2015. "Nonparametric estimation of the leverage effect: a trade-off between robustness and efficiency," Cahiers de recherche 2015-05, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. Carsten H. Chong & Viktor Todorov, 2023. "Asymptotic Expansions for High-Frequency Option Data," Papers 2304.12450, arXiv.org.
    2. Aït-Sahalia, Yacine & Kalnina, Ilze & Xiu, Dacheng, 2020. "High-frequency factor models and regressions," Journal of Econometrics, Elsevier, vol. 216(1), pages 86-105.
    3. Todorov, Viktor, 2021. "Higher-order small time asymptotic expansion of Itô semimartingale characteristic function with application to estimation of leverage from options," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 671-705.
    4. Huiling Yuan & Yulei Sun & Lu Xu & Yong Zhou & Xiangyu Cui, 2022. "A new volatility model: GQARCH‐ItÔ model," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 345-370, May.
    5. Carsten H. Chong & Viktor Todorov, 2023. "Volatility of Volatility and Leverage Effect from Options," Papers 2305.04137, arXiv.org, revised Jan 2024.
    6. Giacomo Toscano & Maria Cristina Recchioni, 2020. "Bias optimal vol-of-vol estimation: the role of window overlapping," Papers 2004.04013, arXiv.org, revised Jul 2021.
    7. Markus Bibinger & Christopher J. Neely & Lars Winkelmann, 2017. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Working Papers 2017-12, Federal Reserve Bank of St. Louis.
    8. Yang, Xinxin & Zheng, Xinghua & Chen, Jiaqi, 2021. "Testing high-dimensional covariance matrices under the elliptical distribution and beyond," Journal of Econometrics, Elsevier, vol. 221(2), pages 409-423.
    9. Mingmian Cheng & Norman R. Swanson, 2019. "Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence," Econometrics, MDPI, vol. 7(1), pages 1-32, March.
    10. Giacomo Toscano & Maria Cristina Recchioni, 2022. "Bias-optimal vol-of-vol estimation: the role of window overlapping," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 137-185, June.
    11. Yuan, Huiling & Zhou, Yong & Xu, Lu & Sun, Yulei & Cui, Xiangyu, 2020. "A New Volatility Model: GQARCH-Ito Model," SocArXiv hkzdr, Center for Open Science.
    12. Zhi Liu, 2022. "Testing for the Presence of the Leverage Effect without Estimation," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    13. Huang, Jing-Zhi & Ni, Jun & Xu, Li, 2022. "Leverage effect in cryptocurrency markets," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    14. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Central limit theorems," Papers 2210.01216, arXiv.org, revised Jul 2023.
    15. Giacomo Toscano & Giulia Livieri & Maria Elvira Mancino & Stefano Marmi, 2021. "Volatility of volatility estimation: central limit theorems for the Fourier transform estimator and empirical study of the daily time series stylized facts," Papers 2112.14529, arXiv.org, revised Sep 2022.
    16. Curato, Imma Valentina & Sanfelici, Simona, 2022. "Stochastic leverage effect in high-frequency data: a Fourier based analysis," Econometrics and Statistics, Elsevier, vol. 23(C), pages 53-82.

  11. Yacine Aït-Sahalia & Dacheng Xiu, 2015. "Principal Component Analysis of High Frequency Data," NBER Working Papers 21584, National Bureau of Economic Research, Inc.

    Cited by:

    1. Aït-Sahalia, Yacine & Kalnina, Ilze & Xiu, Dacheng, 2020. "High-frequency factor models and regressions," Journal of Econometrics, Elsevier, vol. 216(1), pages 86-105.
    2. Dovonon, Prosper & Taamouti, Abderrahim & Williams, Julian, 2022. "Testing the eigenvalue structure of spot and integrated covariance," Journal of Econometrics, Elsevier, vol. 229(2), pages 363-395.
    3. Iara da Silva & Caroline Fernanda Hei Wikuats & Elizabeth Mie Hashimoto & Leila Droprinchinski Martins, 2022. "Effects of Environmental and Socioeconomic Inequalities on Health Outcomes: A Multi-Region Time-Series Study," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    4. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    5. Bollerslev, Tim & Patton, Andrew J. & Zhang, Haozhe, 2022. "Equity clusters through the lens of realized semicorrelations," Economics Letters, Elsevier, vol. 211(C).
    6. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    7. 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.
    8. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    9. Benth, Fred Espen & Schroers, Dennis & Veraart, Almut E.D., 2022. "A weak law of large numbers for realised covariation in a Hilbert space setting," Stochastic Processes and their Applications, Elsevier, vol. 145(C), pages 241-268.
    10. Guangbao Guo & Chunjie Wei & Guoqi Qian, 2023. "Sparse online principal component analysis for parameter estimation in factor model," Computational Statistics, Springer, vol. 38(2), pages 1095-1116, June.
    11. YAMAMOTO, Yohei & 山本, 庸平, 2015. "Asymptotic Inference for Common Factor Models in the Presence of Jumps," Discussion Papers 2015-05, Graduate School of Economics, Hitotsubashi University.
    12. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
    13. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
    14. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    15. Jos'e E. Figueroa-L'opez & Bei Wu, 2020. "Kernel Estimation of Spot Volatility with Microstructure Noise Using Pre-Averaging," Papers 2004.01865, arXiv.org, revised Feb 2022.
    16. Xianyu Yu & Huachen Gao, 2020. "A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    17. Li, Jia & Todorov, Viktor & Tauchen, George, 2016. "Inference theory for volatility functional dependencies," Journal of Econometrics, Elsevier, vol. 193(1), pages 17-34.
    18. Reiß, Markus & Winkelmann, Lars, 2021. "Inference on the maximal rank of time-varying covariance matrices using high-frequency data," Discussion Papers 2021/14, Free University Berlin, School of Business & Economics.
    19. 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).
    20. Muhammad Riaz & Babar Zaman & Ishaq Adeyanju Raji & M. Hafidz Omar & Rashid Mehmood & Nasir Abbas, 2022. "An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector," Mathematics, MDPI, vol. 10(12), pages 1-27, June.
    21. Li, Hong & Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2021. "Improved index insurance design and yield estimation using a dynamic factor forecasting approach," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 208-221.
    22. 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.
    23. Calypso Herrera & Florian Krach & Anastasis Kratsios & Pierre Ruyssen & Josef Teichmann, 2020. "Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices," Papers 2004.13612, arXiv.org, revised Jun 2023.
    24. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.
    25. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    26. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

