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Asger Lunde

Citations

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Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.

    Mentioned in:

    1. A forecast comparison of volatility models: does anything beat a GARCH(1,1)? (Journal of Applied Econometrics 2005) in ReplicationWiki ()

Working papers

  1. Robert F. Engle & Martin Klint Hansen & Asger Lunde, 2012. "And Now, The Rest of the News: Volatility and Firm Specific News Arrival," CREATES Research Papers 2012-56, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Prajwal Eachempati & Praveen Ranjan Srivastava, 2021. "Accounting for unadjusted news sentiment for asset pricing," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 13(3), pages 383-422, May.
    2. James J. Forest, 2025. "The Effect of Macroeconomic Announcements on U.S. Treasury Markets: An Autometric General-to-Specific Analysis of the Greenspan Era," Econometrics, MDPI, vol. 13(3), pages 1-31, June.
    3. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    4. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    5. 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.

  2. Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    2. Konstantinos N. Baltas, 2025. "The nexus between bank efficiency and leverage," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 811-839, January.
    3. Chollete, Loran & Schmeidler, David, 2014. "Demand-Theoretic Approach to Choice of Priors," UiS Working Papers in Economics and Finance 2014/14, University of Stavanger.
    4. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    5. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    6. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    7. Xiaorui Zhu & Yichen Qin & Peng Wang, 2023. "Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models," Papers 2307.07574, arXiv.org, revised Jan 2025.
    8. Soudeep Deb & Sougata Deb, 2022. "An ensemble method for early prediction of dengue outbreak," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 84-101, January.
    9. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    10. Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers 2011.04577, arXiv.org, revised Apr 2023.
    11. Cakici, Nusret & Zaremba, Adam, 2024. "What drives stock returns across countries? Insights from machine learning models," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    12. Stanislav Anatolyev & Nikita Kobotaev, 2015. "Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage," Working Papers w0213, Center for Economic and Financial Research (CEFIR).
    13. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    14. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.
    15. Sakariyahu, Rilwan & Johan, Sofia & Lawal, Rodiat & Paterson, Audrey & Chatzivgeri, Eleni, 2023. "Dynamic connectedness between investors’ sentiment and asset prices: A comparison between major markets in Europe and USA," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    16. Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
    17. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    18. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    19. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2023. "Intergenerational actuarial fairness when longevity increases: Amending the retirement age," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 161-184.
    20. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    21. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    22. Grønborg, Niels S. & Lunde, Asger & Timmermann, Allan & Wermers, Russ, 2021. "Picking funds with confidence," Journal of Financial Economics, Elsevier, vol. 139(1), pages 1-28.
    23. Dong, Xin & Gong, Jinguo & Wang, Qin, 2025. "Environmental attention and the predictability of crude oil volatility: Evidence from a new MIDAS multifractal model," Energy Economics, Elsevier, vol. 143(C).
    24. Enzo D'Innocenzo & André Lucas & Anne Opschoor & Xingmin Zhang, 2024. "Heterogeneity and dynamics in network models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 150-173, January.
    25. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    26. Zhang, Xiaoyun & Guo, Qiang, 2024. "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, vol. 60(C).
    27. Eo, Yunjong & Kang, Kyu Ho, 2020. "The effects of conventional and unconventional monetary policy on forecasting the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    28. Wu, Xinyu & Zhao, An & Liu, Li, 2023. "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    29. Gong, Xue & Lai, Ping & He, Mengxi & Wen, Danyan, 2024. "Climate risk and energy futures high frequency volatility prediction," Energy, Elsevier, vol. 307(C).
    30. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    31. Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
    32. Cui, Yan & Feng, Yun, 2020. "Composite hedge and utility maximization for optimal futures hedging," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 15-32.
    33. Hartl, Tobias & Weigand, Roland, 2019. "Multivariate Fractional Components Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 38283, University of Regensburg, Department of Economics.
    34. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    35. Maksim Teterin & Anatoly Peresetsky, 2025. "Can Ethereum predict Bitcoin’s volatility?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 74-90.
    36. Geng, Qianjie & Wang, Yudong, 2024. "Forecasting the volatility of crude oil basis: Univariate models versus multivariate models," Energy, Elsevier, vol. 295(C).
    37. Wu, Hanlin & Li, Pan & Cao, Jiawei & Xu, Zijian, 2024. "Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model," Energy Economics, Elsevier, vol. 134(C).
    38. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    39. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    40. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2018. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," Econometrics and Statistics, Elsevier, vol. 5(C), pages 67-82.
    41. Bauwens, Luc & Xu, Yongdeng, 2023. "The contribution of realized covariance models to the economic value of volatility timing," LIDAM Discussion Papers CORE 2023018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    42. Lyócsa, Štefan & Tabaček, Jakub, 2026. "Attention to renewable energy: A risk-factor for stocks in the renewable energy sector," Research in International Business and Finance, Elsevier, vol. 81(C).
    43. Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021. "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 44-57.
    44. Lu, Fei & Ma, Feng & Bouri, Elie & Liao, Yin, 2024. "Do commodity futures have a steering effect on the spot stock market in China? New evidence from volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    45. Liu, Wei & Semeyutin, Artur & Lau, Chi Keung Marco & Gozgor, Giray, 2020. "Forecasting Value-at-Risk of Cryptocurrencies with RiskMetrics type models," Research in International Business and Finance, Elsevier, vol. 54(C).
    46. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    47. Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Working Papers No 11/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    48. Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019. "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, vol. 29(C), pages 193-199.
    49. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    50. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    51. Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
    52. Saiz, Lorena & Magro, Manuel Medina, 2025. "What can newspaper articles reveal about the euro area economy?," Working Paper Series 3122, European Central Bank.
    53. Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024. "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    54. Anna‐Lena Sachs & Michael Becker‐Peth & Stefan Minner & Ulrich W. Thonemann, 2022. "Empirical newsvendor biases: Are target service levels achieved effectively and efficiently?," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1839-1855, April.
    55. Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    56. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    57. Luc Bauwens & Guillaume Chevillon & Sébastien Laurent, 2023. "We modeled long memory with just one lag!," Post-Print hal-04185755, HAL.
    58. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    59. Li, Jian, 2025. "Predicting China's transportation sector volatility: Evidence from a new economic indicator," Finance Research Letters, Elsevier, vol. 84(C).
    60. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    61. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    62. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    63. Zhengyang Chi & Junbin Gao & Chao Wang, 2024. "Graph Signal Processing for Global Stock Market Realized Volatility Forecasting," Papers 2410.22706, arXiv.org, revised Sep 2025.
    64. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, April.
    65. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    66. Jan Ditzen & Erkal Ersoy & Haoyang Li & Francesco Ravazzolo, 2026. "Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning," Papers 2602.01963, arXiv.org.
    67. Royer, Julien, 2023. "Conditional asymmetry in Power ARCH(∞) models," Journal of Econometrics, Elsevier, vol. 234(1), pages 178-204.
    68. Caporin, Massimiliano & Velo, Gabriel G., 2015. "Realized range volatility forecasting: Dynamic features and predictive variables," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 98-112.
    69. 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).
    70. Lorenzo Lucchese & Mikko Pakkanen & Almut Veraart, 2022. "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective," Papers 2211.13777, arXiv.org, revised Oct 2023.
    71. Wang, Xiaohu & Xiao, Weilin & Yu, Jun, 2023. "Modeling and forecasting realized volatility with the fractional Ornstein–Uhlenbeck process," Journal of Econometrics, Elsevier, vol. 232(2), pages 389-415.
    72. Sichao He & Yansong Zhang, 2026. "Heads, Not Backbones: Output Heads Dominate Architectures on Fat-Tailed Returns," Papers 2606.30037, arXiv.org.
    73. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    74. Verena Monschang & Mark Trede & Bernd Wilfling, 2023. "Multi-horizon uniform superior predictive ability revisited: A size-exploiting and consistent test," CQE Working Papers 10623, Center for Quantitative Economics (CQE), University of Muenster.
    75. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2020. "Macroeconomic Data Transformations Matter," CIRANO Working Papers 2020s-42, CIRANO.
    76. Alessandra Amendola & Vincenzo Candila & Fabrizio Cipollini & Giampiero M. Gallo, 2020. "Doubly Multiplicative Error Models with Long- and Short-run Components," Papers 2006.03458, arXiv.org.
    77. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    78. Marie-Pier Bergeron Boucher & Søren Kjærgaard & James E. Oeppen & James W. Vaupel, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    79. Demetrio Lacava, 2026. "Shifting Correlations: How Trade Policy Uncertainty Alters stock-T bill Relationships," Papers 2603.25285, arXiv.org.
    80. Liu, Yuanyuan & Niu, Zibo & Suleman, Muhammad Tahir & Yin, Libo & Zhang, Hongwei, 2022. "Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework," Energy, Elsevier, vol. 238(PA).
    81. Barde, Sylvain, 2026. "Large-scale model comparison with fast model confidence sets," Journal of Econometrics, Elsevier, vol. 253(C).
    82. Bekierman, Jeremias & Manner, Hans, 2018. "Forecasting realized variance measures using time-varying coefficient models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 276-287.
    83. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    84. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    85. Degiannakis, Stavros, 2021. "Stock market as a nowcasting indicator for real investment," MPRA Paper 110914, University Library of Munich, Germany.
    86. 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).
    87. Caio Mário Mesquita & Cristiano Arbex Valle & Adriano César Machado Pereira, 2024. "Scenario Generation for Financial Data with a Machine Learning Approach Based on Realized Volatility and Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1879-1919, May.
    88. Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2022. "Semi-nonparametric risk assessment with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
    89. Andrew C. Chang & Phillip Li, 2018. "Measurement Error In Macroeconomic Data And Economics Research: Data Revisions, Gross Domestic Product, And Gross Domestic Income," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1846-1869, July.
    90. Nalban, Valeriu, 2018. "Forecasting with DSGE models: What frictions are important?," Economic Modelling, Elsevier, vol. 68(C), pages 190-204.
    91. Isabel Casas & Helena Veiga, 2021. "Exploring Option Pricing and Hedging via Volatility Asymmetry," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1015-1039, April.
    92. Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    93. Zhimin Wu & Guanghui Cai, 2024. "Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1956-1974, September.
