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Peter Hansen

Not to be confused with: Petter Vegard Hansen

Citations

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

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. Chen Tong & Peter Reinhard Hansen, 2023. "Characterizing Correlation Matrices that Admit a Clustered Factor Representation," Papers 2308.05895, arXiv.org.

    Cited by:

    1. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.

  2. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Aug 2024.

    Cited by:

    1. Massimo Guidolin & Erwin Hansen & Gabriel Cabrera, 2023. "Time-Varying Risk Aversion and International Stock Returns," BAFFI CAREFIN Working Papers 23203, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. 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).

  3. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2021. "Option Pricing with State-dependent Pricing Kernel," Papers 2112.05308, arXiv.org, revised Apr 2022.

    Cited by:

    1. Mozumder, Sharif & Frijns, Bart & Talukdar, Bakhtear & Kabir, M. Humayun, 2024. "On practitioners closed-form GARCH option pricing," International Review of Financial Analysis, Elsevier, vol. 94(C).
    2. Tong, Chen, 2024. "Pricing CBOE VIX in non-affine GARCH models with variance risk premium," Finance Research Letters, Elsevier, vol. 62(PA).

  4. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2021. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Papers 2112.05302, arXiv.org.

    Cited by:

    1. Tong, Chen, 2024. "Pricing CBOE VIX in non-affine GARCH models with variance risk premium," Finance Research Letters, Elsevier, vol. 62(PA).
    2. 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).

  5. Peter Reinhard Hansen, 2021. "Relative Contagiousness of Emerging Virus Variants: An Analysis of the Alpha, Delta, and Omicron SARS-CoV-2 Variants," Papers 2110.00533, arXiv.org, revised Jan 2022.

    Cited by:

    1. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.

  6. Elena Ivona Dumitrescu & Peter Hansen, 2020. "How Should Parameter Estimation Be Tailored to the Objective?," Post-Print hal-03331109, HAL.

    Cited by:

    1. Yi-Ting Chen & Chu-An Liu, 2021. "Model Averaging for Asymptotically Optimal Combined Forecasts," IEAS Working Paper : academic research 21-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    2. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    3. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.

  7. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.

    Cited by:

    1. Hafner, Christian M. & Wang, Linqi, 2023. "A dynamic conditional score model for the log correlation matrix," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Ilya Archakov & Peter Reinhard Hansen, 2020. "A New Parametrization of Correlation Matrices," Papers 2012.02395, arXiv.org.
    3. Tong, Chen & Hansen, Peter Reinhard, 2023. "Characterizing correlation matrices that admit a clustered factor representation," Economics Letters, Elsevier, vol. 233(C).
    4. Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.
    5. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org, revised Jul 2024.

  8. Ilya Archakov & Peter Reinhard Hansen, 2020. "A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices," Papers 2012.02698, arXiv.org, revised Nov 2021.

    Cited by:

    1. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.

  9. Ilya Archakov & Peter Reinhard Hansen, 2020. "A New Parametrization of Correlation Matrices," Papers 2012.02395, arXiv.org.

    Cited by:

    1. K. B. Gubbels & J. Y. Ypma & C. W. Oosterlee, 2023. "Principal Component Copulas for Capital Modelling," Papers 2312.13195, arXiv.org.
    2. Ilya Archakov & Peter Reinhard Hansen & Yiyao Luo, 2024. "A new method for generating random correlation matrices," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 188-212.
    3. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.
    4. Ilya Archakov & Peter Reinhard Hansen, 2024. "A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 1099-1113, July.
    5. Hafner, Christian M. & Wang, Linqi, 2023. "A dynamic conditional score model for the log correlation matrix," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    7. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023. "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1054-1086.
    8. Tong, Chen & Hansen, Peter Reinhard, 2023. "Characterizing correlation matrices that admit a clustered factor representation," Economics Letters, Elsevier, vol. 233(C).
    9. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    10. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.
    11. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    12. Dilip B. Madan & King Wang, 2022. "Two sided efficient frontiers at multiple time horizons," Annals of Finance, Springer, vol. 18(3), pages 327-353, September.

  10. Peter Reinhard Hansen & Pawel Janus & Siem Jan Koopman, 2016. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Tinbergen Institute Discussion Papers 16-061/III, Tinbergen Institute.