  12. Zhaogang Song & Dacheng Xiu, 2014. "A Tale of Two Option Markets: Pricing Kernels and Volatility Risk," Finance and Economics Discussion Series 2014-58, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Andrew Papanicolaou, 2021. "Extreme-Strike Comparisons and Structural Bounds for SPX and VIX Options," Papers 2101.00299, arXiv.org, revised Mar 2021.
    2. Ruijun Bu & Fredj Jawadi & Yuyi Li, 2020. "A multifactor transformed diffusion model with applications to VIX and VIX futures," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 27-53, January.
    3. Denis Belomestny & Wolfgang Karl Härdle & Ekaterina Krymova, 2017. "Sieve Estimation Of The Minimal Entropy Martingale Marginal Density With Application To Pricing Kernel Estimation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(06), pages 1-21, September.
    4. Chris Bardgett & Elise Gourier & Markus Leippold, 2016. "Inferring Volatility Dynamics and Risk Premia from the S&P 500 and VIX markets," Working Papers 780, Queen Mary University of London, School of Economics and Finance.
    5. Barone-Adesi, Giovanni & Fusari, Nicola & Mira, Antonietta & Sala, Carlo, 2020. "Option market trading activity and the estimation of the pricing kernel: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 216(2), pages 430-449.
    6. Maik Dierkes & Jan Krupski & Sebastian Schroen & Philipp Sibbertsen, 2024. "Volatility-dependent probability weighting and the dynamics of the pricing kernel puzzle," Review of Derivatives Research, Springer, vol. 27(1), pages 1-35, April.
    7. Manuel Ammann & Alexander Feser, 2019. "Robust Estimation of Risk-Neutral Moments," Working Papers on Finance 1902, University of St. Gallen, School of Finance.
    8. Barletta, Andrea & Santucci de Magistris, Paolo & Violante, Francesco, 2019. "A non-structural investigation of VIX risk neutral density," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 1-20.
    9. Ilze Kalnina & Dacheng Xiu, 2017. "Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 384-396, January.
    10. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    11. Gloria Gonzalez-Rivera & Joao Henrique Mazzeu & Esther Ruiz & Helena Veiga, 2017. "A Bootstrap Approach for Generalized Autocontour Testing. Implications for VIX Forecast Densities," Working Papers 201709, University of California at Riverside, Department of Economics.
    12. Brendan K. Beare & Juwon Seo, 2022. "Stochastic arbitrage with market index options," Papers 2207.00949, arXiv.org, revised Jul 2022.
    13. Steven Heston & Kris Jacobs & Hyung Joo Kim, 2023. "The Pricing Kernel in Options," Finance and Economics Discussion Series 2023-053, Board of Governors of the Federal Reserve System (U.S.).
    14. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "A data-driven framework for consistent financial valuation and risk measurement," European Journal of Operational Research, Elsevier, vol. 289(1), pages 381-398.
    15. Robert A. Jarrow & Simon S. Kwok, 2021. "Inferring financial bubbles from option data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 1013-1046, November.
    16. Chen Tong & Zhuo Huang & Tianyi Wang, 2022. "Do VIX futures contribute to the valuation of VIX options?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1644-1664, September.
    17. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    18. Koëter, Joren, 2021. "Essays on asset pricing, investor preferences, and derivative markets," Other publications TiSEM 9e88a66e-b972-4af3-91d6-0, Tilburg University, School of Economics and Management.
    19. Manuel Ammann & Alexander Feser, 2019. "Robust estimation of risk‐neutral moments," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1137-1166, September.
    20. Gonçalves Mazzeu, Joao Henrique & González-Rivera, Gloria & Ruiz Ortega, Esther & Veiga, Helena, 2016. "A Bootstrap Approach for Generalized Autocontour Testing," DES - Working Papers. Statistics and Econometrics. WS 23457, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Oct 2022.
    22. Yaw‐Huei Wang & Kuang‐Chieh Yen, 2019. "The information content of the implied volatility term structure on future returns," European Financial Management, European Financial Management Association, vol. 25(2), pages 380-406, March.
    23. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    24. Alexandru Badescu & Zhenyu Cui & Juan-Pablo Ortega, 2019. "Closed-form variance swap prices under general affine GARCH models and their continuous-time limits," Annals of Operations Research, Springer, vol. 282(1), pages 27-57, November.
    25. Chen Tong & Zhuo Huang, 2021. "Pricing VIX options with realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1180-1200, August.
    26. Hasibul Chowdhury & Robert Faff & Khoa Hoang, 2021. "Using abnormal analyst coverage to unlock new evidence on stock price crash risk," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 1557-1588, April.
    27. Arjun Chatrath & Rohan A. Christie‐David & Hong Miao & Sanjay Ramchander, 2019. "Losers and prospectors in the short‐term options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(6), pages 721-743, June.
    28. Yabei Zhu & Xingguo Luo & Qi Xu, 2023. "Industry variance risk premium, cross‐industry correlation, and expected returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(1), pages 3-32, January.
    29. Jarno Talponen, 2018. "Matching distributions: Recovery of implied physical densities from option prices," Papers 1803.03996, arXiv.org.
    30. Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021. "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, vol. 100(C).
    31. Shvimer, Yossi & Herbon, Avi, 2020. "Comparative empirical study of binomial call-option pricing methods using S&P 500 index data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    32. Lu, Junwen & Qu, Zhongjun, 2021. "Sieve estimation of option-implied state price density," Journal of Econometrics, Elsevier, vol. 224(1), pages 88-112.
    33. Jiao, Yuhan & Liu, Qiang & Guo, Shuxin, 2021. "Pricing kernel monotonicity and term structure: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 123(C).
    34. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2021. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Papers 2112.05302, arXiv.org.
    35. Bu Ruijun & Cheng Jie & Hadri Kaddour, 2017. "Specification analysis in regime-switching continuous-time diffusion models for market volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 65-80, February.
    36. Zu, Yang & Boswijk, H. Peter, 2017. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Journal of Empirical Finance, Elsevier, vol. 41(C), pages 53-75.
    37. Andrea Barletta & Paolo Santucci de Magistris & Francesco Violante, 2016. "Retrieving Risk-Neutral Densities Embedded in VIX Options: a Non-Structural Approach," CREATES Research Papers 2016-20, Department of Economics and Business Economics, Aarhus University.
    38. Huang, Darien & Schlag, Christian & Shaliastovich, Ivan & Thimme, Julian, 2018. "Volatility-of-volatility risk," SAFE Working Paper Series 210, Leibniz Institute for Financial Research SAFE.
    39. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    40. Ana M. Monteiro & Antonio A. F. Santos, 2020. "Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints," Review of Derivatives Research, Springer, vol. 23(1), pages 41-61, April.
    41. Bollerslev, Tim & Todorov, Viktor, 2014. "Time-varying jump tails," Journal of Econometrics, Elsevier, vol. 183(2), pages 168-180.
    42. Schneider, Paul, 2019. "An anatomy of the market return," Journal of Financial Economics, Elsevier, vol. 132(2), pages 325-350.
    43. Liao, Wen Ju & Sung, Hao-Chang, 2020. "Implied risk aversion and pricing kernel in the FTSE 100 index," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    44. Maria Grith & Wolfgang Karl Härdle & Volker Krätschmer, 2013. "Reference Dependent Preferences and the EPK Puzzle," SFB 649 Discussion Papers SFB649DP2013-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    45. Xinyu WU & Senchun REN & Hailin ZHOU, 2017. "Empirical Pricing Kernels: Evidence from the Hong Kong Stock Market," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 263-278.
    46. Ana M. Monteiro & António A. F. Santos, 2022. "Option prices for risk‐neutral density estimation using nonparametric methods through big data and large‐scale problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 152-171, January.
    47. Augustyniak, Maciej & Badescu, Alexandru & Bégin, Jean-François, 2023. "A discrete-time hedging framework with multiple factors and fat tails: On what matters," Journal of Econometrics, Elsevier, vol. 232(2), pages 416-444.
    48. Xingguo Luo & Jin E. Zhang & Wenjun Zhang, 2019. "Instantaneous squared VIX and VIX derivatives," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1193-1213, October.

  13. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.

    Cited by:

    1. Yuta Koike, 2014. "An estimator for the cumulative co-volatility of asynchronously observed semimartingales with jumps," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 460-481, June.
    2. 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.
    3. Stefano Peluso & Fulvio Corsi & Antonietta Mira, 2015. "A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 665-697.
    4. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    5. 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.

Articles

  1. Stefano Giglio & Bryan Kelly & Dacheng Xiu, 2022. "Factor Models, Machine Learning, and Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 337-368, November.

    Cited by:

    1. Yujie Ding & Shuai Jia & Tianyi Ma & Bingcheng Mao & Xiuze Zhou & Liuliu Li & Dongming Han, 2023. "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction," Papers 2310.05627, arXiv.org.
    2. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    3. Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2024. "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models," Papers 2402.03659, arXiv.org, revised Feb 2024.
    4. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "View fusion vis-\`a-vis a Bayesian interpretation of Black-Litterman for portfolio allocation," Papers 2301.13594, arXiv.org.
    5. Jozef Barunik & Matej Nevrla, 2022. "Common Idiosyncratic Quantile Risk," Papers 2208.14267, arXiv.org, revised Jun 2023.
    6. Chen, Ding & Guo, Biao & Zhou, Guofu, 2023. "Firm fundamentals and the cross-section of implied volatility shapes," Journal of Financial Markets, Elsevier, vol. 63(C).
    7. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
    8. Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
    9. Nie, Chun-Xiao & Song, Fu-Tie, 2023. "Stable versus fragile community structures in the correlation dynamics of Chinese industry indices," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

  2. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.

    Cited by:

    1. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
    2. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    3. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    4. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    6. Adcock, Christopher & Bessler, Wolfgang & Conlon, Thomas, 2022. "Characteristic-sorted portfolios and macroeconomic risks—An orthogonal decomposition," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 24-50.
    7. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    8. Bruno Spilak & Wolfgang Karl Hardle, 2022. "Risk budget portfolios with convex Non-negative Matrix Factorization," Papers 2204.02757, arXiv.org, revised Jun 2023.
    9. Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
    10. David Alaminos & Ignacio Esteban & M. Belén Salas, 2023. "Neural networks for estimating Macro Asset Pricing model in football clubs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 57-75, April.
    11. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    12. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    13. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    14. Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
    15. Castro-Iragorri, C & Ramírez, J, 2021. "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo 19431, Universidad del Rosario.
    16. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    17. Zikai Wei & Anyi Rao & Bo Dai & Dahua Lin, 2023. "HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE," Papers 2306.02848, arXiv.org.
    18. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    19. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    20. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    21. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "On statistical arbitrage under a conditional factor model of equity returns," Papers 2309.02205, arXiv.org.
    22. Ni Zhan & Yijia Sun & Aman Jakhar & He Liu, 2021. "Graphical Models for Financial Time Series and Portfolio Selection," Papers 2101.09214, arXiv.org.
    23. Yilun Wang & Shengjie Guo, 2024. "RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction," Papers 2403.02500, arXiv.org.
    24. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    25. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    26. Daniel Poh & Stephen Roberts & Stefan Zohren, 2022. "Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity," Papers 2208.09968, arXiv.org, revised Feb 2023.
    27. Peter B. Lerner, 2022. "Fourier Integral Operator Model of Market Liquidity: The Chinese Experience 2009–2010," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    28. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    29. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    30. Li Rong Wang & Hsuan Fu & Xiuyi Fan, 2023. "Stock Price Predictability and the Business Cycle via Machine Learning," Papers 2304.09937, arXiv.org.
    31. Qihui Chen, 2022. "A Unified Framework for Estimation of High-dimensional Conditional Factor Models," Papers 2209.00391, arXiv.org.
    32. Jiti Gao & Bin Peng & Yanrong Yang, 2023. "A Localized Neural Network with Dependent Data: Estimation and Inference," Papers 2306.05593, arXiv.org.
    33. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
    34. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Higher-order Expansions and Inference for Panel Data Models," Papers 2205.00577, arXiv.org, revised Jun 2023.
    35. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
    36. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
    37. Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
    38. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    39. Weidong Lin & Abderrahim Taamouti, 2023. "Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach," Working Papers 202310, University of Liverpool, Department of Economics.
    40. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
    41. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    42. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    43. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
    44. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
    45. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org.
    46. Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
    47. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
    48. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
    49. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    50. Ioana Boier, 2022. "Multiresolution Signal Processing of Financial Market Objects," Papers 2210.15934, arXiv.org, revised Nov 2022.
    51. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    52. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    53. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    54. Kazuki Amagai & Tomoya Suzuki, 2023. "Long-Term Modeling of Financial Machine Learning for Active Portfolio Management," Papers 2301.12346, arXiv.org.
    55. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    56. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    57. Hector O. Zapata & Supratik Mukhopadhyay, 2022. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing," JRFM, MDPI, vol. 15(11), pages 1-17, November.
    58. Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
    59. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    60. Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
    61. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    62. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    63. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.