    94. Alain-Philippe Fortin & Jean-Guy Simonato & Georges Dionne, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management.
    95. Joost Bosker & Marc Gürtler & Marvin Zöllner, 2025. "Machine learning-based variable selection for clustered credit risk modeling," Journal of Business Economics, Springer, vol. 95(4), pages 617-652, May.
    96. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
    97. Qu, Hui & Wang, Tianyang & Zhang, Yi & Sun, Pengfei, 2019. "Dynamic hedging using the realized minimum-variance hedge ratio approach – Examination of the CSI 300 index futures," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    98. Marina Friedrich & Karim Moussa & Yuliya Shapovalova & David van der Straten, 2025. "Forecasting Atmospheric Ethane: Application to the Jungfraujoch Measurement Station," Tinbergen Institute Discussion Papers 25-025/III, Tinbergen Institute.
    99. Mei, Dexiang & Zeng, Qing & Cao, Xiang & Diao, Xiaohua, 2019. "Uncertainty and oil volatility: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 155-163.
    100. Lee, Hsiang-Tai & Lee, Chien-Chiang, 2022. "A regime-switching real-time copula GARCH model for optimal futures hedging," International Review of Financial Analysis, Elsevier, vol. 84(C).
    101. Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    102. Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
    103. Bazhenov, Timofey & Fantazzini, Dean, 2019. "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility," MPRA Paper 93544, University Library of Munich, Germany.
    104. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    105. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    106. Guo, Lili & Cheng, Quanfeixue & He, Xiangyi & Su, Mengying & Li, Houjian, 2025. "Bioenergy market predictions using AI: Integrating climate change and green finance," Renewable Energy, Elsevier, vol. 250(C).
    107. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    108. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    109. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    110. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
    111. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German Car Sales Using Google Data and Multivariate Models," MPRA Paper 67110, University Library of Munich, Germany.
    112. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    113. Bermudez, P. de Zea & Marín, J. Miguel & Rue, Håvard & Veiga, Helena, 2024. "Integrated nested Laplace approximations for threshold stochastic volatility models," Econometrics and Statistics, Elsevier, vol. 30(C), pages 15-35.
    114. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    115. D. Schneller & S. Heiden & M. Heiden & A. Hamid, 2018. "Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility," German Economic Review, Verein für Socialpolitik, vol. 19(2), pages 209-236, May.
    116. Kawakami, Kei, 2013. "Conditional forecast selection from many forecasts: An application to the Yen/Dollar exchange rate," Journal of the Japanese and International Economies, Elsevier, vol. 28(C), pages 1-18.
    117. Li, Yan & Liang, Chao & Ma, Feng & Wang, Jiqian, 2020. "The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 36(C).
    118. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    119. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    120. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    121. Craig S Wright, 2026. "Utility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions," Papers 2601.07852, arXiv.org.
    122. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    123. Horváth, Matúš & Výrost, Tomáš, 2025. "No shortfall of ES estimators: Insights from cryptocurrency portfolios," Finance Research Letters, Elsevier, vol. 73(C).
    124. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    125. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    126. Naimoli, Antonio, 2022. "The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets," MPRA Paper 112588, University Library of Munich, Germany.
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    1163. Karine Constant & Marion Davin & Gilles de Truchis & Benjamin Keddad, 2024. "The European Renewable Energy Sector in Calm and Turmoil Periods: The Key Role of Sovereign Risk," Post-Print hal-04794038, HAL.
    1164. Rambaccussing, Dooruj & Kwiatkowski, Andrzej, 2020. "Forecasting with news sentiment: Evidence with UK newspapers," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1501-1516.
    1165. Mawuli Segnon & Bjorn Schulte-Tillmann & Riza Demirer & Rangan Gupta, 2025. "Deglobalization and Foreign Exchange Volatility: The Role of Supply Chain Pressures," Working Papers 202506, University of Pretoria, Department of Economics.
    1166. Li, Yifan & Nolte, Ingmar & Nolte, Sandra & Yu, Shifan, 2025. "Realized candlestick wicks," Journal of Econometrics, Elsevier, vol. 250(C).
    1167. Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023. "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    1168. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    1169. Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "Liquidity and realized range-based volatility forecasting: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1102-1113.
    1170. Qu, Hui & Li, Guo, 2023. "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, vol. 119(C).
    1171. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    1172. Qian, Lihua & Wang, Jiqian & Ma, Feng & Li, Ziyang, 2022. "Bitcoin volatility predictability–The role of jumps and regimes," Finance Research Letters, Elsevier, vol. 47(PB).
    1173. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    1174. Pirino, Davide & Pollastri, Alessandro & Trapin, Luca, 2025. "Testing liquidity: A statistical theory based on asset staleness," Econometrics and Statistics, Elsevier, vol. 35(C), pages 23-40.
    1175. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    1176. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    1177. Casas Villalba, Maria Isabel & Mao, Xiuping & Veiga, Helena, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de Estadística.
    1178. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.
    1179. 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).
    1180. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  3. Peter R. Hansen & Asger Lunde & Valeri Voev, 2010. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility," CREATES Research Papers 2010-74, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    3. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    5. Peter Christoffersen & Mathieu Fournier & Kris Jacobs, 2013. "The Factor Structure in Equity Options," CREATES Research Papers 2013-47, Department of Economics and Business Economics, Aarhus University.
    6. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Asger Lunde & Kasper V. Olesen, 2014. "Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange," CREATES Research Papers 2013-19, Department of Economics and Business Economics, Aarhus University.
    8. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).
    9. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    10. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    11. Anke D. Leroux & Vance L. Martin & Kathryn A. St. John, 2022. "Modeling time varying risk of natural resource assets: Implications of climate change," Quantitative Economics, Econometric Society, vol. 13(1), pages 225-257, January.
    12. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    13. Kevin Sheppard & Wen Xu, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    14. Xin Jin & Jia Liu & Qiao Yang, 2021. "Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach," Econometrics, MDPI, vol. 9(4), pages 1-22, December.
    15. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  4. Peter R. Hansen & Asger Lunde, 2010. "Estimating the Persistence and the Autocorrelation Function of a Time Series that is Measured with Error," CREATES Research Papers 2010-08, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Z. Merrick Li & Oliver Linton, 2022. "A ReMeDI for Microstructure Noise," Econometrica, Econometric Society, vol. 90(1), pages 367-389, January.
    2. Kim, Jihyun & Meddahi, Nour, 2020. "Volatility Regressions with Fat Tails," TSE Working Papers 20-1097, Toulouse School of Economics (TSE).
    3. Francis X. Diebold & Kamil Yilmaz, 2011. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Working Papers 11-45, Federal Reserve Bank of Philadelphia.
    4. Pedersen, Lasse Heje & Bollerslev, Tim & Hood, Benjamin & Huss, John, 2018. "Risk Everywhere: Modeling and Managing Volatility," CEPR Discussion Papers 12687, Centre for Economic Policy Research.
    5. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    6. Dinghai Xu, 2019. "A Study on Volatility Spurious Almost Integration Effect: A Threshold Realized GARCH Approach," Working Papers 1903, University of Waterloo, Department of Economics, revised Dec 2019.
    7. Gabriele La Spada & Fabrizio Lillo, 2011. "The effect of round-off error on long memory processes," Papers 1107.4476, arXiv.org, revised Mar 2013.
    8. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2022. "Option pricing with state‐dependent pricing kernel," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1409-1433, August.
    9. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    10. Kim, Jihyun & Meddahi, Nour, 2020. "Volatility regressions with fat tails," Journal of Econometrics, Elsevier, vol. 218(2), pages 690-713.
    11. Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Estimation of Long Memory in Integrated Variance," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 785-814, October.
    12. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    13. Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Indirect inference with time series observed with error," CREATES Research Papers 2014-57, Department of Economics and Business Economics, Aarhus University.
    14. 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.
    15. Matthias Jonas & Piotr Żebrowski, 2019. "The crux with reducing emissions in the long-term: The underestimated “now” versus the overestimated “then”," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 1169-1190, August.
    16. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    17. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2025. "Robust Cauchy-Based Methods for Predictive Regressions," Papers 2511.09249, arXiv.org, revised Apr 2026.
    18. Christian M. Hafner & Arie Preminger, 2016. "The effect of additive outliers on a fractional unit root test," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 401-420, October.
    19. 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.
    20. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    21. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).
    22. Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
    23. Ranaldo, Angelo & de Magistris, Paolo Santucci, 2022. "Liquidity in the global currency market," Journal of Financial Economics, Elsevier, vol. 146(3), pages 859-883.
    24. Yunfei Chen & Wei Jiang, 2024. "Time and frequency volatility spillovers among commodities: Evidence from pre and during the Russia-Ukraine war," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(2), pages 249-273, May.
    25. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    26. Shin, Minseok & Kim, Donggyu & Wang, Yazhen & Fan, Jianqing, 2025. "Factor and idiosyncratic VAR volatility matrix models for heavy-tailed high-frequency financial observations," Journal of Econometrics, Elsevier, vol. 252(PA).
    27. Yanqi Gong & Chunyi Wang & Hongxuan Fu & Sandylove Afrane & Pingjian Yang & Jian-Lin Chen & Guozhu Mao, 2025. "Spatiotemporal Analysis and Prediction of Avian Migration Under Climate Change," Sustainability, MDPI, vol. 17(7), pages 1-27, March.
    28. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    29. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    30. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    31. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "Realized semibetas: Disentangling “good” and “bad” downside risks," Journal of Financial Economics, Elsevier, vol. 144(1), pages 227-246.
    32. Diebold, Francis X. & Yılmaz, Kamil, 2023. "Reprint of: On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 234(S), pages 70-90.
    33. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    34. Rasmus T. Varneskov & Pierre Perron, 2015. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series wp2015-015, Boston University - Department of Economics.
    35. Onsurang Norrbin & Aaron D. Smallwood, 2011. "Mean Reversion in the Real Interest Rate and the Effects of Calculating Expected Inflation," Southern Economic Journal, John Wiley & Sons, vol. 78(1), pages 107-130, July.
    36. Preve, Daniel, 2015. "Linear programming-based estimators in nonnegative autoregression," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 225-234.
    37. Jihyun Kim & Nour Meddahi, 2020. "Volatility Regressions with Fat Tails," Post-Print hal-03142647, HAL.
    38. 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.
    39. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.