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. Umlandt, Dennis, 2023. "Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.
    4. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    5. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Research Papers in Economics 2022-02, University of Trier, Department of Economics.
    6. Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
    7. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.
    8. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    9. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
    10. Dennis Umlandt, 2020. "Likelihood-based Dynamic Asset Pricing: Learning Time-varying Risk Premia from Cross-Sectional Models," Working Paper Series 2020-06, University of Trier, Research Group Quantitative Finance and Risk Analysis.
    11. Manabu Asai & Michael McAleer, 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Tinbergen Institute Discussion Papers 18-005/III, Tinbergen Institute.
    12. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2021. "Exploring volatility of crude oil intra-day return curves: a functional GARCH-X Model," MPRA Paper 109231, University Library of Munich, Germany.
    13. Hartkopf, Jan Patrick & Reh, Laura, 2023. "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, vol. 56(C).
    14. Asai Manabu & So Mike K. P., 2023. "Realized BEKK-CAW Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 49-77, January.
    15. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    16. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org, revised Jul 2024.
    17. Vogler, Jan & Golosnoy, Vasyl, 2023. "Unrestricted maximum likelihood estimation of multivariate realized volatility models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1063-1074.
    18. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    19. BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.

  11. 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.

    Cited by:

    1. Siem Jan Koopman & Rutger Lit & André Lucas, 2017. "Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1490-1503, October.
    2. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.

  12. Peter Reinhard Hansen, 2015. "A Martingale Decomposition of Discrete Markov Chains," CREATES Research Papers 2015-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Peter Reinhard Hansen & Yiyao Luo, 2023. "Robust Estimation of Realized Correlation: New Insight about Intraday Fluctuations in Market Betas," Papers 2310.19992, arXiv.org.
    2. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.

  13. Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," CREATES Research Papers 2012-45, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Wang, Cindy S.H. & Fan, Rui & Xie, Yiqiang, 2023. "Market systemic risk, predictability and macroeconomics news," Finance Research Letters, Elsevier, vol. 56(C).
    2. Firmin Doko Tchatoka & Qazi Haque, 2020. "On bootstrapping tests of equal forecast accuracy for nested models," Economics Discussion / Working Papers 20-06, The University of Western Australia, Department of Economics.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    4. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    5. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    6. Hunt, Ian, 2022. "In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining," International Journal of Forecasting, Elsevier, vol. 38(3), pages 872-877.
    7. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    8. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    9. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    10. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.
    11. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    12. Kothari, Pratik & O’Doherty, Michael S., 2023. "Job postings and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
    13. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    14. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    15. Jean-Yves Pitarakis, 2020. "A Novel Approach to Predictive Accuracy Testing in Nested Environments," Papers 2008.08387, arXiv.org, revised Oct 2023.
    16. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    17. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    18. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
    19. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2023. "Comparing forecasting performance in cross-sections," Journal of Econometrics, Elsevier, vol. 237(2).
    20. 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.

  14. 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.

    Cited by:

    1. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    2. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    3. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
    4. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    5. Wu, Xinyu & Xia, Michelle & Zhang, Huanming, 2020. "Forecasting VaR using realized EGARCH model with skewness and kurtosis," Finance Research Letters, Elsevier, vol. 32(C).
    6. Chen, Qihao & Huang, Zhuo & Liang, Fang, 2023. "Measuring systemic risk with high-frequency data: A realized GARCH approach," Finance Research Letters, Elsevier, vol. 54(C).
    7. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2021. "Forecasting the volatility of asset returns: The informational gains from option prices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 862-880.
    8. 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.
    9. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    10. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
    11. 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.
    12. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    13. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    14. Yu‐Sheng Lai, 2018. "Estimation of the optimal futures hedge ratio for equity index portfolios using a realized beta generalized autoregressive conditional heteroskedasticity model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1370-1390, November.
    15. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Asai, M. & McAleer, M.J. & Peiris, S., 2017. "Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory," Econometric Institute Research Papers EI2017-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," LEO Working Papers / DR LEO 2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    18. Gkillas, Konstantinos & Konstantatos, Christoforos & Floros, Christos & Tsagkanos, Athanasios, 2021. "Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis," International Review of Financial Analysis, Elsevier, vol. 74(C).
    19. Dinghai Xu, 2021. "A study on volatility spurious almost integration effect: A threshold realized GARCH approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4104-4126, July.
    20. Chao Wang & Richard Gerlach, 2021. "A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting," Papers 2106.00288, arXiv.org, revised Oct 2022.
    21. Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
    22. Fang Liang & Lingshan Du & Zhuo Huang, 2023. "Option pricing with overnight and intraday volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1576-1614, November.
    23. Chen Tong & Zhuo Huang & Tianyi Wang, 2022. "Do VIX futures contribute to the valuation of VIX options?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1644-1664, September.
    24. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Alexandros Garefalakis & Nikolaos Sariannidis, 2020. "Greek sovereign crisis and European exchange rates: effects of news releases and their providers," Annals of Operations Research, Springer, vol. 294(1), pages 515-536, November.
    25. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.
    26. Alicia Adsera, Ana Ferrer and Virginia Herranz, 2020. "Descriptive labour market outcomes of immigrant women across Europe," Working Papers 2004, University of Waterloo, Department of Economics, revised 2020.
    27. Stoupos, Nikolaos & Kiohos, Apostolos, 2022. "Euro area stock markets integration: Empirical evidence after the end of 2010 debt crisis," Finance Research Letters, Elsevier, vol. 46(PB).
    28. Huawei Niu & Tianyu Liu, 2024. "Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model," Empirical Economics, Springer, vol. 67(1), pages 75-96, July.
    29. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2021. "Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility," Discussion paper series HIAS-E-104, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    30. Trifonov, Juri & Potanin, Bogdan, 2024. "GARCH-M model with an asymmetric risk premium: Distinguishing between ‘good’ and ‘bad’ volatility periods," International Review of Financial Analysis, Elsevier, vol. 91(C).
    31. Huiling Yuan & Guodong Li & Junhui Wang, 2022. "High-Frequency-Based Volatility Model with Network Structure," Papers 2204.12933, arXiv.org.
    32. Papantonis Ioannis & Rompolis Leonidas S. & Tzavalis Elias & Agapitos Orestis, 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.
    33. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2024. "Macro‐financial linkages in the high‐frequency domain: Economic fundamentals and the Covid‐induced uncertainty channel in US and UK financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1581-1608, April.
    34. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2024. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Journal of Financial Econometrics, Oxford University Press, vol. 22(1), pages 187-223.
    35. Gerlach, Richard & Naimoli, Antonio & Storti, Giuseppe, 2018. "Time Varying Heteroskedastic Realized GARCH models for tracking measurement error bias in volatility forecasting," MPRA Paper 83893, University Library of Munich, Germany.
    36. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Aug 2024.
    37. 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.
    38. P Gorgi & P R Hansen & P Janus & S J Koopman, 2019. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 1-32.
    39. 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.
    40. Manabu Asai & Chia-Lin Chang & Michael McAleer, 2016. "Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers," Documentos de Trabajo del ICAE 2016-15, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    41. Moawia Alghalith & Christos Floros & Konstantinos Gkillas, 2020. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility," Risks, MDPI, vol. 8(2), pages 1-15, April.
    42. Wu, Xinyu & Xie, Haibin, 2021. "A realized EGARCH-MIDAS model with higher moments," Finance Research Letters, Elsevier, vol. 38(C).
    43. Tran, Thuy Nhung, 2022. "The Volatility of the Stock Market and Financial Cycle: GARCH Family Models," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 151-168.
    44. Si Mohammed, Kamel & Tedeschi, Marco & Mallek, Sabrine & Tarczyńska-Łuniewska, Małgorzata & Zhang, Anqi, 2023. "Realized semi variance quantile connectedness between oil prices and stock market: Spillover from Russian-Ukraine clash," Resources Policy, Elsevier, vol. 85(PA).
    45. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    46. Chen Tong & Zhuo Huang, 2021. "Pricing VIX options with realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1180-1200, August.
