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Bezirgen Veliyev

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. 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.

    Mentioned in:

    1. Machine Learning for Realized Volatility Forecasting
      by Francis Diebold in No Hesitations on 2021-02-01 12:16:00

Working papers

  1. Kim Christensen & Allan Timmermann & Bezirgen Veliyev, 2026. "Warp speed price moves: Jumps after earnings announcements," Papers 2601.08962, arXiv.org, revised Jan 2026.

    Cited by:

    1. Songrun He, 2026. "Interpretable Systematic Risk around the Clock," Papers 2604.13458, arXiv.org.
    2. Xinyue He & Ziran Li & Zhepeng Hu, 2025. "What the Night Tells the Day: Forecasting Realized Volatility in Chinese Commodity Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(12), pages 2332-2354, December.
    3. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Jesus Pantoja Cárdenas, 2026. "Long Memory and Asymmetric Uncertainty Effects on Stock Returns and Volatility: A Fractional Integration Approach," CESifo Working Paper Series 12806, CESifo.
    4. Xinyue He & Siyu Bian, 2025. "The Announcement Volatility Risk Premium in Agricultural Markets: Evidence From USDA Report Releases," Journal of Agricultural Economics, Wiley Blackwell, vol. 76(3), pages 651-665, September.

  2. Phillip Heiler & Asbj{o}rn Kaufmann & Bezirgen Veliyev, 2024. "Treatment Evaluation at the Intensive and Extensive Margins," Papers 2412.11179, arXiv.org, revised Aug 2026.

    Cited by:

    1. Gevorg Khandamiryan & Vira Semenova, 2026. "Adaptive Estimation of Aggregated Values of Conditional Linear Programs," Papers 2606.08359, arXiv.org.
    2. Yingying Dong & Phillip Heiler, 2026. "Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity," Papers 2606.09223, arXiv.org.

  3. 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.

    Cited by:

    1. Ali Rayeni & Hosein Naderi, 2025. "Predicting the Canadian Yield Curve Using Machine Learning Techniques," IJFS, MDPI, vol. 13(3), pages 1-30, September.
    2. Rehim Kılıç, 2025. "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series 2025-061, Board of Governors of the Federal Reserve System (U.S.).
    3. Minh Vo, 2025. "Measuring and Forecasting Stock Market Volatilities with High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3503-3544, June.
    4. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
    5. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Xie, Chi, 2025. "Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks," Journal of Empirical Finance, Elsevier, vol. 83(C).
    6. Dániel Léber & Balázs Egyed, 2026. "The Sentiment Augmented GARCH-LSTM Hybrid Model for Value-at-Risk Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 67(1), pages 313-353, January.
    7. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    8. Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.
    9. Alessio Brini, 2026. "Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks," Papers 2607.05291, arXiv.org.
    10. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    11. Alessio Brini & David A. Hsieh & Patrick Kuiper & Sean Moushegian & David Ye, 2025. "Empirical Models of the Time Evolution of SPX Option Prices," Papers 2506.17511, arXiv.org.
    12. Guangying Liu & Ziyan Zhuang & Min Wang, 2024. "Forecasting the high‐frequency volatility based on the LSTM‐HIT model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1356-1373, August.
    13. Anubha Goel & Puneet Pasricha & Juho Kanniainen, 2024. "Time-Series Foundation AI Model for Value-at-Risk Forecasting," Papers 2410.11773, arXiv.org, revised May 2025.
    14. Brini, Alessio & Toscano, Giacomo, 2025. "SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1093-1111.
    15. Radmir Mishelevich Leushuis & Nicolai Petkov, 2026. "Advances in forecasting realized volatility: a review of methodologies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-29, December.
    16. Shafqat Iqbal & Štefan Lyócsa, 2026. "A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1261-1291, April.
    17. Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    18. Martina Halouskov'a & v{S}tefan Ly'ocsa, 2025. "Forecasting U.S. equity market volatility with attention and sentiment to the economy," Papers 2503.19767, arXiv.org.
    19. Timothé Gronier & William Maréchal & Christophe Geissler & Stéphane Gibout, 2022. "Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control," Energies, MDPI, vol. 16(1), pages 1-17, December.
    20. Zhu, Ziyang & Zheng, Yuhao & Wang, Xinyi & Huang, Dasen & Feng, Lingbing, 2025. "Forecasting China's precious metal futures volatility: GBRT models and time-model dimension combination of Tree SHAP," International Review of Financial Analysis, Elsevier, vol. 104(PA).
    21. Chassot, Jonathan & Audrino, Francesco, 2026. "HARd to beat: The overlooked impact of rolling windows in the era of machine learning," International Journal of Forecasting, Elsevier, vol. 42(2), pages 330-343.
    22. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    23. Marie Corillon & Stephan Smeekes & Ines Wilms, 2026. "Sparse Tree-Based Aggregation for Time Series Regressions," Papers 2606.03665, arXiv.org, revised Jul 2026.
    24. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, 2026. "Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability," Working Papers wp61, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Feb 2026.
    25. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    26. Burak Korkusuz & Mehmet Sahiner, 2025. "Coin impact on cross-crypto realized volatility and dynamic cryptocurrency volatility connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-32, December.
    27. Zhou, Mingtao & Ma, Yong, 2025. "Climate risk and predictability of global stock market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 101(C).
    28. Yaxuan Kong & Yoontae Hwang & Marcus Kaiser & Chris Vryonides & Roel Oomen & Stefan Zohren, 2025. "Fusing Narrative Semantics for Financial Volatility Forecasting," Papers 2510.20699, arXiv.org.
    29. Natalia Roszyk & Robert 'Slepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers 2407.16780, arXiv.org.
    30. Uluc Aysun & Melanie Guldi, 2026. "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers 2026-01, University of Central Florida, Department of Economics.
    31. Talha Omer & Kristofer Månsson & Pär Sjölander & Gazi Salah Uddin, 2026. "Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(4), pages 1633-1651, July.
    32. Lihki Rubio & Adriana Palacio Pinedo & Adriana Mejía Castaño & Filipe Ramos, 2023. "Forecasting volatility by using wavelet transform, ARIMA and GARCH models," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 803-830, December.
    33. Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025. "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, vol. 98(C).
    34. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2025. "Volatility forecasting and volatility-timing strategies: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 75(C).
    35. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
    36. Chen, Wang & Chen, Zhu & Luo, Qin, 2025. "Predicting volatility in China's clean energy sector: Advantages of the carbon transition risk," Finance Research Letters, Elsevier, vol. 72(C).
    37. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
    38. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    39. John Kamwele Mutinda & Li Yong, 2026. "Decomposition-Ensemble Approach for Realized Volatility Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 68(1), pages 7-59, July.
    40. Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
    41. Chen, Ying & Kimura, Yosuke & Inoue, Kotaro, 2025. "How does managerial perception of uncertainty affect corporate investment during the COVID-19 pandemic: A text mining approach," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    42. Juan D. Díaz & Erwin Hansen & Gabriel Cabrera, 2025. "Forecasting the Volatility of US Oil and Gas Firms With Machine Learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1383-1402, July.
    43. Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
    44. Tenghan Zhong, 2026. "Risk-Sensitive Specialist Routing for Volatility Forecasting," Papers 2604.10402, arXiv.org, revised Jul 2026.
    45. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
    46. Fu, Tong & Huang, Dasen & Feng, Lingbing & Tang, Xiaoping, 2024. "More is better? The impact of predictor choice on the INE oil futures volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
    47. Luo, Qin & Lu, Xinjie & Huang, Dengshi & Zeng, Qing, 2024. "The impact of carbon transition risk concerns on stock market cycles: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    48. Lyócsa, Štefan & Todorova, Neda, 2024. "Forecasting of clean energy market volatility: The role of oil and the technology sector," Energy Economics, Elsevier, vol. 132(C).
    49. Lyócsa, Štefan & Todorova, Neda, 2024. "What drives the uranium sector risk? The role of attention, economic and geopolitical uncertainty," Energy Economics, Elsevier, vol. 140(C).
    50. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).
    51. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    52. Conrad, Christian & Kleen, Onno & Lönn, Rasmus, 2026. "Volatility forecasting for low-volatility investing," International Journal of Forecasting, Elsevier, vol. 42(2), pages 570-586.
    53. Patton, Andrew J. & Zhang, Haozhe, 2026. "Bespoke realized volatility: Tailored measures of risk for volatility prediction," Journal of Econometrics, Elsevier, vol. 254(PA).