  3. Stefano Giglio & Dacheng Xiu, 2021. "Asset Pricing with Omitted Factors," Journal of Political Economy, University of Chicago Press, vol. 129(7), pages 1947-1990.

    Cited by:

    1. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Giuseppe Cavaliere & S'ilvia Gonc{c}alves & Morten {O}rregaard Nielsen & Edoardo Zanelli, 2022. "Bootstrap inference in the presence of bias," Papers 2208.02028, arXiv.org, revised Nov 2023.
    4. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    5. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org.
    6. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    7. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.
    8. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    9. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    10. Lee, Hsiu-Chuan & Lee, Yun-Huan & Nguyen, Cuong, 2023. "Tail comovements of implied volatility indices and global index futures returns predictability," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    11. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    12. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    13. Jozef Barunik & Mattia Bevilacqua & Michael Ellington, 2023. "Common Firm-level Investor Fears: Evidence from Equity Options," Papers 2309.03968, arXiv.org.
    14. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    15. Federico C. Nucera & Lucio Sarno & Gabriele Zinna, 2023. "Currency risk premiums redux?," Temi di discussione (Economic working papers) 1415, Bank of Italy, Economic Research and International Relations Area.
    16. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    17. Calice, Giovanni & Lin, Ming-Tsung, 2021. "Exploring risk premium factors for country equity returns," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 294-322.
    18. Nicola Borri & Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2024. "One Factor to Bind the Cross-Section of Returns," NBER Working Papers 32365, National Bureau of Economic Research, Inc.
    19. Amjad Taha & Gulcay Tuna, 2023. "Oil Price and Composite Risk Exposure within International Capital Asset Pricing Model: A Case of Saudi Arabia and Turkey," Energies, MDPI, vol. 16(7), pages 1-18, March.
    20. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
    21. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).
    22. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).
    23. Lee, Kiryoung & Joen, Yoontae & Kim, Minki, 2022. "Which uncertainty measures matter for the cross-section of stock returns?#," Finance Research Letters, Elsevier, vol. 46(PB).
    24. Zhaoxing Gao & Ruey S. Tsay, 2023. "Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors," Papers 2307.07689, arXiv.org.
    25. Avis Devine & Andrew Sanderford & Chongyu Wang, 2024. "Sustainability and Private Equity Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 161-187, February.
    26. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
    27. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    29. Felix Haase & Matthias Neuenkirch, 2023. "Macroeconomic Expectations and State-Dependent Factor Returns," Research Papers in Economics 2023-09, University of Trier, Department of Economics.
    30. Beaulieu, Marie-Claude & Dufour, Jean-Marie & Khalaf, Lynda & Melin, Olena, 2023. "Identification-robust beta pricing, spanning, mimicking portfolios, and the benchmark neutrality of catastrophe bonds," Journal of Econometrics, Elsevier, vol. 236(1).
    31. Muhammad Abdullah & Hussein A. Abdou & Christopher Godfrey & Ahmed A. Elamer & Yousry Ahmed, 2023. "Assessing the Use of Gold as a Zero-Beta Asset in Empirical Asset Pricing: Application to the US Equity Market," JRFM, MDPI, vol. 16(3), pages 1-48, March.
    32. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2023. "Beta-Sorted Portfolios," Staff Reports 1068, Federal Reserve Bank of New York.
    33. Mikhail Chernov & Magnus Dahlquist & Lars Lochstoer, 2023. "Pricing Currency Risks," Journal of Finance, American Finance Association, vol. 78(2), pages 693-730, April.
    34. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    35. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    36. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    37. Cynthia M. Gong & Di Luo & Huainan Zhao, 2021. "Liquidity risk and the beta premium," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(4), pages 789-814, December.

  4. Stefano Giglio & Yuan Liao & Dacheng Xiu & Wei Jiang, 2021. "Thousands of Alpha Tests [The performance of hedge funds: Risk, return, and incentives]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3456-3496.

    Cited by:

    1. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
    2. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    3. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    4. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Journal of Finance, American Finance Association, vol. 75(5), pages 2503-2553, October.
    5. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    6. Huang, Haitao & Jiang, Lei & Leng, Xuan & Peng, Liang, 2023. "Bootstrap analysis of mutual fund performance," Journal of Econometrics, Elsevier, vol. 235(1), pages 239-255.
    7. Lee, Hsiu-Chuan & Lee, Yun-Huan & Nguyen, Cuong, 2023. "Tail comovements of implied volatility indices and global index futures returns predictability," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    8. Pätäri, Eero & Ahmed, Sheraz & Luukka, Pasi & Yeomans, Julian Scott, 2023. "Can monthly-return rank order reveal a hidden dimension of momentum? The post-cost evidence from the U.S. stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    9. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    10. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    11. Nabil Bouamara & S'ebastien Laurent & Shuping Shi, 2023. "Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications," Papers 2303.13406, arXiv.org, revised Jun 2023.
    12. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Mar 2024.
    13. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    14. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    15. Niels Joachim Gormsen & Eben Lazarus, 2023. "Duration‐Driven Returns," Journal of Finance, American Finance Association, vol. 78(3), pages 1393-1447, June.
    16. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    17. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    18. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Papers 2006.04269, arXiv.org.

  5. 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.

    Cited by:

    1. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    2. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    3. Huang, Jionghao & Chen, Baifan & Xu, Yushi & Xia, Xiaohua, 2023. "Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach," Finance Research Letters, Elsevier, vol. 53(C).
    4. Cunado, Juncal & Chatziantoniou, Ioannis & Gabauer, David & de Gracia, Fernando Perez & Hardik, Marfatia, 2023. "Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures," Journal of Commodity Markets, Elsevier, vol. 30(C).

  6. Aït-Sahalia, Yacine & Kalnina, Ilze & Xiu, Dacheng, 2020. "High-frequency factor models and regressions," Journal of Econometrics, Elsevier, vol. 216(1), pages 86-105.

    Cited by:

    1. Andersen, Torben G. & Riva, Raul & Thyrsgaard, Martin & Todorov, Viktor, 2023. "Intraday cross-sectional distributions of systematic risk," Journal of Econometrics, Elsevier, vol. 235(2), pages 1394-1418.
    2. 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.
    3. Winkelmann, Lars & Yao, Wenying, 2021. "Tests for jumps in yield spreads," Discussion Papers 2021/15, Free University Berlin, School of Business & Economics.
    4. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    5. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    6. Aysenur Tarakcioglu Altinay & Mesut Dogan & Bilge Leyli Demirel Ergun & Sevdie Alshiqi, 2023. "The Fama-French Five-Factor Asset Pricing Model: A Research on Borsa Istanbul," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 3-21.
    7. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org.
    8. Michael D. Plante, 2023. "Investing in the Batteries and Vehicles of the Future: A View Through the Stock Market," Working Papers 2314, Federal Reserve Bank of Dallas, revised 25 Mar 2024.
    9. Shin, Minseok & Kim, Donggyu & Fan, Jianqing, 2023. "Adaptive robust large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 237(1).
    10. Janis Becker & Christian Leschinski, 2021. "Estimating the volatility of asset pricing factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 269-278, March.
    11. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org.
    12. Jareño, Francisco & González, María de la O & Escolástico, Alba M., 2020. "Extension of the Fama and French model: A study of the largest European financial institutions," International Economics, Elsevier, vol. 164(C), pages 115-139.

  7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    See citations under working paper version above.
  8. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    See citations under working paper version above.
  9. 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.