    47. Manabu Asai & Michael McAleer, 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Tinbergen Institute Discussion Papers 18-005/III, Tinbergen Institute.
    48. Chunliang Deng & Xingfa Zhang & Yuan Li & Qiang Xiong, 2020. "Garch Model Test Using High-Frequency Data," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    49. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    50. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    51. Naimoli, Antonio, 2023. "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, vol. 176(C).
    52. Muntazir Hussain & Usman Bashir & Ramiz Ur Rehman, 2024. "Exchange Rate and Stock Prices Volatility Connectedness and Spillover during Pandemic Induced-Crises: Evidence from BRICS Countries," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(1), pages 183-203, March.
    53. 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.
    54. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2021. "Option Pricing with State-dependent Pricing Kernel," Papers 2112.05308, arXiv.org, revised Apr 2022.
    55. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    56. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    57. Rangika Peiris & Chao Wang & Richard Gerlach & Minh-Ngoc Tran, 2024. "Semi-parametric financial risk forecasting incorporating multiple realized measures," Papers 2402.09985, arXiv.org, revised Jun 2024.
    58. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch & Seong-Min Yoon, 2020. "OPEC News and Jumps in the Oil Market," Working Papers 202053, University of Pretoria, Department of Economics.
    59. Cathy W.S. Chen & Toshiaki Watanabe, 2019. "Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 747-765, May.
    60. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
    61. Yuta Kurose, 2022. "Bayesian GARCH modeling for return and range," Economics Bulletin, AccessEcon, vol. 42(3), pages 1717-1727.
    62. Dinghai Xu, 2020. "Canadian Stock Market Volatility under COVID-19," Working Papers 2001, University of Waterloo, Department of Economics, revised May 2020.
    63. Yves Dominicy & Harry-Paul Vander Elst, 2015. "Macro-Driven VaR Forecasts: From Very High to Very Low Frequency Data," Working Papers ECARES ECARES 2015-41, ULB -- Universite Libre de Bruxelles.
    64. Donggyu Kim & Minseog Oh & Yazhen Wang, 2022. "Conditional quantile analysis for realized GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 640-665, July.
    65. Ahmed BenSaïda, 2021. "The Good and Bad Volatility: A New Class of Asymmetric Heteroskedastic Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 540-570, April.
    66. Tianyi Wang & Sicong Cheng & Fangsheng Yin & Mei Yu, 2022. "Overnight volatility, realized volatility, and option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1264-1283, July.
    67. Zhiyuan Pan & Jun Zhang & Yudong Wang & Juan Huang, 2024. "Modeling and forecasting stock return volatility using the HARGARCH model with VIX information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1383-1403, August.
    68. Chen, Cathy W.S. & Watanabe, Toshiaki & Lin, Edward M.H., 2023. "Bayesian estimation of realized GARCH-type models with application to financial tail risk management," Econometrics and Statistics, Elsevier, vol. 28(C), pages 30-46.
    69. Díaz-Hernández, Adán & Constantinou, Nick, 2019. "A multiple regime extension to the Heston–Nandi GARCH(1,1) model," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 162-180.
    70. Shijia Song & Handong Li, 2023. "A new model for forecasting VaR and ES using intraday returns aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1039-1054, August.
    71. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
    72. Nikolaos Stoupos & Apostolos Kiohos, 2022. "Euro Area: Towards a European Common Bond? – Empirical Evidence from the Sovereign Debt Markets," Journal of Common Market Studies, Wiley Blackwell, vol. 60(4), pages 1019-1046, July.
    73. Stavroula Yfanti & Georgios Chortareas & Menelaos Karanasos & Emmanouil Noikokyris, 2022. "A three‐dimensional asymmetric power HEAVY model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2737-2761, July.
    74. Hiroyuki Kawakatsu, 2022. "Modeling Realized Variance with Realized Quarticity," Stats, MDPI, vol. 5(3), pages 1-25, September.
    75. Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org, revised Jan 2021.
    76. Huang, Zhuo & Liu, Hao & Wang, Tianyi, 2016. "Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model," Economic Modelling, Elsevier, vol. 52(PB), pages 812-821.
    77. Xu, Yongdeng, 2022. "The Exponential HEAVY Model: An Improved Approach to Volatility Modeling and Forecasting," Cardiff Economics Working Papers E2022/5, Cardiff University, Cardiff Business School, Economics Section.
    78. Guanghui Cai & Zhimin Wu & Lei Peng, 2021. "Forecasting volatility with outliers in Realized GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 667-685, July.
    79. Xie, Haibin & Yu, Chengtan, 2020. "Realized GARCH models: Simpler is better," Finance Research Letters, Elsevier, vol. 33(C).
    80. Lu, Xinjie & Su, Yuandong & Huang, Dengshi, 2023. "Chinese agricultural futures volatility: New insights from potential domestic and global predictors," International Review of Financial Analysis, Elsevier, vol. 89(C).
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    82. Didit Budi Nugroho & Takayuki Morimoto, 2019. "Incorporating Realized Quarticity into a Realized Stochastic Volatility Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(4), pages 495-528, December.
    83. 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).
    84. Nikolaos Stoupos & Apostolos Kiohos, 2021. "BREXIT referendum’s impact on the financial markets in the UK," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 157(1), pages 1-19, February.
    85. 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.
    86. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).
    87. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    88. Donggyu Kim, 2021. "Exponential GARCH-Ito Volatility Models," Papers 2111.04267, arXiv.org.
    89. Fangsheng Yin & Yang Bian & Tianyi Wang, 2021. "A short cut: Directly pricing VIX futures with discrete‐time long memory model and asymmetric jumps," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 458-477, April.
    90. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    91. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.