  4. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "A GMM approach to estimate the roughness of stochastic volatility," Papers 2010.04610, arXiv.org, revised Jan 2026.

    Cited by:

    1. Rama Cont & Purba Das, 2024. "Rough Volatility: Fact or Artefact?," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 191-223, May.
    2. 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.
    3. Shi, Shuping & Yu, Jun & Zhang, Chen, 2024. "On the spectral density of fractional Ornstein–Uhlenbeck processes," Journal of Econometrics, Elsevier, vol. 245(1).
    4. Julien Guyon & Jordan Lekeufack, 2023. "Volatility is (mostly) path-dependent," Quantitative Finance, Taylor & Francis Journals, vol. 23(9), pages 1221-1258, September.
    5. Jinguan Lin & Yizhi Mao & Hongxia Hao & Guangying Liu, 2025. "Semiparametric Estimation and Application of Realized GARCH Model with Time-Varying Leverage Effect," Mathematics, MDPI, vol. 13(9), pages 1-26, May.
    6. Kim Christensen & Ulrich Hounyo & Zhi Liu, 2024. "A nonparametric test for diurnal variation in spot correlation processes," Papers 2408.02757, arXiv.org, revised Jan 2026.
    7. Bo Yuan & Damiano Brigo & Antoine Jacquier & Nicola Pede, 2024. "Deep learning interpretability for rough volatility," Papers 2411.19317, arXiv.org.
    8. Carsten H. Chong & Viktor Todorov, 2024. "A nonparametric test for rough volatility," Papers 2407.10659, arXiv.org.
    9. Angelini, Daniele & Bianchi, Sergio, 2023. "Nonlinear biases in the roughness of a Fractional Stochastic Regularity Model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    10. Ranieri Dugo & Giacomo Giorgio & Paolo Pigato, 2024. "Multivariate Rough Volatility," Papers 2412.14353, arXiv.org, revised May 2026.
    11. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    12. Bennedsen, Mikkel & Christensen, Kim & Christensen, Peter Korsbakke, 2026. "To be or not to be: Roughness or long memory in volatility?," Journal of Econometrics, Elsevier, vol. 254(PB).
    13. Mikkel Bennedsen & Kim Christensen & Peter Christensen, 2024. "To be or not to be: Roughness or long memory in volatility?," Papers 2403.12653, arXiv.org, revised Jan 2026.
    14. Li, Yicun & Teng, Yuanyang, 2023. "Statistical inference in discretely observed fractional Ornstein–Uhlenbeck processes," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    15. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Minimax Theory," Papers 2210.01214, arXiv.org, revised Feb 2024.
    16. Lechiheb, Atef, 2026. "Canonical Rough Path over Tempered Fractional Brownian Motion: Existence, Construction, and Applications," TSE Working Papers 26-1740, Toulouse School of Economics (TSE).
    17. Xiaohu Wang & Weilin Xiao & Jun Yu & Chen Zhang, 2025. "Maximum Likelihood Estimation of Fractional Ornstein-Uhlenbeck Process with Discretely Sampled Data," Working Papers 202527, University of Macau, Faculty of Business Administration.
    18. Ranieri Dugo & Giacomo Giorgio & Paolo Pigato, 2024. "The Multivariate Fractional Ornstein-Uhlenbeck Process," CEIS Research Paper 581, Tor Vergata University, CEIS, revised 28 Aug 2024.
    19. Peter Christensen, 2024. "Roughness Signature Functions," Papers 2401.02819, arXiv.org.
    20. Xiyue Han & Alexander Schied, 2025. "On the rate of convergence of estimating the Hurst parameter of rough stochastic volatility models," Papers 2504.09276, arXiv.org, revised Sep 2025.
    21. Johannes Muhle-Karbe & Youssef Ouazzani Chahdi & Mathieu Rosenbaum & Gr'egoire Szymanski, 2026. "A unified theory of order flow, market impact, and volatility," Papers 2601.23172, arXiv.org, revised Feb 2026.
    22. Xiyue Han & Alexander Schied, 2023. "Estimating the roughness exponent of stochastic volatility from discrete observations of the integrated variance," Papers 2307.02582, arXiv.org, revised Apr 2026.
    23. Markus Bibinger & Jun Yu & Chen Zhang, 2025. "Modeling and Forecasting Realized Volatility with Multivariate Fractional Brownian Motion," Papers 2504.15985, arXiv.org, revised Aug 2026.
    24. Mandasari, Putriesti & Luckstead, Jeff, 2025. "Examining the nexus between exporting status and CO2 productivity in Indonesian agri-based manufacturing," Energy Economics, Elsevier, vol. 143(C).
    25. Ofelia Bonesini & Antoine Jacquier & Alexandre Pannier, 2023. "Rough volatility, path-dependent PDEs and weak rates of convergence," Papers 2304.03042, arXiv.org, revised May 2026.
    26. Alexandre Pannier, 2023. "Path-dependent PDEs for volatility derivatives," Papers 2311.08289, arXiv.org, revised Jul 2025.
    27. 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 Jun 2024.
    28. Dugo, Ranieri & Giorgio, Giacomo & Pigato, Paolo, 2026. "The multivariate fractional Ornstein–Uhlenbeck process," Stochastic Processes and their Applications, Elsevier, vol. 192(C).
    29. Saad Mouti, 2023. "Rough volatility: evidence from range volatility estimators," Papers 2312.01426, arXiv.org, revised Sep 2024.