    Cited by:

    1. Cai, T. Tony & Hu, Jianchang & Li, Yingying & Zheng, Xinghua, 2020. "High-dimensional minimum variance portfolio estimation based on high-frequency data," Journal of Econometrics, Elsevier, vol. 214(2), pages 482-494.
    2. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    3. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    4. Erlin Guo & Cuixia Li & Fengqin Tang, 2023. "The Convergence Rates of Large Volatility Matrix Estimator Based on Noise, Jumps, and Asynchronization," Mathematics, MDPI, vol. 11(6), pages 1-11, March.
    5. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    6. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    7. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    8. Davide Lauria & W. Brent Lindquist & Svetlozar T. Rachev, 2023. "Enhancing CVaR portfolio optimisation performance with GAM factor models," Papers 2401.00188, arXiv.org.
    9. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.

  10. Aït-Sahalia, Yacine & Xiu, Dacheng, 2019. "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, vol. 211(1), pages 176-205.

    Cited by:

    1. Maria Elvira Mancino & Tommaso Mariotti & Giacomo Toscano, 2022. "Asymptotic Normality for the Fourier spot volatility estimator in the presence of microstructure noise," Papers 2209.08967, arXiv.org.
    2. Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
    3. Aït-Sahalia, Yacine & Kalnina, Ilze & Xiu, Dacheng, 2020. "High-frequency factor models and regressions," Journal of Econometrics, Elsevier, vol. 216(1), pages 86-105.
    4. 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..
    5. Diego Amaya & Jean-François Bégin & Geneviève Gauthier, 2022. "The Informational Content of High-Frequency Option Prices," Management Science, INFORMS, vol. 68(3), pages 2166-2201, March.
    6. Winkelmann, Lars & Yao, Wenying, 2020. "Cojump anchoring," Discussion Papers 2020/17, Free University Berlin, School of Business & Economics.
    7. 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).
    8. Maria Elvira Mancino & Simone Scotti & Giacomo Toscano, 2020. "Is the variance swap rate affine in the spot variance? Evidence from S&P500 data," Papers 2004.04015, arXiv.org.
    9. Winkelmann, Lars & Yao, Wenying, 2021. "Tests for jumps in yield spreads," Discussion Papers 2021/15, Free University Berlin, School of Business & Economics.
    10. Giacomo Toscano & Maria Cristina Recchioni, 2020. "Bias optimal vol-of-vol estimation: the role of window overlapping," Papers 2004.04013, arXiv.org, revised Jul 2021.
    11. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    12. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    13. Michael D. Plante, 2023. "Investing in the Batteries and Vehicles of the Future: A View Through the Stock Market," Working Papers 2314, Federal Reserve Bank of Dallas, revised 25 Mar 2024.
    14. Giacomo Toscano & Maria Cristina Recchioni, 2022. "Bias-optimal vol-of-vol estimation: the role of window overlapping," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 137-185, June.
    15. 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.
    16. Beste Hamiye Beyaztas & Soutir Bandyopadhyay & Abhijit Mandal, 2021. "A robust specification test in linear panel data models," Papers 2104.07723, arXiv.org.
    17. Mykland, Per A. & Zhang, Lan, 2021. "The Observed Asymptotic Variance: Hard edges, and a regression approach," Journal of Econometrics, Elsevier, vol. 222(1), pages 411-428.
    18. Yang, Chao & Yu, Chengcheng & Dong, Wentao & Yuan, Quan, 2023. "Substitutes or complements? Examining effects of urban rail transit on bus ridership using longitudinal city-level data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    19. Dimpfl, Thomas & Peter, Franziska J., 2021. "Nothing but noise? Price discovery across cryptocurrency exchanges," Journal of Financial Markets, Elsevier, vol. 54(C).

  11. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
    See citations under working paper version above.
  12. Amengual, Dante & Xiu, Dacheng, 2018. "Resolution of policy uncertainty and sudden declines in volatility," Journal of Econometrics, Elsevier, vol. 203(2), pages 297-315.

    Cited by:

    1. Gu, Chen & Kurov, Alexander & Wolfe, Marketa Halova, 2018. "Relief Rallies after FOMC Announcements as a Resolution of Uncertainty," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 1-18.
    2. Ruijun Bu & Fredj Jawadi & Yuyi Li, 2020. "A multifactor transformed diffusion model with applications to VIX and VIX futures," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 27-53, January.
    3. Lorella Fatone & Francesca Mariani & Francesco Zirilli, 2024. "Calibration in the “real world” of a partially specified stochastic volatility model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 75-102, January.
    4. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    5. Liu, Hong & Tang, Xiaoxiao & Zhou, Guofu, 2022. "Recovering the FOMC risk premium," Journal of Financial Economics, Elsevier, vol. 145(1), pages 45-68.
    6. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    7. Thomas Eisenbach & Martin Schmalz & Marianne Andries, 2015. "Asset Pricing with Horizon-Dependent Risk Aversion," 2015 Meeting Papers 1069, Society for Economic Dynamics.
    8. Yacine Aït-Sahalia & Felix Matthys & Emilio Osambela & Ronnie Sircar, 2021. "When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance," NBER Working Papers 29195, National Bureau of Economic Research, Inc.
    9. Bruno Feunou & Mohammad R. Jahan-Parvar & Cédric Okou, 2015. "Downside Variance Risk Premium," Staff Working Papers 15-36, Bank of Canada.
    10. Ahmed Al Samman & Mostafa Kotb GabAlla, 2020. "Impact of Country Risk and Return on FPI," International Journal of Economics and Financial Issues, Econjournals, vol. 10(6), pages 57-68.
    11. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    12. Michael D Bauer & Aeimit Lakdawala & Philippe Mueller, 2022. "Market-Based Monetary Policy Uncertainty," The Economic Journal, Royal Economic Society, vol. 132(644), pages 1290-1308.
    13. Baxa, Jaromir & Buliskeria, Nino & Elminejad, Ali & Havranek, Tomas & Havrankova, Zuzana & Kundu, Suranjana, 2023. "A comment on Bauer, Lakdawala, Mueller: Market-Based Monetary Policy Uncertainty (2022)," I4R Discussion Paper Series 77, The Institute for Replication (I4R).
    14. Wang, Qi & Wang, Zerong, 2020. "VIX valuation and its futures pricing through a generalized affine realized volatility model with hidden components and jump," Journal of Banking & Finance, Elsevier, vol. 116(C).
    15. Vilhelmsson, Anders, 2020. "Macro news and long-run volatility expectations," Knut Wicksell Working Paper Series 2020/1, Lund University, Knut Wicksell Centre for Financial Studies.
    16. Marianne Andries & Thomas M. Eisenbach & Martin C. Schmalz, 2014. "Horizon-Dependent Risk Aversion and the Timing and Pricing of Uncertainty," Staff Reports 703, Federal Reserve Bank of New York.
    17. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    18. Luu Duc Huynh, Toan, 2020. "The effect of uncertainty on the precious metals market: New insights from Transfer Entropy and Neural Network VAR," Resources Policy, Elsevier, vol. 66(C).
    19. Oh, Dong Hwan & Park, Yang-Ho, 2023. "GARCH option pricing with volatility derivatives," Journal of Banking & Finance, Elsevier, vol. 146(C).
    20. Peter Van Tassel, 2017. "Global Variance Term Premia and Intermediary Risk Appetite," 2017 Meeting Papers 149, Society for Economic Dynamics.
    21. Zaremba, Adam & Kizys, Renatas & Aharon, David Y., 2021. "Volatility in International Sovereign Bond Markets: The role of government policy responses to the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 43(C).
    22. Claudiu Tiberiu Albulescu & Eugenia Grecu, 2023. "Government Interventions and Sovereign Bond Market Volatility during COVID-19: A Quantile Analysis," Mathematics, MDPI, vol. 11(5), pages 1-14, February.
    23. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    24. Daniel Perico Ortiz, 2023. "Economic policy statements, social media, and stock market uncertainty: An analysis of Donald Trump’s tweets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 333-367, June.
    25. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    26. Kurov, Alexander & Wolfe, Marketa Halova & Gilbert, Thomas, 2021. "The disappearing pre-FOMC announcement drift," Finance Research Letters, Elsevier, vol. 40(C).
    27. Christopher Thiem, 2020. "Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility," Open Economies Review, Springer, vol. 31(2), pages 317-342, April.
    28. Isiaka Akande Raifu, 2023. "Examining structural stability and time-varying causality between economic policy uncertainty and Asia-Pacific Islamic stock price," Economics Bulletin, AccessEcon, vol. 43(1), pages 28-37.
    29. Yang-Ho Park, 2019. "Variance Disparity and Market Frictions," Finance and Economics Discussion Series 2019-059, Board of Governors of the Federal Reserve System (U.S.).
    30. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Leibniz Institute for Financial Research SAFE.
    31. Ai, Hengjie & Han, Leyla Jianyu & Pan, Xuhui Nick & Xu, Lai, 2022. "The cross section of the monetary policy announcement premium," Journal of Financial Economics, Elsevier, vol. 143(1), pages 247-276.
    32. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "The Term Structure of Systematic and Idiosyncratic Risk," Hannover Economic Papers (HEP) dp-618, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    33. Nguyen, Thanh Cong, 2022. "Economic policy uncertainty: The probability and duration of economic recessions in major European Union countries," Research in International Business and Finance, Elsevier, vol. 62(C).
    34. Deniz Erdemlioglu & Christopher J. Neely & Xiye Yang, 2023. "Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications," Working Papers 2023-016, Federal Reserve Bank of St. Louis.
    35. Wang, Yuchen & Wang, Xiaoming, 2023. "Economic policy uncertainty and information intermediary: The case of short seller," Economic Modelling, Elsevier, vol. 120(C).
    36. Pacati, Claudio & Pompa, Gabriele & Renò, Roberto, 2018. "Smiling twice: The Heston++ model," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 185-206.
    37. Difang Huang & Yubin Li & Xinjie Wang & Zhaodong (Ken) Zhong, 2022. "Does the Federal Open Market Committee cycle affect credit risk?," Financial Management, Financial Management Association International, vol. 51(1), pages 143-167, March.
    38. Byomakesh Debata & Jitendra Mahakud, 2018. "Economic policy uncertainty and stock market liquidity," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 10(1), pages 112-135, April.
    39. Bin Wu & Pengzhan Chen & Wuyi Ye, 2021. "Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1055-1073, July.
    40. Bruno Feunou & Cédric Okou, 2018. "Risk‐neutral moment‐based estimation of affine option pricing models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 1007-1025, November.
    41. Kundu, Srikanta & Paul, Amartya, 2022. "Effect of economic policy uncertainty on stock market return and volatility under heterogeneous market characteristics," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 597-612.
    42. Park, Yang-Ho, 2020. "Variance disparity and market frictions," Journal of Econometrics, Elsevier, vol. 214(2), pages 326-348.
    43. Perico Ortiz, Daniel, 2021. "The high frequency impact of economic policy narratives on stock market uncertainty," FAU Discussion Papers in Economics 02/2021, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    44. Katherine B. Ensor & Yu Han & Barbara Ostdiek & Stuart M. Turnbull, 2020. "Dynamic jump intensities and news arrival in oil futures markets," Journal of Asset Management, Palgrave Macmillan, vol. 21(4), pages 292-325, July.
    45. Fulop, Andras & Li, Junye, 2019. "Bayesian estimation of dynamic asset pricing models with informative observations," Journal of Econometrics, Elsevier, vol. 209(1), pages 114-138.