  5. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Manabu Asai & Michael McAleer, 2015. "The Impact of Jumps and Leverage in Forecasting Co-Volatility," Documentos de Trabajo del ICAE 2015-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Stanislav Anatolyev & Nikita Kobotaev, 2015. "Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage," Working Papers w0213, Center for Economic and Financial Research (CEFIR).
    4. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    5. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    6. Lam, Clifford & Feng, Phoenix & Hu, Charlie, 2017. "Nonlinear shrinkage estimation of large integrated covariance matrices," LSE Research Online Documents on Economics 69812, London School of Economics and Political Science, LSE Library.
    7. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    9. Serge Darolles & Christian Gouriéroux & Emmanuelle Jay, 2012. "Robust Portfolio Allocation with Systematic Risk Contribution Restrictions," Working Papers 2012-35, Center for Research in Economics and Statistics.
    10. Bauwens, Luc & Xu, Yongdeng, 2023. "The contribution of realized covariance models to the economic value of volatility timing," LIDAM Discussion Papers CORE 2023018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Manabu Asai & Mike K. P. So, 2021. "Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 271-294, May.
    12. Ilze Kalnina & Natalia Sizova, 2015. "Estimation of volatility measures using high frequency data (in Russian)," Quantile, Quantile, issue 13, pages 3-14, May.
    13. Flavia Barsotti & Simona Sanfelici, 2012. "Microstructure effect on firm’s volatility risk," Working Papers - Mathematical Economics 2012-05, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    14. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    15. Connor, Gregory & Suurlaht, Anita, 2013. "Dynamic stock market covariances in the Eurozone," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 353-370.
    16. Nabil Bouamara & Kris Boudt & Sébastien Laurent & Christopher J. Neely, 2024. "Sluggish news reactions: A combinatorial approach for synchronizing stock jumps," Working Papers 2024-006, Federal Reserve Bank of St. Louis, revised 06 Jul 2026.
    17. Flavia Barsotti & Simona Sanfelici, 2016. "Market Microstructure Effects on Firm Default Risk Evaluation," Econometrics, MDPI, vol. 4(3), pages 1-31, July.
    18. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    19. Wang, Chengyang & Nishiyama, Yoshihiko, 2015. "Volatility forecast of stock indices by model averaging using high-frequency data," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 324-337.
    20. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    21. Dinghai Xu, 2019. "A Study on Volatility Spurious Almost Integration Effect: A Threshold Realized GARCH Approach," Working Papers 1903, University of Waterloo, Department of Economics, revised Dec 2019.
    22. 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).
    23. Maria Elvira Mancino & Simona Sanfelici, 2012. "Estimation of quarticity with high-frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 607-622, December.
    24. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
    25. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2013. "Conditional alphas and realized betas," Textos para discussão 341, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    26. Qu, Hui & Wang, Tianyang & Zhang, Yi & Sun, Pengfei, 2019. "Dynamic hedging using the realized minimum-variance hedge ratio approach – Examination of the CSI 300 index futures," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    27. Oh, Minseog & Kim, Donggyu & Wang, Yazhen, 2026. "Robust realized integrated beta estimator with application to dynamic analysis of integrated beta," Journal of Econometrics, Elsevier, vol. 254(PA).
    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. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    30. Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
    31. Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
    32. Asai Manabu & So Mike K.P., 2015. "Long Memory and Asymmetry for Matrix-Exponential Dynamic Correlation Processes," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 69-94, January.
    33. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    34. Bohan Zhao & Hong Yin & Yonghong Long, 2024. "An Advanced Time-Varying Capital Asset Pricing Model via Heterogeneous Autoregressive Framework: Evidence from the Chinese Stock Market," Mathematics, MDPI, vol. 13(1), pages 1-14, December.
    35. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    36. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2012. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and Covolatility," Global COE Hi-Stat Discussion Paper Series gd12-269, Institute of Economic Research, Hitotsubashi University.
    37. Yasin Simsek, 2025. "Spot Regressions with Candlesticks," Papers 2510.12911, arXiv.org, revised Apr 2026.
    38. Mushtaq, Hina & Ishtiaq, Muhammad & Jamal, Surayya & Raza Rizvi, Syed Maisam & Raza, Hamad, 2026. "Regime-Switching volatility and risk quantification in South Asian and developed stock Markets: A Comparative perspective using Markov-Switching GARCH with MLE and MCMC estimations," The North American Journal of Economics and Finance, Elsevier, vol. 82(C).
    39. Matteo Bonato & Luca Taschini, 2016. "Comovement and the financialization of commodities," GRI Working Papers 215, Grantham Research Institute on Climate Change and the Environment.
    40. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    41. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    42. Naoto Kunitomo & Hiroumi Misaki & Seisho Sato, 2015. "The SIML Estimation of Integrated Covariance and Hedging Coefficient Under Round-off Errors, Micro-market Price Adjustments and Random Sampling," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(3), pages 333-368, September.
    43. Xin-Bing Kong, 2013. "A direct approach to risk approximation for vast portfolios under gross-exposure constraint using high-frequency data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 647-669, November.
    44. Naoto Kunitomo & Hiroumi Misaki & Seisho Sato, 2015. "The SIML Estimation of Integrated Covariance and Hedging Coefficient under Round-off Errors, Micro-market Price Adjustments and Random Sampling," CIRJE F-Series CIRJE-F-965, CIRJE, Faculty of Economics, University of Tokyo.
    45. Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," Tinbergen Institute Discussion Papers 13-003/III, Tinbergen Institute.
    46. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    47. Peter Reinhard Hansen & Zhuo Huang, 2012. "Exponential GARCH Modeling with Realized Measures of Volatility," CREATES Research Papers 2012-44, Department of Economics and Business Economics, Aarhus University.
    48. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    49. Boudt, Kris & Dragun, Kirill & Sauri, Orimar & Vanduffel, Steven, 2023. "ETF Basket-Adjusted Covariance estimation," Journal of Econometrics, Elsevier, vol. 235(2), pages 1144-1171.
    50. Ang, Andrew & Kristensen, Dennis, 2012. "Testing conditional factor models," Journal of Financial Economics, Elsevier, vol. 106(1), pages 132-156.
    51. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    52. Bibinger, Markus & Reiß, Markus, 2011. "Spectral estimation of covolatility from noisy observations using local weights," SFB 649 Discussion Papers 2011-086, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    2. Flavia Barsotti & Simona Sanfelici, 2012. "Microstructure effect on firm’s volatility risk," Working Papers - Mathematical Economics 2012-05, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    3. Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    4. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Oct 2025.
    5. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    6. Corradi, Valentina & Distaso, Walter & Swanson, Norman R., 2009. "Predictive density estimators for daily volatility based on the use of realized measures," Journal of Econometrics, Elsevier, vol. 150(2), pages 119-138, June.
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    9. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
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    20. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
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    23. Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
    24. Gorgi, P. & Koopman, S.J., 2023. "Beta observation-driven models with exogenous regressors: A joint analysis of realized correlation and leverage effects," Journal of Econometrics, Elsevier, vol. 237(2).
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    26. Mancino, M.E. & Sanfelici, S., 2008. "Robustness of Fourier estimator of integrated volatility in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2966-2989, February.
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    651. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.
    652. Awartani, Basel & Maghyereh, Aktham, 2025. "The value of cross market volatility in improving the forecast accuracy of risk in the gold, the dollar and the oil futures markets," Finance Research Letters, Elsevier, vol. 83(C).
    653. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    654. Qiang Liu & Zhi Liu & Chuanhai Zhang, 2020. "Heteroscedasticity test of high-frequency data with jumps and microstructure noise," Papers 2010.07659, arXiv.org.
    655. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.
    656. Maria Elvira Mancino & Simona Sanfelici, 2011. "Estimation of Quarticity with High Frequency Data," Working Papers - Mathematical Economics 2011-06, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, revised Jan 2012.
    657. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.
    658. Jozef Barunik & Lukas Vacha, 2012. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Papers 1202.1854, arXiv.org, revised Feb 2013.
    659. Cathy W. S. Chen & Edward M. H. Lin & Tara F. J. Huang, 2022. "Bayesian quantile forecasting via the realized hysteretic GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1317-1337, November.
    660. Kim Christensen & Roel Oomen & Roberto Renò, 2018. "The drift burst hypothesis," CREATES Research Papers 2018-21, Department of Economics and Business Economics, Aarhus University.
    661. Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
    662. Gustavo Fruet Dias & Cristina M. Scherrer & Fotis Papailias, 2016. "Volatility Discovery," CREATES Research Papers 2016-07, Department of Economics and Business Economics, Aarhus University.
    663. Marco Flaccadoro & Stefania Villa, 2025. "Global risk aversion and the term premium gap in emerging market economies," Temi di discussione (Economic working papers) 1493, Bank of Italy, Economic Research and International Relations Area.
    664. Cui, Wenhao & Hu, Jie & Wang, Jiandong, 2026. "Reprint of: Nonparametric estimation for high-frequency data incorporating trading information," Journal of Econometrics, Elsevier, vol. 254(PA).
    665. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
    666. Roland Füss & Ferdinand Mager & Michael Stein & Lu Zhao, 2018. "Financial crises, price discovery, and information transmission: a high-frequency perspective," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 333-365, November.
    667. Chai, Edwina F.L. & Lee, Adrian D. & Wang, Jianxin, 2015. "Global information distribution in the gold OTC markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 206-217.
    668. Silvia Goncalves & Nour Meddahi, 2008. "Edgeworth Corrections for Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 139-162.
    669. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    670. Maria Elvira Mancino & Maria Cristina Recchioni, 2015. "Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-33, September.
    671. 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.
    672. Selma Chaker & Nour Meddahi, 2013. "Volatility Forecasting when the Noise Variance Is Time-Varying," Staff Working Papers 13-48, Bank of Canada.
    673. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    674. Heejoon Han & Myung D. Park, 2013. "Comparison of Realized Measure and Implied Volatility in Forecasting Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 522-533, September.
    675. Zu, Y., 2015. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Working Papers 15/02, Department of Economics, City St George's, University of London.
    676. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    677. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    678. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    679. Yuqing Feng & Yaojie Zhang & Yudong Wang, 2024. "Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 567-582, April.
    680. Park, Sujin & Hong, Seok Young & Linton, Oliver, 2016. "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error," Journal of Econometrics, Elsevier, vol. 191(2), pages 325-347.
    681. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    682. PASCUAL, Roberto & VEREDAS, David, 2006. "Does the open limit order book matter in explaining long run volatility ?," LIDAM Discussion Papers CORE 2006110, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    683. Tim Bollerslev & Viktor Todorov, 2010. "Estimation of Jump Tails," CREATES Research Papers 2010-16, Department of Economics and Business Economics, Aarhus University.
    684. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    685. Giampiero M. Gallo & Edoardo Otranto, 2016. "Combining Markov Switching and Smooth Transition in Modeling Volatility: A Fuzzy Regime MEM," Econometrics Working Papers Archive 2016_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    686. Markus Reiss, 2010. "Asymptotic equivalence and sufficiency for volatility estimation under microstructure noise," Papers 1001.3006, arXiv.org.
    687. Naoto Kunitomo & Hiroumi Misaki, 2013. "The SIML Estimation of Integrated Covariance and Hedging Coefficient under Micro-market noise and Random Sampling," CIRJE F-Series CIRJE-F-893, CIRJE, Faculty of Economics, University of Tokyo.
    688. Li, Yingying & Liu, Guangying & Zhang, Zhiyuan, 2022. "Volatility of volatility: Estimation and tests based on noisy high frequency data with jumps," Journal of Econometrics, Elsevier, vol. 229(2), pages 422-451.
    689. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    690. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    691. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    692. Todorova, Neda & Souček, Michael, 2014. "Overnight information flow and realized volatility forecasting," Finance Research Letters, Elsevier, vol. 11(4), pages 420-428.
    693. Ngo Hoang-Long & Ogawa Shigeyoshi, 2009. "A central limit theorem for the functional estimation of the spot volatility," Monte Carlo Methods and Applications, De Gruyter, vol. 15(4), pages 353-380, January.
    694. Qu, Hui & Li, Guo, 2023. "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, vol. 119(C).
    695. Liu, Cheng & Wang, Moming & Xia, Ningning, 2022. "Design-free estimation of integrated covariance matrices for high-frequency data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    696. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    697. Yang, Minxian, 2019. "The risk return relationship: Evidence from index returns and realised variances," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    698. Fleming, Jeff & Paye, Bradley S., 2011. "High-frequency returns, jumps and the mixture of normals hypothesis," Journal of Econometrics, Elsevier, vol. 160(1), pages 119-128, January.