  5. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.

    Cited by:

    1. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    2. Kock, Anders Bredahl & Preinerstorfer, David, 2026. "Regularizing fairness in optimal policy learning with distributional targets," Journal of Econometrics, Elsevier, vol. 254(PB).
    3. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org, revised Apr 2025.

  6. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.

    Cited by:

    1. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org, revised Feb 2025.
    2. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    3. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
    4. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  7. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures," CREATES Research Papers 2020-12, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Blanka Horvath & Josef Teichmann & Žan Žurič, 2021. "Deep Hedging under Rough Volatility," Risks, MDPI, vol. 9(7), pages 1-20, July.
    2. Othmane Zarhali & Emmanuel Bacry & Jean-Franc{c}ois Muzy, 2026. "From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model," Papers 2601.10517, arXiv.org, revised Jun 2026.
    3. Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.
    4. Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Papers 2102.01962, arXiv.org.
    5. Wu, Peng & Muzy, Jean-François & Bacry, Emmanuel, 2022. "From rough to multifractal volatility: The log S-fBM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

  8. Kim Christensen & Martin Thyrsgaard & Bezirgen Veliyev, 2018. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," CREATES Research Papers 2018-19, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kim Christensen & Ulrich Hounyo & Zhi Liu, 2024. "A nonparametric test for diurnal variation in spot correlation processes," Papers 2408.02757, arXiv.org, revised Jan 2026.
    2. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2026. "A machine learning approach to volatility forecasting," Papers 2601.13014, arXiv.org.
    3. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    4. Bennedsen, Mikkel & Christensen, Kim & Christensen, Peter Korsbakke, 2026. "To be or not to be: Roughness or long memory in volatility?," Journal of Econometrics, Elsevier, vol. 254(PB).
    5. Mikkel Bennedsen & Kim Christensen & Peter Christensen, 2024. "To be or not to be: Roughness or long memory in volatility?," Papers 2403.12653, arXiv.org, revised Jan 2026.
    6. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    7. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
    8. Laurent, Sébastien & Renò, Roberto & Shi, Shuping, 2026. "Realized drift," Journal of Econometrics, Elsevier, vol. 254(PA).
    9. Yinfen Tang & Tao Su & Zhiyuan Zhang, 2022. "Distribution-free specification test for volatility function based on high-frequency data with microstructure noise," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(8), pages 977-1022, November.
    10. A. López-Pérez & M. Febrero-Bande & W. González-Manteiga, 2025. "Estimation and specification test for diffusion models with stochastic volatility," Statistical Papers, Springer, vol. 66(2), pages 1-36, February.