  15. Peter Reinhard Hansen & Allan Timmermann, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," CREATES Research Papers 2012-43, Department of Economics and Business Economics, Aarhus University.

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    1. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    2. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    3. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
    4. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    5. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    6. Goodness C. Aye & Frederick W. Deale & Rangan Gupta, 2016. "Does Debt Ceiling and Government Shutdown Help in Forecasting the US Equity Risk Premium?," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 63(3), pages 273-291.
    7. Chen, Jian & Jiang, Fuwei & Liu, Yangshu & Tu, Jun, 2017. "International volatility risk and Chinese stock return predictability," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 183-203.
    8. Firmin Doko Tchatoka & Qazi Haque, 2020. "On bootstrapping tests of equal forecast accuracy for nested models," Economics Discussion / Working Papers 20-06, The University of Western Australia, Department of Economics.
    9. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
    10. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    11. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    12. Biqing Cai & Jiti Gao, 2017. "A simple nonlinear predictive model for stock returns," Monash Econometrics and Business Statistics Working Papers 18/17, Monash University, Department of Econometrics and Business Statistics.
    13. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    14. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    15. Hatice Gökçe Karasoy Can & Çağlar Yüncüler, 2018. "The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(9), pages 2136-2152, July.
    16. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    17. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
    18. Chen, Jian & Jiang, Fuwei & Xue, Shuyu & Yao, Jiaquan, 2019. "The world predictive power of U.S. equity market skewness risk," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 210-227.
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    20. Thomadakis, Apostolos, 2016. "Do Combination Forecasts Outperform the Historical Average? Economic and Statistical Evidence," MPRA Paper 71589, University Library of Munich, Germany.
    21. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    22. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
    23. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    24. Christian Hutter, 2020. "A new indicator for nowcasting employment subject to social security contributions in Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-10, December.
    25. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    26. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    27. Peter Reinhard Hansen & Allan Timmermann, 2015. "Equivalence Between Out‐of‐Sample Forecast Comparisons and Wald Statistics," Econometrica, Econometric Society, vol. 83, pages 2485-2505, November.
    28. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    29. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    30. Algaba, Andres & Boudt, Kris, 2017. "Generalized financial ratios to predict the equity premium," Economic Modelling, Elsevier, vol. 66(C), pages 244-257.
    31. Swasti R. Khuntia & Jose L. Rueda & Mart A.M.M. Van der Meijden, 2018. "Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model," Energies, MDPI, vol. 11(12), pages 1-19, November.
    32. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    33. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    34. 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).
    35. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    36. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    37. Hui Chen & Scott Joslin & Sophie X. Ni, 2019. "Demand for Crash Insurance, Intermediary Constraints, and Risk Premia in Financial Markets," NBER Working Papers 25573, National Bureau of Economic Research, Inc.
    38. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    39. Biqing Cai & Jiti Gao, 2013. "Hermite Series Estimation in Nonlinear Cointegrating Models," Monash Econometrics and Business Statistics Working Papers 17/13, Monash University, Department of Econometrics and Business Statistics.
    40. He, Kaijian & Liu, Youjin & Yu, Lean & Lai, Kin Keung, 2016. "Multiscale dependence analysis and portfolio risk modeling for precious metal markets," Resources Policy, Elsevier, vol. 50(C), pages 224-233.
    41. Li, Chenchen & Wang, Yudong & Wu, Chongfeng, 2022. "Oil implied volatility and expected stock returns along the worldwide supply chain," Energy Economics, Elsevier, vol. 114(C).
    42. Bätje, Fabian & Menkhoff, Lukas, 2016. "Predicting the equity premium via its components," VfS Annual Conference 2016 (Augsburg): Demographic Change 145789, Verein für Socialpolitik / German Economic Association.
    43. Sakkas, Athanasios & Tessaromatis, Nikolaos, 2020. "Factor based commodity investing," Journal of Banking & Finance, Elsevier, vol. 115(C).
    44. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    45. Baltas, Nick & Karyampas, Dimitrios, 2018. "Forecasting the equity risk premium: The importance of regime-dependent evaluation," Journal of Financial Markets, Elsevier, vol. 38(C), pages 83-102.
    46. Markiewicz, Agnieszka & Pick, Andreas, 2014. "Adaptive learning and survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 685-707.
    47. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    48. Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
    49. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    50. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    51. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    52. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    53. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    54. Kolev, Gueorgui I. & Karapandza, Rasa, 2017. "Out-of-sample equity premium predictability and sample split–invariant inference," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 188-201.
    55. He, Kaijian & Wang, Lijun & Zou, Yingchao & Lai, Kin Keung, 2014. "Value at risk estimation with entropy-based wavelet analysis in exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 62-71.
    56. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    57. Chen, Jian & Jiang, Fuwei & Li, Hongyi & Xu, Weidong, 2016. "Chinese stock market volatility and the role of U.S. economic variables," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 70-83.
    58. Huang, Dashan & Li, Jiangyuan & Wang, Liyao & Zhou, Guofu, 2020. "Time series momentum: Is it there?," Journal of Financial Economics, Elsevier, vol. 135(3), pages 774-794.
    59. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    60. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, vol. 2(1), pages 1-20, March.
    61. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.
    62. Li, Xiaowei & Wu, Zhengyu & Zhang, Hao & Zhang, Lu, 2024. "Risk-neutral skewness and stock market returns: A time-series analysis," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    63. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Discussion Papers 46/2020, Deutsche Bundesbank.
    64. Ewa Feder-Sempach & Piotr Szczepocki & Wiesław Dębski, 2023. "What if beta is not stable? Applying the Kalman filter to risk estimates of top US companies over the long time horizon," Bank i Kredyt, Narodowy Bank Polski, vol. 54(1), pages 25-44.
    65. Fernald, John G. & Hsu, Eric & Spiegel, Mark M., 2021. "Reprint: Is China fudging its GDP figures? Evidence from trading partner data," Journal of International Money and Finance, Elsevier, vol. 114(C).
    66. Jing Tian & Qing Zhou, 2018. "Improving equity premium forecasts by incorporating structural break uncertainty," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 619-656, November.
    67. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    68. James Lightwood & Steve Anderson & Stanton A Glantz, 2020. "Predictive validation and forecasts of short-term changes in healthcare expenditure associated with changes in smoking behavior in the United States," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.