  8. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2005. "Model confidence sets for forecasting models," FRB Atlanta Working Paper 2005-07, Federal Reserve Bank of Atlanta.

    Cited by:

    1. Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
    2. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    3. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    4. Davide De Gaetano, 2016. "Forecast Combinations For Realized Volatility In Presence Of Structural Breaks," Departmental Working Papers of Economics - University 'Roma Tre' 0208, Department of Economics - University Roma Tre.
    5. Michiel de Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," Working Paper 2010/01, Norges Bank.
    6. Caporin, M. & McAleer, M.J., 2010. "Ranking multivariate GARCH models by problem dimension," Econometric Institute Research Papers EI 2010-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    8. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    9. Jörg Döpke & Ulrich Fritsche, 2006. "Growth and inflation forecasts for Germany a panel-based assessment of accuracy and efficiency," Empirical Economics, Springer, vol. 31(3), pages 777-798, September.
    10. He, Xiangyi & Li, Yiwei & Li, Houjian, 2024. "Revolutionizing Bitcoin price forecasts: A comparative study of advanced hybrid deep learning architectures," Finance Research Letters, Elsevier, vol. 69(PA).
    11. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," FRB Atlanta Working Paper 2003-28, Federal Reserve Bank of Atlanta.
    12. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    13. James M. Nason & Gregor W. Smith, 2005. "Identifying The New Keynesian Phillips Curve," Working Paper 1026, Economics Department, Queen's University.
    14. McAleer, Michael & Jimenez-Martin, Juan-Angel & Perez-Amaral, Teodosio, 2013. "GFC-robust risk management strategies under the Basel Accord," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 97-111.
    15. Stéphane Goutte & Klemens Klotzner & Hoang Viet Le & Hans Jörg von Mettenheim, 2024. "Forecasting photovoltaic production with neural networks and weather features," Post-Print hal-04779953, HAL.
    16. Andrew J. Patton & Kevin Sheppard, 2008. "Evaluating Volatility and Correlation Forecasts," OFRC Working Papers Series 2008fe22, Oxford Financial Research Centre.
    17. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    18. Shuqin Zhang & Zhuoya Li & Lijiao Jing & Xinmin Li, 2025. "Nowcasting Monthly Chinese GDP with Mixed Frequency Data: A Model Averaging Approach," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 4493-4511, December.
    19. Christian Dreger & Konstantin A. Kholodilin, 2010. "Forecasting Private Consumption by Consumer Surveys," Discussion Papers of DIW Berlin 1066, DIW Berlin, German Institute for Economic Research.
    20. Caporin, M. & McAleer, M.J., 2010. "Model Selection and Testing of Conditional and Stochastic Volatility Models," Econometric Institute Research Papers EI 2010-57, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Joseph P & Romano & Azeem M. Shaikh & Michael Wolf, 2005. "Formalized Data Snooping Based on Generalized Error Rates," IEW - Working Papers 259, Institute for Empirical Research in Economics - University of Zurich.

  9. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2005. "Testing the significance of calendar effects," FRB Atlanta Working Paper 2005-02, Federal Reserve Bank of Atlanta.

    Cited by:

    1. Chowdhury, Anup & Uddin, Moshfique & Anderson, Keith, 2022. "Trading behaviour and market sentiment: Firm-level evidence from an emerging Islamic market," Global Finance Journal, Elsevier, vol. 53(C).
    2. Cho, Young-Hyun & Linton, Oliver & Whang, Yoon-Jae, 2007. "Are there Monday effects in stock returns: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 736-755, December.
    3. Hudson, Robert S. & Gregoriou, Andros, 2015. "Calculating and comparing security returns is harder than you think: A comparison between logarithmic and simple returns," International Review of Financial Analysis, Elsevier, vol. 38(C), pages 151-162.
    4. Dumitriu, Ramona & Stefanescu, Razvan & Nistor, Costel, 2012. "The Halloween effect during quiet and turbulent times," MPRA Paper 41539, University Library of Munich, Germany, revised 25 Sep 2012.
    5. Guglielmo Maria Caporale & Alex Plastun, 2016. "Calendar Anomalies in the Ukrainian Stock Market," Discussion Papers of DIW Berlin 1573, DIW Berlin, German Institute for Economic Research.
    6. Boubaker, Sabri & Essaddam, Naceur & Nguyen, Duc Khuong & Saadi, Samir, 2017. "On the robustness of week-day effect to error distributional assumption: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 114-130.
    7. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    8. Atanasova, Christina V. & Hudson, Robert S., 2010. "Technical trading rules and calendar anomalies -- Are they the same phenomena?," Economics Letters, Elsevier, vol. 106(2), pages 128-130, February.
    9. Tsiakas, Ilias, 2008. "Overnight information and stochastic volatility: A study of European and US stock exchanges," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 251-268, February.
    10. Dichtl, Hubert & Drobetz, Wolfgang, 2014. "Are stock markets really so inefficient? The case of the “Halloween Indicator”," Finance Research Letters, Elsevier, vol. 11(2), pages 112-121.
    11. Chatzitzisi, Evanthia & Fountas, Stilianos & Panagiotidis, Theodore, 2021. "Another look at calendar anomalies," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 823-840.
    12. Ayman Abdalmajeed Ahmad Al-Smadi & Mahmoud Khalid Almsafir & Nur Hanis Hazwani Binti Husni, 2018. "Trends And Calendar Effects In Malaysia’S Stock Market," Romanian Economic Business Review, Romanian-American University, vol. 13(2), pages 15-22, June.
    13. Dumitriu, Ramona & Stefanescu, Razvan & Nistor, Costel, 2012. "Holiday effects during quiet and turbulent times," MPRA Paper 41625, University Library of Munich, Germany, revised 07 Mar 2012.
    14. John C. Frain, 2008. "Maximum Likelihood Estimates of Regression Coefficients with alpha-stable residuals and Day of Week effects in Total Returns on Equity Indices," Trinity Economics Papers tep0108, Trinity College Dublin, Department of Economics, revised May 2008.
    15. Alt, Raimund & Fortin, Ines & Weinberger, Simon, 2011. "The Monday effect revisited: An alternative testing approach," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 447-460, June.
    16. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
    17. Apergis, Nicholas & Gupta, Rangan, 2017. "Can (unusual) weather conditions in New York predict South African stock returns?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 377-386.