  9. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2018. "Functional Sequential Treatment Allocation," Papers 1812.09408, arXiv.org, revised Aug 2020.

    Cited by:

    1. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org, revised Feb 2025.
    2. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
    3. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    4. Toru Kitagawa & Jeff Rowley, 2024. "Bandit algorithms for policy learning: methods, implementation, and welfare-performance," The Japanese Economic Review, Springer, vol. 75(3), pages 407-447, July.
    5. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    6. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    7. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
    8. Kock, Anders Bredahl & Preinerstorfer, David, 2026. "Regularizing fairness in optimal policy learning with distributional targets," Journal of Econometrics, Elsevier, vol. 254(PB).
    9. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    12. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.

  10. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2018. "Edgeworth expansion for Euler approximation of continuous diffusion processes," CREATES Research Papers 2018-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ciprian A. Tudor & Nakahiro Yoshida, 2020. "Asymptotic expansion of the quadratic variation of a mixed fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 23(2), pages 435-463, July.
    2. Elisa Alòs & Masaaki Fukasawa, 2021. "The asymptotic expansion of the regular discretization error of Itô integrals," Mathematical Finance, Wiley Blackwell, vol. 31(1), pages 323-365, January.

  11. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. He, Lidan & Liu, Qiang & Liu, Zhi, 2020. "Edgeworth corrections for spot volatility estimator," Statistics & Probability Letters, Elsevier, vol. 164(C).
    2. 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.
    3. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    4. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    5. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.

  12. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Christensen, Kim & Timmermann, Allan & Veliyev, Bezirgen, 2025. "Warp speed price moves: Jumps after earnings announcements," Journal of Financial Economics, Elsevier, vol. 167(C).
    2. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    3. Christensen, Kim & Kolokolov, Aleksey, 2024. "An unbounded intensity model for point processes," Journal of Econometrics, Elsevier, vol. 244(1).
    4. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    5. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    6. Qianli Zhao & Chao Wang & Richard Gerlach & Giuseppe Storti & Lingxiang Zhang, 2024. "Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures," Papers 2411.17136, arXiv.org, revised Jul 2026.
    7. Takaki Hayashi & Yuta Koike, 2017. "Multi-scale analysis of lead-lag relationships in high-frequency financial markets," Papers 1708.03992, arXiv.org, revised May 2020.
    8. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2026. "Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment," Papers 2601.16613, arXiv.org.
    9. Mathias Vetter, 2021. "A universal approach to estimate the conditional variance in semimartingale limit theorems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1089-1125, December.
    10. Fabian Mies & Ansgar Steland, 2019. "Nonparametric Gaussian inference for stable processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(3), pages 525-555, October.
    11. 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.
    12. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.

  13. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2015. "Edgeworth expansion for the pre-averaging estimator," CREATES Research Papers 2015-60, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2018. "Edgeworth expansion for Euler approximation of continuous diffusion processes," CREATES Research Papers 2018-28, Department of Economics and Business Economics, Aarhus University.
    2. Yamagishi, Hayate & Yoshida, Nakahiro, 2023. "Order estimate of functionals related to fractional Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 161(C), pages 490-543.
    3. Ciprian A. Tudor & Nakahiro Yoshida, 2020. "Asymptotic expansion of the quadratic variation of a mixed fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 23(2), pages 435-463, July.
    4. Danial Saef & Odett Nagy & Sergej Sizov & Wolfgang Karl Härdle, 2024. "Understanding temporal dynamics of jumps in cryptocurrency markets: evidence from tick-by-tick data," Digital Finance, Springer, vol. 6(4), pages 605-638, December.
    5. Yoshida, Nakahiro, 2023. "Asymptotic expansion and estimates of Wiener functionals," Stochastic Processes and their Applications, Elsevier, vol. 157(C), pages 176-248.
    6. Tudor, Ciprian A. & Yoshida, Nakahiro, 2023. "High order asymptotic expansion for Wiener functionals," Stochastic Processes and their Applications, Elsevier, vol. 164(C), pages 443-492.
    7. Yamagishi, Hayate & Yoshida, Nakahiro, 2024. "Asymptotic expansion of the quadratic variation of fractional stochastic differential equation," Stochastic Processes and their Applications, Elsevier, vol. 175(C).