  13. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.

    Cited by:

    1. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Optimal Portfolio Using Factor Graphical Lasso," Working Papers 202025, University of California at Riverside, Department of Economics.
    2. Kong, Xin-Bing & Liu, Zhi & Zhou, Wang, 2019. "A rank test for the number of factors with high-frequency data," Journal of Econometrics, Elsevier, vol. 211(2), pages 439-460.
    3. Matthew Muntifering, 2021. "Air pollution, investor sentiment and excessive returns," Journal of Asset Management, Palgrave Macmillan, vol. 22(2), pages 110-119, March.
    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. Dovonon, Prosper & Taamouti, Abderrahim & Williams, Julian, 2022. "Testing the eigenvalue structure of spot and integrated covariance," Journal of Econometrics, Elsevier, vol. 229(2), pages 363-395.
    6. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Yinqiu He & Zi Wang & Gongjun Xu, 2021. "A Note on the Likelihood Ratio Test in High-Dimensional Exploratory Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 442-463, June.
    8. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    9. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    10. Cai, T. Tony & Hu, Jianchang & Li, Yingying & Zheng, Xinghua, 2020. "High-dimensional minimum variance portfolio estimation based on high-frequency data," Journal of Econometrics, Elsevier, vol. 214(2), pages 482-494.
    11. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Sep 2023.
    12. Xin-Bing Kong & Yong-Xin Liu & Long Yu & Peng Zhao, 2022. "Matrix Quantile Factor Model," Papers 2208.08693, arXiv.org, revised May 2023.
    13. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    14. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    15. Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
    16. Román-Aso, Juan A. & Coca Villalba, Fernando & Mastral Franks, Vanessa & Bosch Frigola, Irene, 2021. "Using Principal Component Analysis to create an index of financial conditions in Spain. Differences by firm size and industry," EconStor Preprints 234038, ZBW - Leibniz Information Centre for Economics.
    17. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    18. Noureddine Kouaissah & Sergio Ortobelli Lozza & Ikram Jebabli, 2022. "Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 833-859, October.
    19. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    20. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    21. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    22. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    23. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    24. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    25. Guangbao Guo & Chunjie Wei & Guoqi Qian, 2023. "Sparse online principal component analysis for parameter estimation in factor model," Computational Statistics, Springer, vol. 38(2), pages 1095-1116, June.
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    29. Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020. "A dynamic conditional approach to portfolio weights forecasting," Papers 2004.12400, arXiv.org.
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    31. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    32. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    33. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
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    35. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas," Departmental Working Papers 201711, Rutgers University, Department of Economics.
    36. Ding, Yi & Li, Yingying & Zheng, Xinghua, 2021. "High dimensional minimum variance portfolio estimation under statistical factor models," Journal of Econometrics, Elsevier, vol. 222(1), pages 502-515.
    37. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    38. Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2023. "Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets," Papers 2302.09382, arXiv.org.
    39. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2021. "High-dimensional estimation of quadratic variation based on penalized realized variance," Papers 2103.03237, arXiv.org.
    40. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    41. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    42. Markus Pelger & Ruoxuan Xiong, 2022. "State-Varying Factor Models of Large Dimensions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1315-1333, June.
    43. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    44. Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
    45. Shin, Minseok & Kim, Donggyu & Fan, Jianqing, 2023. "Adaptive robust large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 237(1).
    46. 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.
    47. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    48. Kim, Donggyu & Song, Xinyu & Wang, Yazhen, 2022. "Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    49. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
    50. Rafael Alves & Diego S. de Brito & Marcelo C. Medeiros & Ruy M. Ribeiro, 2023. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Papers 2303.16151, arXiv.org.
    51. Reiß, Markus & Winkelmann, Lars, 2021. "Inference on the maximal rank of time-varying covariance matrices using high-frequency data," Discussion Papers 2021/14, Free University Berlin, School of Business & Economics.
    52. 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.
    53. 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).
    54. Jan Patrick Hartkopf, 2023. "Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models," Empirical Economics, Springer, vol. 64(1), pages 393-436, January.
    55. Lam, Clifford & Feng, Phoenix, 2018. "A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data," LSE Research Online Documents on Economics 88375, London School of Economics and Political Science, LSE Library.
    56. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    57. Šprajc, Polona & Bjegović, Miroslav & Vasić, Bojana, 2019. "Energy security in decision making and governance - Methodological analysis of energy trilemma index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    58. Lam, Clifford & Feng, Phoenix, 2018. "A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data," Journal of Econometrics, Elsevier, vol. 206(1), pages 226-257.
    59. 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.
    60. Yu, Long & He, Yong & Zhang, Xinsheng, 2019. "Robust factor number specification for large-dimensional elliptical factor model," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    61. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    62. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    63. Kong, Xin-Bing & Liu, Cheng, 2018. "Testing against constant factor loading matrix with large panel high-frequency data," Journal of Econometrics, Elsevier, vol. 204(2), pages 301-319.
    64. 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.
    65. Gribisch, Bastian & Hartkopf, Jan Patrick & Liesenfeld, Roman, 2020. "Factor state–space models for high-dimensional realized covariance matrices of asset returns," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 1-20.
    66. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2021. "Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data," Papers 2102.12783, arXiv.org, revised Feb 2022.
    67. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.
    68. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
    69. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.

  14. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.

    Cited by:

    1. 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).
    2. Kong, Xin-Bing & Liu, Cheng, 2018. "Testing against constant factor loading matrix with large panel high-frequency data," Journal of Econometrics, Elsevier, vol. 204(2), pages 301-319.
    3. 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.
    4. 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. Ilze Kalnina & Dacheng Xiu, 2017. "Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 384-396, January.
    See citations under working paper version above.
  16. 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.