  16. 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. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
    2. 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.
    3. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    4. Zhang, Xiaoyun & Guo, Qiang, 2024. "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, vol. 60(C).
    5. 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.
    6. 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.
    7. 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, May.
    8. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    9. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2019. "High-Frequency Volatility Forecasting of US Housing Markets," Working Papers 201977, University of Pretoria, Department of Economics.
    10. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
    11. 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.
    12. 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).
    13. Mawuli Segnon & Rangan Gupta & Bernd Wilfling, 2022. "Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks," Working Papers 202203, University of Pretoria, Department of Economics.
    14. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    15. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    16. 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.
    17. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    18. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
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    Cited by:

    1. 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".
    2. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, December.
    3. Hafner, Christian & Preminger, Arie, 2015. "The effect of additive outliers on a fractional unit root test," LIDAM Discussion Papers ISBA 2015027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. 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. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    6. Daniel Preve, "undated". "Linear programming-based estimators in nonnegative autoregression," GRU Working Paper Series GRU_2016_001, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    7. Z. Merrick Li & Oliver Linton, 2022. "A ReMeDI for Microstructure Noise," Econometrica, Econometric Society, vol. 90(1), pages 367-389, January.
    8. 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.
    9. 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.
    10. 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.
    11. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, Department of Economics and Business Economics, Aarhus University.
    12. Eduardo Rossi & Paolo Santucci de Magistris, 2018. "Indirect inference with time series observed with error," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 874-897, September.
    13. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    14. La Spada Gabriele & Lillo Fabrizio, 2014. "The effect of round-off error on long memory processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 445-482, September.
    15. Dinghai Xu, 2021. "A study on volatility spurious almost integration effect: A threshold realized GARCH approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4104-4126, July.
    16. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    17. Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Estimation of long memory in integrated variance," CREATES Research Papers 2011-11, Department of Economics and Business Economics, Aarhus University.
    18. 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.
    19. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
    20. Ranaldo, Angelo & de Magistris, Paolo Santucci, 2022. "Liquidity in the global currency market," Journal of Financial Economics, Elsevier, vol. 146(3), pages 859-883.
    21. 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.
    22. 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.
    23. Kim, Jihyun & Meddahi, Nour, 2020. "Volatility regressions with fat tails," Journal of Econometrics, Elsevier, vol. 218(2), pages 690-713.
    24. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    25. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2021. "Option Pricing with State-dependent Pricing Kernel," Papers 2112.05308, arXiv.org, revised Apr 2022.
    26. 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.
    27. Kim, Jihyun & Meddahi, Nour, 2020. "Volatility Regressions with Fat Tails," TSE Working Papers 20-1097, Toulouse School of Economics (TSE).
    28. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    29. Jihyun Kim & Nour Meddahi, 2020. "Volatility Regressions with Fat Tails," Post-Print hal-03142647, HAL.
    30. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    31. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    32. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    33. 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.
    34. 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.

  18. 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. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    2. 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.
    3. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.
    4. 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.
    5. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    6. Kevin Sheppard & Wen Xu, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    7. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    9. 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.
    10. 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.
    11. Peter Christoffersen & Mathieu Fournier & Kris Jacobs, 2018. "The Factor Structure in Equity Options," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 595-637.
    12. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. 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.
    14. 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.

  19. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    2. 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.
    3. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    4. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
    5. 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.
    6. 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.
    7. Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," LEO Working Papers / DR LEO 2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    8. 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.
    9. Hansen, Peter R. & Lunde, Asger, 2014. "Estimating The Persistence And The Autocorrelation Function Of A Time Series That Is Measured With Error," Econometric Theory, Cambridge University Press, vol. 30(1), pages 60-93, February.
    10. Hansen, Peter Reinhard, 2015. "A martingale decomposition of discrete Markov chains," Economics Letters, Elsevier, vol. 133(C), pages 14-18.
    11. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.
    12. 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.
    13. 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.
    14. Tian, Shuairu & Hamori, Shigeyuki, 2015. "Modeling interest rate volatility: A Realized GARCH approach," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 158-171.
    15. Ole E. Barndorff-Nielsen & David G. Pollard & Neil Shephard, 2012. "Integer-valued L�vy processes and low latency financial econometrics," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 587-605, January.
    16. 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.
    17. Peter Reinhard Hansen & Zhuo (Albert) Huang & Howard Howan Shek, "undated". "Realized GARCH: A Complete Model of Returns and Realized Measures of Volatility," CREATES Research Papers 2010-13, Department of Economics and Business Economics, Aarhus University.