  10. Asger Lunde & Peter Reinhard Hansen, 2004. "Realized Variance and IID Market Microstructure Noise," Econometric Society 2004 North American Summer Meetings 526, Econometric Society.

    Cited by:

    1. Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Inference for Integrated Volatility," Departmental Working Papers 200616, Rutgers University, Department of Economics.
    2. Veiga, Helena, 2006. "Volatility forecasts: a continuous time model versus discrete time models," DES - Working Papers. Statistics and Econometrics. WS ws062509, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Lakshmi Padmakumari & S. Maheswaran, 2018. "Covariance estimation using random permutations," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    4. Jeremy Large, 2007. "Estimating Quadratic Variation When Quoted Prices Change by a Constant Increment," Economics Series Working Papers 340, University of Oxford, Department of Economics.
    5. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
    6. Delia-Elena Diaconaşu, 2015. "CENTRAL AND EASTERN EUROPEAN STOCK MARKETS IN TIMES OF CRISIS (International Conference "Recent Advances in Economic and Social Research", 13-14 mai 2015, București)," Institute for Economic Forecasting Conference Proceedings 151205, Institute for Economic Forecasting.
    7. Mark Podolskij & Mathias Vetter, 2007. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," CREATES Research Papers 2007-27, Department of Economics and Business Economics, Aarhus University.
    8. de Vilder, Robin G. & Visser, Marcel P., 2007. "Volatility Proxies for Discrete Time Models," MPRA Paper 4917, University Library of Munich, Germany.
    9. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    10. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics.
    11. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    12. Jeremy Large, 2005. "Estimating quadratic variation when quoted prices jump by a constant increment," Economics Papers 2005-W05, Economics Group, Nuffield College, University of Oxford.
    13. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    14. Imane El Ouadghiri & Remzi Uctum, 2015. "Jumps in Equilibrium Prices and Asymmetric News in Foreign Exchange Markets," EconomiX Working Papers 2015-14, University of Paris Nanterre, EconomiX.
    15. Mark Podolskij & Daniel Ziggel, 2007. "A Range-Based Test for the Parametric Form of the Volatility in Diffusion Models," CREATES Research Papers 2007-26, Department of Economics and Business Economics, Aarhus University.
    16. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2003. "Realized Beta: Persistence and Predictability," PIER Working Paper Archive 04-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Mar 2004.
    17. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007-23, Christian-Albrechts-University of Kiel, Department of Economics.
    18. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Accuracy Of Integrated Volatility Estimators," Working Paper 1225, Economics Department, Queen's University.
    19. Mark Podolskij & Daniel Ziggel, 2008. "New tests for jumps: a threshold-based approach," CREATES Research Papers 2008-34, Department of Economics and Business Economics, Aarhus University.
    20. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
    21. Elena Pelinescu & Delia-Elena Diacona?u, 2015. "The Volatility of Romanian Exchange Rate: A GARCH Approach," Review of Economics & Finance, Better Advances Press, Canada, vol. 5, pages 92-99, November.
    22. Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2005. "Edgeworth Expansions for Realized Volatility and Related Estimators," NBER Technical Working Papers 0319, National Bureau of Economic Research, Inc.
    23. Gael M. Martin & Andrew Reidy & Jill Wright, 2006. "Assessing the Impact of Market Microstructure Noise and Random Jumps on the Relative Forecasting Performance of Option-Implied and Returns-Based Volatility," Monash Econometrics and Business Statistics Working Papers 10/06, Monash University, Department of Econometrics and Business Statistics.
    24. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2004. "Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise," Economics Papers 2004-W28, Economics Group, Nuffield College, University of Oxford.
    25. Orregaard Nielsen, Morten & Houmann Frederiksen, Per, 2005. "Finite Sample Accuracy of Integrated Volatility Estimators," Queen's Economics Department Working Papers 273721, Queen's University - Department of Economics.
    26. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," Economics Papers 2005-W06, Economics Group, Nuffield College, University of Oxford.
    27. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2007. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," International Finance Discussion Papers 905, Board of Governors of the Federal Reserve System (U.S.).
    28. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
    29. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Papers 2006-W03, Economics Group, Nuffield College, University of Oxford.
    30. Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Density Estimators for Daily Volatility Based on the Use of Realized Measures," Departmental Working Papers 200620, Rutgers University, Department of Economics.

  11. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2004. "Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise," Economics Papers 2004-W28, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
    3. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    5. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    6. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    7. Ole E. Barndorff-Nielsen & Neil Shephard & Matthias Winkel, 2005. "Limit theorems for multipower variation in the presence of jumps," Economics Papers 2005-W07, Economics Group, Nuffield College, University of Oxford.
    8. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    9. Ole Barndorff-Nielsen & Neil Shephard, 2004. "Multipower Variation and Stochastic Volatility," Economics Papers 2004-W30, Economics Group, Nuffield College, University of Oxford.
    10. Torben G. Andersen & Luca Benzoni, 2007. "Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification test for Affine Term Structure Models," NBER Working Papers 12962, National Bureau of Economic Research, Inc.
    11. Jeremy Large, 2005. "Estimating quadratic variation when quoted prices jump by a constant increment," Economics Papers 2005-W05, Economics Group, Nuffield College, University of Oxford.
    12. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    13. Giuseppe Curci & Fulvio Corsi, 2012. "Discrete sine transform for multi-scale realized volatility measures§," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 263-279, April.
    14. B. Jungbacker & S.J. Koopman, 2005. "Model-based Measurement of Actual Volatility in High-Frequency Data," Tinbergen Institute Discussion Papers 05-002/4, Tinbergen Institute.
    15. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
    16. Masato Ubukata & Toshiaki Watanabe, 2013. "Pricing Nikkei 225 Options Using Realized Volatility," Global COE Hi-Stat Discussion Paper Series gd12-273, Institute of Economic Research, Hitotsubashi University.
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    1. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    2. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    3. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2018. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," Econometrics and Statistics, Elsevier, vol. 5(C), pages 67-82.
    5. Bauwens, Luc & Xu, Yongdeng, 2023. "The contribution of realized covariance models to the economic value of volatility timing," LIDAM Discussion Papers CORE 2023018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021. "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 44-57.
    7. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    8. Dimitris N. Politis & Dimitrios D. Thomakos, 2007. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," Working Paper series 44_07, Rimini Centre for Economic Analysis.
    9. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    10. Ralf Becker & Adam Clements & Christopher Coleman-Fenn, 2009. "Forecast performance of implied volatility and the impact of the volatility risk premium," NCER Working Paper Series 45, National Centre for Econometric Research.
    11. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    12. Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
    13. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    14. Radovan Parrák, 2013. "The Economic Valuation of Variance Forecasts: An Artificial Option Market Approach," Working Papers IES 2013/09, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2013.
    15. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    16. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    17. Borgsen, Sina & Glaser, Markus, 2005. "Diversifikationseffekte durch Small und Mid Caps?," Sonderforschungsbereich 504 Publications 05-10, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
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    23. Louzis Dimitrios P., 2016. "Steady-state priors and Bayesian variable selection in VAR forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 495-527, December.
    24. Borgsen, Sina & Glaser, Markus, 2005. "Diversifikationseffekte durch small und mid caps? : Eine empirische Untersuchung basierend auf europäischen Aktienindizes," Papers 05-10, Sonderforschungsbreich 504.
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    31. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
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    1. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    2. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    3. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    4. Høg, Esben, 2008. "Volatility and realized quadratic variation of differenced returns : A wavelet method approach," Finance Research Group Working Papers F-2008-06, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    5. Jozef Barunik & Lukas Vacha, 2015. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1347-1364, August.
    6. Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain," European Journal of Operational Research, Elsevier, vol. 251(1), pages 329-340.
    7. Wang, Fangfang, 2014. "Optimal design of Fourier estimator in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 708-722.
    8. Neil Shephard & Ole E. Barndorff-Nielsen & Department of Mathematical Sciences & University of Aarhus & Denmark, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Series Working Papers 240, University of Oxford, Department of Economics.
    9. Orregaard Nielsen, Morten & Houmann Frederiksen, Per, 2005. "Finite Sample Accuracy of Integrated Volatility Estimators," Queen's Economics Department Working Papers 273721, Queen's University - Department of Economics.