    Cited by:

    1. Aït-Sahalia, Yacine & Xiu, Dacheng, 2019. "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, vol. 211(1), pages 176-205.
    2. 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.
    3. Usman Arief & Zaäfri Ananto Husodo, 2021. "Private Information from Extreme Price Movements (Empirical Evidences from Southeast Asia Countries)," International Symposia in Economic Theory and Econometrics, in: Recent Developments in Asian Economics International Symposia in Economic Theory and Econometrics, volume 28, pages 221-242, Emerald Group Publishing Limited.
    4. Sönksen, Jantje & Grammig, Joachim, 2021. "Empirical asset pricing with multi-period disaster risk: A simulation-based approach," Journal of Econometrics, Elsevier, vol. 222(1), pages 805-832.
    5. Li, Chenxing & Maheu, John M, 2020. "A Multivariate GARCH-Jump Mixture Model," MPRA Paper 104770, University Library of Munich, Germany.
    6. Arouri, Mohamed & M’saddek, Oussama & Nguyen, Duc Khuong & Pukthuanthong, Kuntara, 2019. "Cojumps and asset allocation in international equity markets," Journal of Economic Dynamics and Control, Elsevier, vol. 98(C), pages 1-22.
    7. Shaw, Charles, 2018. "Conditional heteroskedasticity in crypto-asset returns," MPRA Paper 90437, University Library of Munich, Germany.
    8. Semeyutin, Artur & Gozgor, Giray & Lau, Chi Keung Marco & Xu, Bing, 2021. "Effects of idiosyncratic jumps and co-jumps on oil, gold, and copper markets," Energy Economics, Elsevier, vol. 104(C).
    9. Liling Deng & Haifang Xiong & Zhiqiang Wang, 2023. "Research on cojumps of electronic commerce overnight factors in volatility prediction based on joint BW test," Electronic Commerce Research, Springer, vol. 23(1), pages 115-135, March.
    10. Weijia Peng & Chun Yao, 2023. "Sector-level equity returns predictability with machine learning and market contagion measure," Empirical Economics, Springer, vol. 65(4), pages 1761-1798, October.
    11. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    12. Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio," Papers 2002.02008, arXiv.org, revised Sep 2020.
    13. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
    14. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    15. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    16. Ron Alquist & Reinhard Ellwanger & Jianjian Jin, 2020. "The Effect of Oil Price Shocks on Asset Markets: Evidence from Oil Inventory News," Staff Working Papers 2020-8, Bank of Canada.
    17. Donggyu Kim & Minseok Shin, 2023. "Volatility models for stylized facts of high‐frequency financial data," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 262-279, May.
    18. 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.
    19. Shin, Minseok & Kim, Donggyu & Fan, Jianqing, 2023. "Adaptive robust large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 237(1).
    20. Charles Shaw, 2018. "Conditional heteroskedasticity in crypto-asset returns," Papers 1804.07978, arXiv.org, revised Dec 2018.
    21. Deniz Erdemlioglu & Christopher J. Neely & Xiye Yang, 2023. "Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications," Working Papers 2023-016, Federal Reserve Bank of St. Louis.
    22. Semeyutin, Artur & Downing, Gareth, 2022. "Co-jumps in the U.S. interest rates and precious metals markets and their implications for investors," International Review of Financial Analysis, Elsevier, vol. 81(C).
    23. Hattori, Masazumi & Shim, Ilhyock & Sugihara, Yoshihiko, 2021. "Cross-stock market spillovers through variance risk premiums and equity flows," Journal of International Money and Finance, Elsevier, vol. 119(C).
    24. Donggyu Kim & Minseok Shin & Yazhen Wang, 2021. "Overnight GARCH-It\^o Volatility Models," Papers 2102.13467, arXiv.org, revised Jun 2022.
    25. Bruno Solnik & Thaisiri Watewai, 2016. "International Correlation Asymmetries: Frequent-but-Small and Infrequent-but-Large Equity Returns," PIER Discussion Papers 31, Puey Ungphakorn Institute for Economic Research.
    26. 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.
    27. Konstantinos Gkillas & Paraskevi Katsiampa & Dimitrios I. Vortelinos & Mark E. Wohar, 2023. "Greek government‐debt crisis events and European financial markets: News surprises on Greek bond yields and inter‐relations of European financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4037-4054, October.
    28. Iwanicz-Drozdowska Małgorzata & Rogowicz Karol & Smaga Paweł, 2023. "Market-moving events and their role in portfolio optimization of generations X, Y, and Z," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 59(4), pages 371-397, December.
    29. Zainudin, Ahmad Danial & Mohamad, Azhar, 2021. "Cross hedging with stock index futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 128-144.
    30. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    31. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
    32. Pablo Cristini Guedes & Fernanda Maria Müller & Marcelo Brutti Righi, 2023. "Risk measures-based cluster methods for finance," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-56, March.
    33. Aleksy Leeuwenkamp & Wentao Hu, 2023. "New general dependence measures: construction, estimation and application to high-frequency stock returns," Papers 2309.00025, arXiv.org.
    34. Gottschalk, Sylvia, 2017. "Entropy measure of credit risk in highly correlated markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 11-19.
    35. Donggyu Kim, 2021. "Exponential GARCH-Ito Volatility Models," Papers 2111.04267, arXiv.org.
    36. Zhang, Yi & Zhou, Long & Chen, Yajiao & Liu, Fang, 2022. "The contagion effect of jump risk across Asian stock markets during the Covid-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    37. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    38. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.

  17. Jia Li & Dacheng Xiu, 2016. "Generalized Method of Integrated Moments for High‐Frequency Data," Econometrica, Econometric Society, vol. 84, pages 1613-1633, July.

    Cited by:

    1. Yang, Xiye, 2020. "Time-invariant restrictions of volatility functionals: Efficient estimation and specification tests," Journal of Econometrics, Elsevier, vol. 215(2), pages 486-516.
    2. Zhang, Congshan & Li, Jia & Todorov, Viktor & Tauchen, George, 2022. "Variation and efficiency of high-frequency betas," Journal of Econometrics, Elsevier, vol. 228(1), pages 156-175.
    3. 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.
    4. Tim Bollerslev & Jia Li & Leonardo Salim Saker Chaves, 2021. "Generalized Jump Regressions for Local Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1015-1025, October.
    5. Jacod, Jean & Klüppelberg, Claudia & Müller, Gernot, 2017. "Testing for non-correlation between price and volatility jumps," Journal of Econometrics, Elsevier, vol. 197(2), pages 284-297.
    6. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    7. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    8. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    9. Angelo Ranaldo & Paolo Santucci de Magistris, 2018. "Trading Volume, Illiquidity and Commonalities in FX Markets," Working Papers on Finance 1823, University of St. Gallen, School of Finance, revised Oct 2019.
    10. Alessandro Casini & Pierre Perron, 2020. "Continuous Record Laplace-based Inference about the Break Date in Structural Change Models," Boston University - Department of Economics - Working Papers Series WP2020-014, Boston University - Department of Economics.
    11. Ruijun Bu & Jihyun Kim & Bin Wang, 2020. "Uniform and Lp Convergences of Nonparametric Estimation for Diffusion Models," Working Papers 202021, University of Liverpool, Department of Economics.
    12. 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.
    13. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    14. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.
    15. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

  18. Song, Zhaogang & Xiu, Dacheng, 2016. "A tale of two option markets: Pricing kernels and volatility risk," Journal of Econometrics, Elsevier, vol. 190(1), pages 176-196.
    See citations under working paper version above.
  19. 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.

    Cited by:

    1. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Optimal Portfolio Using Factor Graphical Lasso," Working Papers 202025, University of California at Riverside, Department of Economics.
    2. Kong, Xin-Bing & Liu, Zhi & Zhou, Wang, 2019. "A rank test for the number of factors with high-frequency data," Journal of Econometrics, Elsevier, vol. 211(2), pages 439-460.
    3. Hui ‘Fox’ Ling & Christian Franzen, 2017. "Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1277-1304, August.
    4. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2015. "Asset Allocation Strategies Based On Penalized Quantile Regression," "Marco Fanno" Working Papers 0199, Dipartimento di Scienze Economiche "Marco Fanno".
    5. 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.
    6. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    7. 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.
    8. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    10. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    11. Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
    12. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    13. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Janeway Institute Working Papers 2208, Faculty of Economics, University of Cambridge.
    14. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    15. 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).
    16. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    17. Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020. "A dynamic conditional approach to portfolio weights forecasting," Papers 2004.12400, arXiv.org.
    18. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    19. 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).
    20. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    21. 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.
    22. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    23. Ding, Yi & Li, Yingying & Zheng, Xinghua, 2021. "High dimensional minimum variance portfolio estimation under statistical factor models," Journal of Econometrics, Elsevier, vol. 222(1), pages 502-515.
    24. Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2023. "Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets," Papers 2302.09382, arXiv.org.
    25. Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
    26. Shin, Minseok & Kim, Donggyu & Fan, Jianqing, 2023. "Adaptive robust large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 237(1).
    27. Caner, Mehmet & Medeiros, Marcelo & Vasconcelos, Gabriel F.R., 2023. "Sharpe Ratio analysis in high dimensions: Residual-based nodewise regression in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 393-417.
    28. Kim, Donggyu & Song, Xinyu & Wang, Yazhen, 2022. "Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    29. Rafael Alves & Diego S. de Brito & Marcelo C. Medeiros & Ruy M. Ribeiro, 2023. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Papers 2303.16151, arXiv.org.
    30. Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023. "Too similar to combine? On negative weights in forecast combination," International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.
    31. 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.
    32. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    33. Jan Patrick Hartkopf, 2023. "Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models," Empirical Economics, Springer, vol. 64(1), pages 393-436, January.
    34. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    35. Zhang, Jinhua & Mao, Rui & Wang, Jieyu & Xing, Mengying, 2021. "The way back home: Trading behaviours of foreign institutional investors in China amid the COVID-19 pandemic," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    36. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    37. Kong, Xin-Bing & Liu, Cheng, 2018. "Testing against constant factor loading matrix with large panel high-frequency data," Journal of Econometrics, Elsevier, vol. 204(2), pages 301-319.
    38. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    39. Xin-Bing Kong, 2017. "On the number of common factors with high-frequency data," Biometrika, Biometrika Trust, vol. 104(2), pages 397-410.
    40. 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.
    41. Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
    42. Gribisch, Bastian & Hartkopf, Jan Patrick & Liesenfeld, Roman, 2020. "Factor state–space models for high-dimensional realized covariance matrices of asset returns," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 1-20.
    43. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2021. "Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data," Papers 2102.12783, arXiv.org, revised Feb 2022.
    44. Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
    45. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
    46. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.