  14. Mathias Beiglbock & Walter Schachermayer & Bezirgen Veliyev, 2010. "A Direct Proof of the Bichteler--Dellacherie Theorem and Connections to Arbitrage," Papers 1004.5559, arXiv.org.

    Cited by:

    1. Christoph Kuhn & Bjorn Ulbricht, 2013. "Modeling capital gains taxes for trading strategies of infinite variation," Papers 1309.7368, arXiv.org, revised Jun 2015.
    2. Constantinos Kardaras, 2011. "On the closure in the Emery topology of semimartingale wealth-process sets," Papers 1108.0945, arXiv.org, revised Jul 2013.
    3. Dániel Ágoston Bálint & Martin Schweizer, 2019. "Properly Discounted Asset Prices Are Semimartingales," Swiss Finance Institute Research Paper Series 19-53, Swiss Finance Institute.
    4. Vladimir Vovk, 2012. "Continuous-time trading and the emergence of probability," Finance and Stochastics, Springer, vol. 16(4), pages 561-609, October.
    5. Kardaras, Constantinos, 2013. "On the closure in the Emery topology of semimartingale wealth-process sets," LSE Research Online Documents on Economics 44996, London School of Economics and Political Science, LSE Library.
    6. Dániel Ágoston Bálint & Martin Schweizer, 2018. "Making No-Arbitrage Discounting-Invariant: A New FTAP Beyond NFLVR and NUPBR," Swiss Finance Institute Research Paper Series 18-23, Swiss Finance Institute, revised Mar 2018.
    7. Christoph Kuhn & Alexander Molitor, 2020. "Semimartingale price systems in models with transaction costs beyond efficient friction," Papers 2001.03190, arXiv.org, revised Aug 2021.

Articles

  1. Christensen, Kim & Timmermann, Allan & Veliyev, Bezirgen, 2025. "Warp speed price moves: Jumps after earnings announcements," Journal of Financial Economics, Elsevier, vol. 167(C).
    See citations under working paper version above.
  2. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2024. "Functional Sequential Treatment Allocation With Covariates," Econometric Theory, Cambridge University Press, vol. 40(6), pages 1211-1252, December.
    See citations under working paper version above.
  3. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
    See citations under working paper version above.
  4. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    See citations under working paper version above.
  5. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
    See citations under working paper version above.
  6. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2022. "Functional Sequential Treatment Allocation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1311-1323, September.
    See citations under working paper version above.
  7. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
    See citations under working paper version above.
  8. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    See citations under working paper version above.
  9. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    See citations under working paper version above.
  10. Ulrich Hounyo & Bezirgen Veliyev, 2016. "Validity of Edgeworth expansions for realized volatility estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    See citations under working paper version above.
  11. Beiglböck, Mathias & Schachermayer, Walter & Veliyev, Bezirgen, 2012. "A short proof of the Doob–Meyer theorem," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1204-1209.

    Cited by:

    1. Neufeld, Ariel & Nutz, Marcel, 2014. "Measurability of semimartingale characteristics with respect to the probability law," Stochastic Processes and their Applications, Elsevier, vol. 124(11), pages 3819-3845.
    2. Christoph Kuhn, 2023. "The fundamental theorem of asset pricing with and without transaction costs," Papers 2307.00571, arXiv.org, revised Aug 2024.
    3. Beiglböck, M. & Siorpaes, P., 2014. "Riemann-integration and a new proof of the Bichteler–Dellacherie theorem," Stochastic Processes and their Applications, Elsevier, vol. 124(3), pages 1226-1235.
    4. Vasily Melnikov, 2025. "Limit Theorems for $$\sigma $$ σ -Localized Émery Convergence," Journal of Theoretical Probability, Springer, vol. 38(1), pages 1-25, March.
    5. Oleksii Mostovyi, 2015. "Necessary and sufficient conditions in the problem of optimal investment with intermediate consumption," Finance and Stochastics, Springer, vol. 19(1), pages 135-159, January.
    6. Oleksii Mostovyi, 2011. "Necessary and sufficient conditions in the problem of optimal investment with intermediate consumption," Papers 1107.5852, arXiv.org, revised Jul 2012.

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