  14. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.

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    1. Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    112. 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).
    113. Luca Agnello & Vitor Castro & Ricardo Sousa, 2019. "The Benevolence of Time, Sound Macroeconomic Environment and Governance Quality on the Duration of Sovereign Ratings Phases," Working Papers 34, European Stability Mechanism.
    114. Zegadło, Piotr, 2022. "Identifying bull and bear market regimes with a robust rule-based method," Research in International Business and Finance, Elsevier, vol. 60(C).
    115. Scott M. R. Mahadeo & Reinhold Heinlein & Gabriella Deborah Legrenzi, 2019. "Tracing the Genesis of Contagion in the Oil-Finance Nexus," CESifo Working Paper Series 7925, CESifo.
    116. Gert Elaut & Michael Frömmel & Alexander Mende, 2017. "Duration Dependence, Behavioral Restrictions, and the Market Timing Ability of Commodity Trading Advisors," International Review of Finance, International Review of Finance Ltd., vol. 17(3), pages 427-450, September.
    117. Eric Girardin & Zhenya Liu, 2003. "The Chinese Stock Market: A Casino with 'Buffer Zones'?," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 1(1), pages 57-70.
    118. Valeriy Zakamulin, 2023. "Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-25, March.
    119. S. D. Bekiros & D. A. Georgoutsos, 2008. "Direction-of-change forecasting using a volatility-based recurrent neural network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 407-417.

  16. Allan Timmermann & Asger Lunde, 1998. "The Hazards of Mutual Fund Underperformance: A Cox Regression Analysis," FMG Discussion Papers dp302, Financial Markets Group.

    Cited by:

    1. Peter A. F. Fraser‐Mackenzie & Tiejun Ma & Ming‐Chien Sung & Johnnie E. V. Johnson, 2019. "Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks," Risk Analysis, John Wiley & Sons, vol. 39(7), pages 1560-1581, July.
    2. Juan Carlos Matallín-Sáez & Amparo Soler-Domínguez & Emili Tortosa-Ausina, 2014. "On the robustness of persistence in mutual fund performance," Working Papers 2014/01, Economics Department, Universitat Jaume I, Castellón (Spain).
    3. Darolles, Serge & Florens, Jean-Pierre & Simon, Guillaume, 2010. "Nonparametric Analysis of Hedge Funds Lifetimes," TSE Working Papers 10-174, Toulouse School of Economics (TSE).
    4. Emmanuel Mamatzakis & Mike G. Tsionas, 2021. "Testing for persistence in US mutual funds’ performance: a Bayesian dynamic panel model," Annals of Operations Research, Springer, vol. 299(1), pages 1203-1233, April.
    5. A. Colin Cameron & Anthony D. Hall, 2003. "A Survival Analysis of Australian Equity Mutual Funds," Australian Journal of Management, Australian School of Business, vol. 28(2), pages 209-226, September.
    6. Mohammad Mojtahedi & Sidney Newton & Jason Meding, 2017. "Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1119-1133, January.
    7. Jung-Min KIM, 2015. "Failure Risk and the Cross-Section of Hedge Fund Returns," Working Papers 2015-13, Economic Research Institute, Bank of Korea.
    8. Giacomo Nocera, Lucia Spotorno, 2026. "Termination Risk and Asset Managers’ Risk Taking in Separately Managed Accounts," BAFFI CAREFIN Working Papers 26278, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    9. Alex Grecu & Burton G. Malkiel & Atanu Saha, 2006. "Why Do Hedge Funds Stop Reporting Their Performance?," Working Papers 78, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Boyson, Nicole M., 2010. "Implicit incentives and reputational herding by hedge fund managers," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 283-299, June.
    11. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
    12. Bessler, Wolfgang & Blake, David & Lückoff, Peter & Tonks, Ian, 2010. "Why does mutual fund performance not persist? The impact and interaction of fund flows and manager changes," MPRA Paper 34185, University Library of Munich, Germany.
    13. Fletcher, Jonathan & Forbes, David, 2002. "An exploration of the persistence of UK unit trust performance," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 475-493, December.
    14. Stephen Brown, 1999. "Conditions for Survival: Changing Risk and the Performance of Hedge Fund Managers and CTAs," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-077, New York University, Leonard N. Stern School of Business-.
    15. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    16. Cogneau, Philippe & Hübner, Georges, 2015. "The prediction of fund failure through performance diagnostics," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 224-241.
    17. Kozo Kiyota & Miho Takizawa, 2007. "The Shadow of Death: Pre-exit Performance of Firms in Japan," Hi-Stat Discussion Paper Series d06-204, Institute of Economic Research, Hitotsubashi University.
    18. Jin, Xisong & Nadal De Simone, Francisco, 2014. "A framework for tracking changes in the intensity of investment funds' systemic risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 343-368.
    19. Naohiko Baba & Hiromichi Goko, 2006. "Survival Analysis of Hedge Funds," Bank of Japan Working Paper Series 06-E-5, Bank of Japan.
    20. Cumming, Douglas & Dai, Na & Johan, Sofia, 2015. "Are hedge funds registered in Delaware different?," Journal of Corporate Finance, Elsevier, vol. 35(C), pages 232-246.
    21. Xiangang Xin & P. Eric Yeung & Zilong Zhang, 2024. "Wrong Kind of Transparency? Mutual Funds’ Higher Reporting Frequency, Window Dressing, and Performance," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 62(2), pages 737-781, May.
    22. Blake, David & Cairns, Andrew & Dowd, Kevin, 2008. "Turning pension plans into pension planes: What investment strategy designers of defined contribution pension plans can learn from commercial aircraft designers," MPRA Paper 33749, University Library of Munich, Germany.
    23. Bank for International Settlements, 2003. "Incentive structures in institutional asset management and their implications for financial markets," CGFS Papers, Bank for International Settlements, number 21.
    24. Emmanuel Mamatzakis & Pankaj C. Patel & Mike G. Tsionas, 2024. "A Bayesian learning model of hedge fund performance," Annals of Operations Research, Springer, vol. 333(1), pages 201-238, February.
    25. Carree, Martin A., 2003. "A hazard rate analysis of Russian commercial banks in the period 1994-1997," Economic Systems, Elsevier, vol. 27(3), pages 255-269, September.
    26. Carree, M.A., 2000. "Interest and Hazard Rates of Russian Saving Banks," ERIM Report Series Research in Management ERS-2000-26-STR, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    27. Qiang Bu & Nelson Lacey, 2009. "On understanding mutual fund terminations," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(1), pages 80-99, January.
    28. Hanke, Bernd & Keswani, Aneel & Quigley, Garrett & Zagonov, Maxim, 2018. "Survivorship bias and comparability of UK open-ended fund databases," Economics Letters, Elsevier, vol. 172(C), pages 110-114.
    29. Yamashita, Takashi, 2007. "House price appreciation, liquidity constraints, and second mortgages," Journal of Urban Economics, Elsevier, vol. 62(3), pages 424-440, November.
    30. Juan Yao & Graham Partington & Max Stevenson, 2005. "Run length and the predictability of stock price reversals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 45(4), pages 653-671, December.
    31. Yehong Liu & Guosheng Yin, 2018. "Average Holding Price," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 1-20, March.
    32. Boldron, François & Fève, Frédérique & Florens, Jean-Pierre & Panet-Amaro, C. & Valognes, C., 2010. "Econometric Models and the Evolution of Post-Offices Network," IDEI Working Papers 626, Institut d'Économie Industrielle (IDEI), Toulouse.
    33. Keith Cuthbertson & Dirk Nitzsche & Niall O'Sullivan, 2010. "Mutual Fund Performance: Measurement and Evidence," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 19(2), pages 95-187, May.
    34. Zhao, Xinge, 2004. "Why are some mutual funds closed to new investors?," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1867-1887, August.

  17. Engle, Robert F & Lunde, Asger, 1998. "Trades and Quotes: A Bivariate Point Process," University of California at San Diego, Economics Working Paper Series qt8bh079sq, Department of Economics, UC San Diego.