  20. Xiu, Dacheng, 2014. "Hermite polynomial based expansion of European option prices," Journal of Econometrics, Elsevier, vol. 179(2), pages 158-177.

    Cited by:

    1. Cui, Liyuan & Hong, Yongmiao & Li, Yingxing, 2021. "Solving Euler equations via two-stage nonparametric penalized splines," Journal of Econometrics, Elsevier, vol. 222(2), pages 1024-1056.
    2. Yang, Nian & Chen, Nan & Wan, Xiangwei, 2019. "A new delta expansion for multivariate diffusions via the Itô-Taylor expansion," Journal of Econometrics, Elsevier, vol. 209(2), pages 256-288.
    3. Barletta, Andrea & Santucci de Magistris, Paolo & Violante, Francesco, 2019. "A non-structural investigation of VIX risk neutral density," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 1-20.
    4. 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.
    5. Antonio Cosma & Stefano Galluccio & Paola Pederzoli & O. Scaillet, 2012. "Valuing American Options Using Fast Recursive Projections," Swiss Finance Institute Research Paper Series 12-26, Swiss Finance Institute.
    6. Xin Zang & Jun Ni & Jing-Zhi Huang & Lan Wu, 2015. "Double-jump stochastic volatility model for VIX: evidence from VVIX," Papers 1506.07554, arXiv.org, revised Jul 2015.
    7. Aït-Sahalia, Yacine & Li, Chenxu & Li, Chen Xu, 2021. "Closed-form implied volatility surfaces for stochastic volatility models with jumps," Journal of Econometrics, Elsevier, vol. 222(1), pages 364-392.
    8. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "A data-driven framework for consistent financial valuation and risk measurement," European Journal of Operational Research, Elsevier, vol. 289(1), pages 381-398.
    9. Wan, Xiangwei & Yang, Nian, 2021. "Hermite expansion of transition densities and European option prices for multivariate diffusions with jumps," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    10. Stoyan V. Stoyanov & Yong Shin Kim & Svetlozar T. Rachev & Frank J. Fabozzi, 2017. "Option pricing for Informed Traders," Papers 1711.09445, arXiv.org.
    11. Zhu, Ke & Ling, Shiqing, 2015. "Model-based pricing for financial derivatives," Journal of Econometrics, Elsevier, vol. 187(2), pages 447-457.
    12. Li, Chenxu & Chen, Dachuan, 2016. "Estimating jump–diffusions using closed-form likelihood expansions," Journal of Econometrics, Elsevier, vol. 195(1), pages 51-70.
    13. Barletta, Andrea & Santucci de Magistris, Paolo & Sloth, David, 2019. "It only takes a few moments to hedge options," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 251-269.
    14. Damir Filipovic & Damien Ackerer & Sergio Pulido, 2018. "The Jacobi Stochastic Volatility Model," Post-Print hal-01338330, HAL.
    15. Shan Lu, 2019. "Monte Carlo analysis of methods for extracting risk‐neutral densities with affine jump diffusions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1587-1612, December.
    16. Eraker, Bjørn & Wang, Jiakou, 2015. "A non-linear dynamic model of the variance risk premium," Journal of Econometrics, Elsevier, vol. 187(2), pages 547-556.
    17. Xin Zang & Jun Ni & Jing-Zhi Huang & Lan Wu, 2017. "Double-jump diffusion model for VIX: evidence from VVIX," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 227-240, February.
    18. Steven L. Heston & Alberto G. Rossi, 2017. "A Spanning Series Approach to Options," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 7(1), pages 2-42.
    19. Kirkby, J.L. & Nguyen, Dang H. & Nguyen, Duy & Nguyen, Nhu N., 2022. "Maximum likelihood estimation of diffusions by continuous time Markov chain," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    20. Choi, Seungmoon, 2015. "Explicit form of approximate transition probability density functions of diffusion processes," Journal of Econometrics, Elsevier, vol. 187(1), pages 57-73.
    21. Falko Baustian & Katev{r}ina Filipov'a & Jan Posp'iv{s}il, 2019. "Solution of option pricing equations using orthogonal polynomial expansion," Papers 1912.06533, arXiv.org, revised Jun 2020.
    22. Qu, Yan & Dassios, Angelos & Zhao, Hongbiao, 2023. "Shot-noise cojumps: exact simulation and option pricing," LSE Research Online Documents on Economics 111537, London School of Economics and Political Science, LSE Library.
    23. Carlo Marinelli & Stefano d’Addona, 2023. "Nonparametric estimates of option prices via Hermite basis functions," Annals of Finance, Springer, vol. 19(4), pages 477-522, December.
    24. Andrea Barletta & Paolo Santucci de Magistris, 2018. "Analyzing the Risks Embedded in Option Prices with rndfittool," Risks, MDPI, vol. 6(2), pages 1-15, March.
    25. Carlo Marinelli & Stefano d'Addona, 2022. "Nonparametric estimates of option prices via Hermite basis functions," Papers 2209.09656, arXiv.org, revised Aug 2023.
    26. Kailin Ding & Zhenyu Cui & Yanchu Liu, 2023. "Sequential Itô–Taylor expansions and characteristic functions of stochastic volatility models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1750-1769, December.
    27. Antonio Cosma & Stefano Galluccio & Paola Pederzoli & O. Scaillet, 2016. "Early Exercise Decision in American Options with Dividends, Stochastic Volatility and Jumps," Swiss Finance Institute Research Paper Series 16-73, Swiss Finance Institute.
    28. Alziary Chassat, Bénédicte & Takac, Peter, 2017. "On the Heston Model with Stochastic Volatility: Analytic Solutions and Complete Markets," TSE Working Papers 17-796, Toulouse School of Economics (TSE).
    29. Andrea Barletta & Paolo Santucci de Magistris & Francesco Violante, 2016. "Retrieving Risk-Neutral Densities Embedded in VIX Options: a Non-Structural Approach," CREATES Research Papers 2016-20, Department of Economics and Business Economics, Aarhus University.
    30. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    31. Thomas Mazzoni, 2018. "Asymptotic Expansion of Risk-Neutral Pricing Density," IJFS, MDPI, vol. 6(1), pages 1-26, March.
    32. Damien Ackerer & Damir Filipovi'c & Sergio Pulido, 2016. "The Jacobi Stochastic Volatility Model," Papers 1605.07099, arXiv.org, revised Mar 2018.

  21. Jianqing Fan & Lei Qi & Dacheng Xiu, 2014. "Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 178-191, April.

    Cited by:

    1. Meister, Alexander & Kreiß, Jens-Peter, 2016. "Statistical inference for nonparametric GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 126(10), pages 3009-3040.
    2. Francq, Christian & Zakoian, Jean-Michel, 2012. "Risk-parameter estimation in volatility models," MPRA Paper 41713, University Library of Munich, Germany.
    3. Sentana, Enrique & Fiorentini, Gabriele, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," CEPR Discussion Papers 12934, C.E.P.R. Discussion Papers.
    4. Caiya Zhang & Kaihong Xu & Lianfen Qian, 2020. "Asymptotic properties of the QMLE in a log-linear RealGARCH model with Gaussian errors," Statistical Papers, Springer, vol. 61(6), pages 2313-2330, December.
    5. Conrad, Christian & Mammen , Enno, 2015. "Asymptotics for parametric GARCH-in-Mean Models," Working Papers 0579, University of Heidelberg, Department of Economics.
    6. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    7. Miguel Antonio Alba Suárez & Wilmer Pineda-Ríos & Javier Deaza Chaves, 2019. "Análisis comparativo de las metodologías de estimación semiparamétricas y vía cópulas del Valor en Riesgo (VaR) en el mercado accionario colombiano," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 279-307, Abril-Jun.
    8. Javed Farrukh & Podgórski Krzysztof, 2017. "Tail Behavior and Dependence Structure in the APARCH Model," Journal of Time Series Econometrics, De Gruyter, vol. 9(2), pages 1-48, July.
    9. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    10. Rasmus Søndergaard Pedersen & Anders Rahbek, 2015. "Nonstationary ARCH and GARCH with t-Distributed Innovations," Discussion Papers 15-07, University of Copenhagen. Department of Economics.
    11. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    12. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    13. Georgios Bertsatos & Plutarchos Sakellaris, 2017. "Did the Financial Crisis affect the Market Valuation of Large Systemic U.S. Banks?," Working Papers 201709, Athens University Of Economics and Business, Department of Economics.
    14. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    15. Ñíguez, Trino-Manuel & Perote, Javier, 2016. "Multivariate moments expansion density: Application of the dynamic equicorrelation model," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 216-232.
    16. Hang Liu & Kanchan Mukherjee, 2022. "R-estimators in GARCH models: asymptotics and applications [Rank-based estimation for GARCH processes]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 98-113.
    17. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2022. "The impact and role of COVID-19 uncertainty: A global industry analysis," International Review of Financial Analysis, Elsevier, vol. 80(C).
    18. Christian Gouriéroux & Alain Monfort & Eric Renault, 2017. "Consistent Pseudo-Maximum Likelihood Estimators," Annals of Economics and Statistics, GENES, issue 125-126, pages 187-218.
    19. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.
    20. Helen Caraveli & Ioannis Chatzigiatroudakis & Evangelos Paravalos, 2018. "Determinants of growth differences between Eastern and Southern EU countries: A panel-data approach," Working Papers 201803, Athens University Of Economics and Business, Department of Economics.
    21. Stelios Arvanitis, 2017. "Non-Emptyness of Stochastic Dominance Effiicient Sets via Stochastic Spanning," Working Papers 201710, Athens University Of Economics and Business, Department of Economics.
    22. Wang, Weining & Wooldridge, Jeffrey M. & Xu, Mengshan, 2020. "Improved Estimation of Dynamic Models of Conditional Means and Variances," IRTG 1792 Discussion Papers 2020-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    23. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    24. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    25. Szczygielski, Jan Jakub & Bwanya, Princess Rutendo & Charteris, Ailie & Brzeszczyński, Janusz, 2021. "The only certainty is uncertainty: An analysis of the impact of COVID-19 uncertainty on regional stock markets," Finance Research Letters, Elsevier, vol. 43(C).
    26. M. Angeles Carnero Fernández & Ana Pérez Espartero, 2018. "Outliers and misleading leverage effect in asymmetric GARCH-type models," Working Papers. Serie AD 2018-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    27. Chen, Min & Zhu, Ke, 2015. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," Journal of Econometrics, Elsevier, vol. 189(2), pages 313-320.
    28. Anatolyev Stanislav, 2019. "Volatility filtering in estimation of kurtosis (and variance)," Dependence Modeling, De Gruyter, vol. 7(1), pages 1-23, February.
    29. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    30. Aknouche, Abdelhakim & Al-Eid, Eid & Demouche, Nacer, 2016. "Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models," MPRA Paper 75770, University Library of Munich, Germany, revised 19 Dec 2016.
    31. Mo Zhou & Liang Peng & Rongmao Zhang, 2021. "Empirical likelihood test for the application of swqmele in fitting an arma‐garch model," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 222-239, March.
    32. George C. Bitros & M. Ishaq Nadiri, 2017. "Behavior of business investment in the USA under variable and proportional rates of replacement," Working Papers 201708, Athens University Of Economics and Business, Department of Economics.
    33. Gouriéroux, Christian & Monfort, Alain & Zakoian, Jean-Michel, 2017. "Pseudo-Maximum Likelihood and Lie Groups of Linear Transformations," MPRA Paper 79623, University Library of Munich, Germany.
    34. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    35. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    36. Gabriele Fiorentini & Enrique Sentana, 2018. "Consistent non-Gaussian pseudo maximum likelihood estimators," Working Paper series 18-06, Rimini Centre for Economic Analysis.
    37. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
    38. Christian Gouriéroux & Alain Monfort & Jean-Michel Zakoian, 2018. "Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations," Working Papers 2018-08, Center for Research in Economics and Statistics.
    39. Stefan Richter & Weining Wang & Wei Biao Wu, 2018. "A supreme test for periodic explosive GARCH," Papers 1812.03475, arXiv.org.
    40. Li, Dong & Ling, Shiqing & Zhu, Ke, 2016. "ZD-GARCH model: a new way to study heteroscedasticity," MPRA Paper 68621, University Library of Munich, Germany.
    41. Stefan Richter & Weining Wang & Wei Biao Wu, 2023. "Testing for parameter change epochs in GARCH time series," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 467-491.
    42. Huan Gong & Dong Li, 2020. "On the three‐step non‐Gaussian quasi‐maximum likelihood estimation of heavy‐tailed double autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 883-891, November.
    43. Abdeljalil Settar & Nadia Idrissi Fatmi & Mohammed Badaoui, 2021. "New Approach in Dealing with the Non-Negativity of the Conditional Variance in the Estimation of GARCH Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(1), pages 55-74, March.
    44. Szczygielski, Jan Jakub & Brzeszczyński, Janusz & Charteris, Ailie & Bwanya, Princess Rutendo, 2022. "The COVID-19 storm and the energy sector: The impact and role of uncertainty," Energy Economics, Elsevier, vol. 109(C).
    45. Donggyu Kim, 2016. "Statistical Inference for Unified Garch–Itô Models with High-Frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 513-532, July.
    46. Prono Todd, 2018. "Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
    47. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.
    48. Bitros, George C., 2017. "Germany and Greece: A mapping of their great divide and its EU implications," MPRA Paper 79039, University Library of Munich, Germany.
    49. Zhu, Ke, 2015. "Hausman tests for the error distribution in conditionally heteroskedastic models," MPRA Paper 66991, University Library of Munich, Germany.
    50. Richard T. A. Samuel & Charles Chimedza & Caston Sigauke, 2023. "Simulation Framework to Determine Suitable Innovations for Volatility Persistence Estimation: The GARCH Approach," JRFM, MDPI, vol. 16(9), pages 1-30, September.
    51. Natasha Miaouli & Panagiota Koliousi, 2018. "Efficient bargaining versus Right to manage in the era of liberalization," Working Papers 201804, Athens University Of Economics and Business, Department of Economics.
    52. Yining Chen, 2015. "Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 1-31, March.
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    Cited by:

    1. Chen, Richard Y. & Mykland, Per A., 2017. "Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data," Journal of Econometrics, Elsevier, vol. 200(1), pages 79-103.
    2. Kong, Xin-Bing & Liu, Zhi & Zhou, Wang, 2019. "A rank test for the number of factors with high-frequency data," Journal of Econometrics, Elsevier, vol. 211(2), pages 439-460.
    3. Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, vol. 42(C).
    4. Juncal Cunado & David Gabauer & Rangan Gupta, 2021. "Realized Volatility Spillovers between Energy and Metal Markets: A Time-Varying Connectedness Approach," Working Papers 202180, University of Pretoria, Department of Economics.
    5. Song, Xinyu & Kim, Donggyu & Yuan, Huiling & Cui, Xiangyu & Lu, Zhiping & Zhou, Yong & Wang, Yazhen, 2021. "Volatility analysis with realized GARCH-Itô models," Journal of Econometrics, Elsevier, vol. 222(1), pages 393-410.
    6. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.
    7. 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.
    8. Aït-Sahalia, Yacine & Xiu, Dacheng, 2019. "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, vol. 211(1), pages 176-205.
    9. Jianqing Fan & Yingying Li & Ke Yu, 2012. "Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
    10. IKEDA Shin Suke, 2017. "Illiquidity in the Japan Electric Power Exchange," Discussion papers 17122, Research Institute of Economy, Trade and Industry (RIETI).
    11. 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.
    12. Minseog Oh & Donggyu Kim, 2021. "Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective," Papers 2111.09655, arXiv.org.
    13. Markus Bibinger & Lars Winkelmann, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers SFB649DP2013-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Dmitry Levando & Maxim Sakharov, 2018. "Natural Instability of Equilibrium Prices," Working Papers 2018:01, Department of Economics, University of Venice "Ca' Foscari".
    15. 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.
    16. 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..
    17. Cai, T. Tony & Hu, Jianchang & Li, Yingying & Zheng, Xinghua, 2020. "High-dimensional minimum variance portfolio estimation based on high-frequency data," Journal of Econometrics, Elsevier, vol. 214(2), pages 482-494.
    18. Seifoddini , Jalal & Rahnamay Roodposhti , Fraydoon & Nikoomaram , Hashem, 2015. "Parametric Estimates of High Frequency Market Microstructure Noise as an Unsystematic Risk," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(4), pages 29-50, October.
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    21. Kirill Dragun & Kris Boudt & Orimar Sauri & Steven Vanduffel, 2021. "Beta-Adjusted Covariance Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1010, Ghent University, Faculty of Economics and Business Administration.
    22. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    23. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    24. 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.
    25. 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.
    26. 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.
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    29. Liu, Cheng & Tang, Cheng Yong, 2014. "A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data," Journal of Econometrics, Elsevier, vol. 180(2), pages 217-232.
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    33. Shen, Keren & Yao, Jianfeng & Li, Wai Keung, 2019. "On a spiked model for large volatility matrix estimation from noisy high-frequency data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 207-221.
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    36. Simon Clinet & Yoann Potiron, 2017. "Efficient asymptotic variance reduction when estimating volatility in high frequency data," Papers 1701.01185, arXiv.org, revised Jun 2018.
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    39. 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.
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Chapters

  1. Stefano Giglio & Yuan Liao & Dacheng Xiu, 2021. "Thousands of Alpha Tests," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3456, National Bureau of Economic Research, Inc.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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