  20. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," CREATES Research Papers 2008-63, Department of Economics and Business Economics, Aarhus 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. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    3. 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.
    4. 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.
    5. 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.
    6. Boudt, Kris & Dragun, Kirill & Sauri, Orimar & Vanduffel, Steven, 2023. "ETF Basket-Adjusted Covariance estimation," Journal of Econometrics, Elsevier, vol. 235(2), pages 1144-1171.
    7. 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.
    8. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    9. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    10. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    11. Leonidas Tsiaras, 2010. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," CREATES Research Papers 2010-34, Department of Economics and Business Economics, Aarhus University.
    12. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2013. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 59-84.
    13. Gilder, Dudley & Shackleton, Mark B. & Taylor, Stephen J., 2014. "Cojumps in stock prices: Empirical evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 443-459.
    14. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Working Papers 201925, University of Pretoria, Department of Economics.
    15. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    16. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    17. 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.
    18. Markus Bibinger & Mathias Vetter, 2015. "Estimating the quadratic covariation of an asynchronously observed semimartingale with jumps," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 707-743, August.
    19. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    20. Chaker, Selma, 2019. "The signal and the noise volatilities," Research in International Business and Finance, Elsevier, vol. 50(C), pages 79-105.
    21. 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.
    22. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    23. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    24. Donggyu Kim & Xinyu Song & Yazhen Wang, 2020. "Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency," Papers 2006.12039, arXiv.org.
    25. Francis X. Diebold & Georg Strasser, 2013. "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(4), pages 1304-1337.
    26. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    27. Jianqing Fan & Yingying Li & Ke Yu, 2012. "Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
    28. Peter Reinhard Hansen & Chen Tong, 2024. "Convolution-t Distributions," Papers 2404.00864, arXiv.org.
    29. Manabu Asai & Michael McAleer, 2017. "The impact of jumps and leverage in forecasting covolatility," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 638-650, October.
    30. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    31. Jozef Baruník & Evžen Kocenda & Lukáš Vácha, 2015. "Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillover," CESifo Working Paper Series 5305, CESifo.
    32. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(3), pages 253-291, September.
    33. Barunik, Jozef & Vacha, Lukas, 2018. "Do co-jumps impact correlations in currency markets?," Journal of Financial Markets, Elsevier, vol. 37(C), pages 97-119.
    34. Usman Arief & Zaäfri Ananto Husodo, 2021. "Private Information from Extreme Price Movements (Empirical Evidences from Southeast Asia Countries)," International Symposia in Economic Theory and Econometrics, in: Recent Developments in Asian Economics International Symposia in Economic Theory and Econometrics, volume 28, pages 221-242, Emerald Group Publishing Limited.
    35. Altmeyer, Randolf & Bibinger, Markus, 2015. "Functional stable limit theorems for quasi-efficient spectral covolatility estimators," Stochastic Processes and their Applications, Elsevier, vol. 125(12), pages 4556-4600.
    36. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    37. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    38. 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.
    39. Kim Christensen & Charlotte Christiansen & Anders M. Posselt, 2019. "The Economic Value of VIX ETPs," CREATES Research Papers 2019-14, Department of Economics and Business Economics, Aarhus University.
    40. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Econometrics Working Papers Archive 2016_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    41. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    42. Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, vol. 231(2), pages 348-360.
    43. Bastian Gribisch, 2018. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Empirical Economics, Springer, vol. 55(2), pages 621-651, September.
    44. 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.
    45. Cai, T. Tony & Hu, Jianchang & Li, Yingying & Zheng, Xinghua, 2020. "High-dimensional minimum variance portfolio estimation based on high-frequency data," Journal of Econometrics, Elsevier, vol. 214(2), pages 482-494.
    46. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    47. 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.
    48. Andrew Ang & Dennis Kristensen, 2011. "Testing Conditional Factor Models," NBER Working Papers 17561, National Bureau of Economic Research, Inc.
    49. Štefan Lyócsa & Peter Molnár, 2016. "Volatility forecasting of strategically linked commodity ETFs: gold-silver," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1809-1822, December.
    50. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    51. Stanislav Anatolyev & Nikita Kobotaev, 2018. "Modeling and forecasting realized covariance matrices with accounting for leverage," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 114-139, February.
    52. 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.
    53. 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).
    54. 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.
    55. 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).
    56. 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.
    57. 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).