    Cited by:

    1. Joel Hasbrouck, 1999. "Trading Fast and Slow: Security Market Events in Real Time," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-012, New York University, Leonard N. Stern School of Business-.
    2. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, Center for Economic and Financial Research (CEFIR).
    3. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
    4. Warren Bailey & Lin Zheng & Yinggang Zhou, 2012. "What Makes the VIX Tick?," Working Papers 222012, Hong Kong Institute for Monetary Research.
    5. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," PIER Working Paper Archive 04-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Lin, William T. & Tsai, Shih-Chuan & Chiu, Peter, 2016. "Do foreign institutions outperform in the Taiwan options market?," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 101-115.
    7. Jimmy E. Hilliard & Jitka Hilliard, 2012. "Matching non-synchronous observations in derivative markets: choosing windows and efficient estimators," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 49-60, September.
    8. Rama Cont & Adrien de Larrard, 2013. "Price Dynamics in a Markovian Limit Order Market," Post-Print hal-00552252, HAL.
    9. Spierdijk, L. & Nijman, T.E. & van Soest, A.H.O., 2002. "The Price Impact of Trades in Illiquid Stocks in Periods of High and Low Market Activity," Discussion Paper 2002-29, Tilburg University, Center for Economic Research.
    10. Wiesław Szulczewski & Wojciech Jakubowski, 2018. "The Application of Mixture Distribution for the Estimation of Extreme Floods in Controlled Catchment Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3519-3534, August.
    11. Sadoghi, Amirhossein & Vecer, Jan, 2022. "Optimal liquidation problem in illiquid markets," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1050-1066.
    12. Anatoliy Swishchuk, 2017. "General Compound Hawkes Processes in Limit Order Books," Papers 1706.07459, arXiv.org, revised Jun 2017.
    13. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    14. Chiara Scotti, 2006. "A bivariate model of Fed and ECB main policy rates," International Finance Discussion Papers 875, Board of Governors of the Federal Reserve System (U.S.).
    15. Hautsch, Nikolaus, 1999. "Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions," CoFE Discussion Papers 99/03, University of Konstanz, Center of Finance and Econometrics (CoFE).
    16. Wei Sun & Svetlozar Rachev & Frank Fabozzi & Petko Kalev, 2008. "Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration," Annals of Finance, Springer, vol. 4(2), pages 217-241, March.
    17. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    18. Bhatti, Chad R., 2009. "Intraday trade and quote dynamics: A Cox regression analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2240-2249.
    19. GRAMMIG, Joachim & HEINEN, Andréas & RENGIFO, Erick, 2004. "Trading activity and liquidity supply in a pure limit order book market," LIDAM Discussion Papers CORE 2004058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Johannes Prix & Otto Loistl & Michael Huetl, 2007. "Algorithmic Trading Patterns in Xetra Orders," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 717-739.
    21. Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
    22. Georges Dionne & Xiaozhou Zhou, 2016. "The Dynamics of Ex-ante High-Frequency Liquidity: An Empirical Analysis," Working Papers 15-5, HEC Montreal, Canada Research Chair in Risk Management.
    23. Òscar Jordà & Moritz Schularick & Alan M. Taylor, 2020. "Disasters Everywhere: The Costs of Business Cycles Reconsidered," NBER Working Papers 26962, National Bureau of Economic Research, Inc.
    24. Amirhossein Sadoghi & Jan Vecer, 2022. "Optimal liquidation problem in illiquid markets," Post-Print hal-03696768, HAL.
    25. Bhatti, Chad R., 2010. "The Birnbaum–Saunders autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2062-2078.
    26. Georges Dionne & Xiaozhou Zhou, 2016. "The Dynamics of Ex-ante Weighted Spread: An Empirical Analysis," Working Papers 16-4, HEC Montreal, Canada Research Chair in Risk Management.
    27. Spierdijk, Laura, 2004. "An empirical analysis of the role of the trading intensity in information dissemination on the NYSE," Journal of Empirical Finance, Elsevier, vol. 11(2), pages 163-184, March.
    28. Ola Simonsen, 2007. "An empirical model for durations in stocks," Annals of Finance, Springer, vol. 3(2), pages 241-255, March.
    29. Spierdijk, L., 2002. "An Empirical Analysis of the Role of the Trading Intensity in Information Dissemination on the NYSE," Other publications TiSEM d495caf0-2f2a-425f-8e50-e, Tilburg University, School of Economics and Management.
    30. Renault, Eric & van der Heijden, Thijs & Werker, Bas J.M., 2014. "The dynamic mixed hitting-time model for multiple transaction prices and times," Journal of Econometrics, Elsevier, vol. 180(2), pages 233-250.
    31. Large, Jeremy, 2007. "Measuring the resiliency of an electronic limit order book," Journal of Financial Markets, Elsevier, vol. 10(1), pages 1-25, February.
    32. Collver, Charles, 2009. "Measuring the impact of option market activity on the stock market: Bivariate point process models of stock and option transactions," Journal of Financial Markets, Elsevier, vol. 12(1), pages 87-106, February.
    33. A. Sadoghi & J. Vecer, 2015. "Optimum Liquidation Problem Associated with the Poisson Cluster Process," Papers 1507.06514, arXiv.org, revised Dec 2015.
    34. Kehrle, Kerstin & Peter, Franziska J., 2013. "Who moves first? An intensity-based measure for information flows across stock exchanges," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1629-1642.
    35. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," LIDAM Discussion Papers CORE 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    36. Anatoliy Swishchuk, 2021. "Modelling of Limit Order Books by General Compound Hawkes Processes with Implementations," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 399-428, March.
    37. Rama Cont & Adrien De Larrard, 2011. "Price dynamics in a Markovian limit order market," Papers 1104.4596, arXiv.org.
    38. Anatoliy Swishchuk & Aiden Huffman, 2018. "General Compound Hawkes Processes in Limit Order Books," Papers 1812.02298, arXiv.org.
    39. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    40. Ban Zheng & Franc{c}ois Roueff & Fr'ed'eric Abergel, 2013. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Papers 1301.5007, arXiv.org, revised Feb 2014.
    41. Alfonso Dufour & Robert F Engle, 2000. "The ACD Model: Predictability of the Time Between Concecutive Trades," ICMA Centre Discussion Papers in Finance icma-dp2000-05, Henley Business School, University of Reading.
    42. HEINEN, Andreas & RENGIFO, Erick, 2003. "Multivariate modelling of time series count data: an autoregressive conditional Poisson model," LIDAM Discussion Papers CORE 2003025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    43. Giampaoli, Iacopo & Ng, Wing Lon & Constantinou, Nick, 2009. "Analysis of ultra-high-frequency financial data using advanced Fourier transforms," Finance Research Letters, Elsevier, vol. 6(1), pages 47-53, March.
    44. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    45. Chen, Tao & Li, Jie & Cai, Jun, 2008. "Information content of inter-trade time on the Chinese market," Emerging Markets Review, Elsevier, vol. 9(3), pages 174-193, September.
    46. Yang, Joey Wenling, 2011. "Transaction duration and asymmetric price impact of trades--Evidence from Australia," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 91-102, January.
    47. Spierdijk, L., 2002. "An Empirical Analysis of the Role of the Trading Intensity in Information Dissemination on the NYSE," Discussion Paper 2002-30, Tilburg University, Center for Economic Research.
    48. Erick Rengifo & Andresas Heinen, 2004. "Comovements in Trading activity: A Multivariate Autoregressive Model of Time Series Count Data Using Copulas," Econometric Society 2004 Far Eastern Meetings 755, Econometric Society.
    49. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
    50. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.
    51. Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," Umeå Economic Studies 657, Umeå University, Department of Economics.
    52. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2010. "The Price Impact of Order Book Events," Papers 1011.6402, arXiv.org, revised Apr 2011.

Articles

  1. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    See citations under working paper version above.
  2. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    See citations under working paper version above.
  3. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Subsampling realised kernels," Journal of Econometrics, Elsevier, vol. 160(1), pages 204-219, January.
    See citations under working paper version above.
  4. Asger Lunde & Allan Zebedee, 2009. "Intraday volatility responses to monetary policy events," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(4), pages 383-399, December.

    Cited by:

    1. Jing Wang & Xiaoneng Zhu, 2013. "The reaction of international stock markets to Federal Reserve policy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(1), pages 1-30, March.
    2. Apergis, Nicholas, 2015. "The role of FOMC minutes for US asset prices before and after the 2008 crisis: Evidence from GARCH volatility modeling," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 100-107.
    3. Volta, Vittoria & Aste, Tomaso, 2022. "Causal coupling between European and UK markets triggered by announcements of monetary policy decisions," LSE Research Online Documents on Economics 114947, London School of Economics and Political Science, LSE Library.
    4. Gospodinov, Nikolay & Jamali, Ibrahim, 2012. "The effects of Federal funds rate surprises on S&P 500 volatility and volatility risk premium," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 497-510.
    5. Daniel Jubinski & Marc Tomljanovich, 2013. "Do FOMC minutes matter to markets? An intraday analysis of FOMC minutes releases on individual equity volatility and returns," Review of Financial Economics, John Wiley & Sons, vol. 22(3), pages 86-97, September.
    6. Dossani, Asad, 2024. "Monetary policy and currency variance risk premia," Research in International Business and Finance, Elsevier, vol. 69(C).
    7. Finta, Marinela Adriana, 2021. "Japanese monetary policy and its impact on stock market implied volatility during pleasant and unpleasant weather," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    8. Jubinski, Daniel & Tomljanovich, Marc, 2013. "Do FOMC minutes matter to markets? An intraday analysis of FOMC minutes releases on individual equity volatility and returns," Review of Financial Economics, Elsevier, vol. 22(3), pages 86-97.
    9. Hussain, Syed Mujahid, 2011. "Simultaneous monetary policy announcements and international stock markets response: An intraday analysis," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 752-764, March.
    10. Papadamou, Stephanos & Sidiropoulos, Moïse & Spyromitros, Eleftherios, 2014. "Does central bank transparency affect stock market volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 362-377.
    11. Ioannis Dokas & Georgios Oikonomou & Stephanos Papadamou & Eleftherios Spyromitros, 2023. "Central Bank Credibility’s Effect on Stock Exchange Returns’ Volatility: Evidence from OECD Countries," Economies, MDPI, vol. 11(10), pages 1-15, October.
    12. Andrieş, Alin Marius & Nistor, Simona & Sprincean, Nicu, 2020. "The impact of central bank transparency on systemic risk—Evidence from Central and Eastern Europe," Research in International Business and Finance, Elsevier, vol. 51(C).

  5. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.

    Cited by:

    1. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
    2. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Barbara Będowska-Sójka, 2021. "Is liquidity wasted? The zero-returns on the Warsaw Stock Exchange," Annals of Operations Research, Springer, vol. 297(1), pages 37-51, February.
    4. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    5. Bibinger, Markus & Neely, Christopher & Winkelmann, Lars, 2018. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," IRTG 1792 Discussion Papers 2018-055, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    7. Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.
    8. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2011. "A Functional Filtering and Neighborhood Truncation Approach to Integrated Quarticity Estimation," CREATES Research Papers 2011-23, Department of Economics and Business Economics, Aarhus University.
    9. 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.
    10. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    11. Behrendt, Simon & Schweikert, Karsten, 2021. "A Note on Adaptive Group Lasso for Structural Break Time Series," Econometrics and Statistics, Elsevier, vol. 17(C), pages 156-172.
    12. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The memory of stock return volatility: Asset pricing implications," Journal of Financial Markets, Elsevier, vol. 47(C).
    13. Jonathan Chassot & Michael Creel, 2023. "Constructing Efficient Simulated Moments Using Temporal Convolutional Networks," Working Papers 1412, Barcelona School of Economics.
    14. Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
    15. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    16. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    17. 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.
    18. Nabil Bouamara & Kris Boudt & Sébastien Laurent & Christopher J. Neely, 2024. "Sluggish news reactions: A combinatorial approach for synchronizing stock jumps," Working Papers 2024-006, Federal Reserve Bank of St. Louis, revised 06 Jul 2026.
    19. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
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  6. Peter Hansen & Jeremy Large & Asger Lunde, 2008. "Moving Average-Based Estimators of Integrated Variance," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 79-111.

    Cited by:

    1. Kunitomo, Naoto & Sato, Seisho, 2013. "Separating Information Maximum Likelihood estimation of the integrated volatility and covariance with micro-market noise," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 282-309.
    2. Asai, M. & McAleer, M.J. & Medeiros, M.C., 2008. "Asymmetry and leverage in realized volatility," Econometric Institute Research Papers EI 2008-31, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    4. Kim Christensen & Roel Oomen & Mark Podolskij, 2026. "Realised quantile-based estimation of the integrated variance," Papers 2601.13006, arXiv.org.
    5. Jeremy Large, 2007. "Estimating Quadratic Variation When Quoted Prices Change by a Constant Increment," Economics Series Working Papers 340, University of Oxford, Department of Economics.
    6. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    7. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Post-Print hal-00815564, HAL.
    8. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    9. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    10. 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.
    11. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    12. Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Indirect inference with time series observed with error," CREATES Research Papers 2014-57, Department of Economics and Business Economics, Aarhus University.
    13. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    14. Asai, M. & McAleer, M.J. & Medeiros, M.C., 2010. "Asymmetry and Long Memory in Volatility Modelling," Econometric Institute Research Papers EI 2010-60, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.
    16. Naoto Kunitomo & Seisho Sato, 2010. "On Properties of Separating Information Maximum Likelihood Estimation of Realized Volatility and Covariance with Micro-Market Noise," CARF F-Series CARF-F-228, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    17. 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.
    18. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," Documentos de Trabajo del ICAE 2011-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    19. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
    20. Li, Z. Merrick & Laeven, Roger J.A. & Vellekoop, Michel H., 2020. "Dependent microstructure noise and integrated volatility estimation from high-frequency data," Journal of Econometrics, Elsevier, vol. 215(2), pages 536-558.
    21. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    22. Kunitomo, Naoto & Sato, Seisho, 2011. "The SIML estimation of realized volatility of the Nikkei-225 Futures and hedging coefficient with micro-market noise," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1272-1289.
    23. 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.
    24. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
    25. Li, Yingying & Xie, Shangyu & Zheng, Xinghua, 2016. "Efficient estimation of integrated volatility incorporating trading information," Journal of Econometrics, Elsevier, vol. 195(1), pages 33-50.
    26. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    27. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    28. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    29. Tseng-Chan Tseng & Hung-Cheng Lai & Cha-Fei Lin, 2012. "The impact of overnight returns on realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 357-364, March.
    30. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    31. 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.
    32. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," Cambridge Working Papers in Economics 1464, Faculty of Economics, University of Cambridge.
    33. Griffin, Jim & Liu, Jia & Maheu, John M, 2016. "Bayesian Nonparametric Estimation of Ex-post Variance," MPRA Paper 71220, University Library of Munich, Germany.
    34. Wang Pu & Yixiang Chen & Feng Ma, 2016. "Forecasting the realized volatility in the Chinese stock market: further evidence," Applied Economics, Taylor & Francis Journals, vol. 48(33), pages 3116-3130, July.
    35. Xin Jin & Jia Liu & Qiao Yang, 2021. "Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach," Econometrics, MDPI, vol. 9(4), pages 1-22, December.
    36. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    37. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Papers 2006-W03, Economics Group, Nuffield College, University of Oxford.

  7. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    See citations under working paper version above.
  8. Allan Zebedee & Eric Bentzen & Peter Hansen & Asger Lunde, 2008. "The Greenspan years: an analysis of the magnitude and speed of the equity market response to FOMC announcements," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 22(1), pages 3-20, March.

    Cited by:

    1. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
    2. Gospodinov, Nikolay & Jamali, Ibrahim, 2012. "The effects of Federal funds rate surprises on S&P 500 volatility and volatility risk premium," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 497-510.
    3. DeCoste, Joseph, 2025. "Comovement and S&P 500 membership," Global Finance Journal, Elsevier, vol. 65(C).
    4. Farka, Mira & DaSilva, Amadeu, 2011. "The fed and the term structure: Addressing simultaneity within a structural VAR model," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 935-952.
    5. Yuriy Gorodnichenko & Michael Weber, 2013. "Are Sticky Prices Costly? Evidence From The Stock Market," NBER Working Papers 18860, National Bureau of Economic Research, Inc.

  9. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 68-104.

    Cited by:

    1. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    2. Nolte, Ingmar & Voev, Valeri, 2007. "Estimating high-frequency based (co-) variances: A unified approach," CoFE Discussion Papers 07/07, University of Konstanz, Center of Finance and Econometrics (CoFE).
    3. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. 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).
    5. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2012. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and Covolatility," Global COE Hi-Stat Discussion Paper Series gd12-269, Institute of Economic Research, Hitotsubashi University.
    6. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    7. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    8. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
    9. Bent Jesper Christensen & Rasmus T. Varneskov, 2016. "Dynamic Global Currency Hedging," CREATES Research Papers 2016-03, Department of Economics and Business Economics, Aarhus University.
    10. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.
    11. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Post-Print hal-00815564, HAL.
    12. Sébastien Laurent & Jeroen Rombouts & Francesco Violente, 2009. "On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models," CIRANO Working Papers 2009s-45, CIRANO.
    13. Tóth, Bence & Kertész, János, 2009. "Accurate estimator of correlations between asynchronous signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1696-1705.
    14. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
    15. Yao Axel Ehouman, 2019. "Volatility transmission between oil prices and banks stock prices as a new source of instability: Lessons from the US Experience," Working Papers hal-04141868, HAL.
    16. Dave Berger & H. J. Turtle, 2009. "Time Variability In Market Risk Aversion," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 285-307, September.
    17. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    18. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    19. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    20. Yeh, Jin-Huei & Wang, Jying-Nan, 2010. "Correcting microstructure comovement biases for integrated covariance," Finance Research Letters, Elsevier, vol. 7(3), pages 184-191, September.
    21. Aida Karmous & Heni Boubaker & Lotfi Belkacem, 2021. "Forecasting Volatility for an Optimal Portfolio with Stylized Facts Using Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 461-482, August.
    22. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
    23. 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.
    24. Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," KIER Working Papers 840, Kyoto University, Institute of Economic Research.
    25. Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
    26. Karim, Muhammad Mahmudul & Shah, Mohamed Eskandar & Noman, Abu Hanifa Md. & Yarovaya, Larisa, 2024. "Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    27. 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.
    28. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    29. Yao Axel Ehouman, 2019. "Volatility transmission between oil prices and banks stock prices as a new source of instability: Lessons from the US Experience," EconomiX Working Papers 2019-19, University of Paris Nanterre, EconomiX.
    30. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2013. "Risk spillovers in international equity portfolios," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 121-137.
    31. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    32. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
    33. 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.
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    See citations under working paper version above.
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    Cited by:

    1. Andrea Bastianin & Matteo Manera, 2015. "How Does Stock Market Volatility React to Oil Shocks?," Working Papers 2014.110, Fondazione Eni Enrico Mattei.
    2. Pedro N. Rodríguez, & Simón Sosvilla-Rivero, 2006. "Forecasting Stock Price Changes: Is it Possible?," Working Papers 2006-22, FEDEA.
    3. Ryuji Hashimoto & Takehiro Takayanagi & Masahiro Suzuki & Kiyoshi Izumi, 2026. "LLM agents reveal how human bias shapes path-dependent market dynamics," Journal of Computational Social Science, Springer, vol. 9(2), pages 1-26, May.
    4. Andrea BASTIANIN & Francesca CONTI & Matteo MANERA, 2015. "The Impacts of Oil Price Shocks on Stock Market Volatility: Evidence from the G7 Countries," Departmental Working Papers 2015-17, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    5. Nolte, Ingmar & Voev, Valeri, 2007. "Panel intensity models with latent factors: An application to the trading dynamics on the foreign exchange market," CoFE Discussion Papers 07/02, University of Konstanz, Center of Finance and Econometrics (CoFE).
    6. Hayette Gatfaoui, 2010. "Investigating the dependence structure between credit default swap spreads and the U.S. financial market," Annals of Finance, Springer, vol. 6(4), pages 511-535, October.

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