    58. 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.
    59. 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.
    60. Wang, Kent & Liu, Junwei & Liu, Zhi, 2013. "Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1777-1786.
    61. Markus Bibinger & Per A. Mykland, 2016. "Inference for Multi-dimensional High-frequency Data with an Application to Conditional Independence Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1078-1102, December.
    62. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    63. Kirill Dragun & Kris Boudt & Orimar Sauri & Steven Vanduffel, 2021. "Beta-Adjusted Covariance Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1010, Ghent University, Faculty of Economics and Business Administration.
    64. Diebold, Francis X. & Strasser, Georg H., 2008. "On the correlation structure of microstructure noise in theory and practice," CFS Working Paper Series 2008/32, Center for Financial Studies (CFS).
    65. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    66. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    67. Masato Ubukata & Toshiaki Watanabe, 2014. "Market variance risk premiums in Japan for asset predictability," Empirical Economics, Springer, vol. 47(1), pages 169-198, August.
    68. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    69. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    70. Shinichiro Shirota & Yasuhiro Omori & Hedibert. F. Lopes & Haixiang Piao, 2016. "Cholesky Realized Stochastic Volatility Model," CIRJE F-Series CIRJE-F-1019, CIRJE, Faculty of Economics, University of Tokyo.
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    205. 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).
    206. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
    207. Ogihara, Teppei, 2021. "Misspecified diffusion models with high-frequency observations and an application to neural networks," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 245-292.
    208. 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.
    209. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    210. Jian Zhou, 2017. "Forecasting REIT volatility with high-frequency data: a comparison of alternative methods," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2590-2605, June.
    211. Zhi Liu, 2017. "Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations," Finance and Stochastics, Springer, vol. 21(2), pages 427-469, April.
    212. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
    213. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
    214. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    215. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
    216. Arnab Chakrabarti & Rituparna Sen, 2023. "Copula Estimation for Nonsynchronous Financial Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 116-149, May.
    217. Cui, Wenhao & Hu, Jie & Wang, Jiandong, 2024. "Nonparametric estimation for high-frequency data incorporating trading information," Journal of Econometrics, Elsevier, vol. 240(1).
    218. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    219. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    220. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Impact of the tick-size on financial returns and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4828-4843.
    221. 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.
    222. Flavia Barsotti & Simona Sanfelici, 2016. "Market Microstructure Effects on Firm Default Risk Evaluation," Econometrics, MDPI, vol. 4(3), pages 1-31, July.
    223. Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
    224. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
    225. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
    226. 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.
    227. Philip L. H. Yu & W. K. Li & F. C. Ng, 2017. "The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 513-527, October.
    228. Shen, Yiwen & Shi, Meiqi, 2024. "Intraday variation in cross-sectional stock comovement and impact of index-based strategies," Journal of Financial Markets, Elsevier, vol. 68(C).
    229. 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.
    230. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    231. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    232. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    233. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    234. Giacomo Toscano & Giulia Livieri & Maria Elvira Mancino & Stefano Marmi, 2021. "Volatility of volatility estimation: central limit theorems for the Fourier transform estimator and empirical study of the daily time series stylized facts," Papers 2112.14529, arXiv.org, revised Sep 2022.
    235. 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.
    236. Ingmar Nolte & Valeri Voev, 2011. "Least Squares Inference on Integrated Volatility and the Relationship Between Efficient Prices and Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 94-108, April.
    237. Neil Shephard, 2020. "An estimator for predictive regression: reliable inference for financial economics," Papers 2008.06130, arXiv.org.
    238. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
    239. Bandi, Federico M. & Pirino, Davide & Renò, Roberto, 2024. "Systematic staleness," Journal of Econometrics, Elsevier, vol. 238(1).
    240. Haugom, Erik & Lien, Gudbrand & Veka, Steinar & Westgaard, Sjur, 2014. "Covariance estimation using high-frequency data: Sensitivities of estimation methods," Economic Modelling, Elsevier, vol. 43(C), pages 416-425.
    241. Mike Buckle & Jing Chen & Julian Williams, 2014. "How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 542-557, November.
    242. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    243. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.
    244. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.
    245. Ole Martin & Mathias Vetter, 2019. "Laws of large numbers for Hayashi–Yoshida-type functionals," Finance and Stochastics, Springer, vol. 23(3), pages 451-500, July.
    246. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).

  21. Peter Reinhard Hansen, 2008. "Reduced-Rank Regression: A Useful Determinant Identity," CREATES Research Papers 2008-02, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Bruno Feunou & Jean-Sébastien Fontaine & Abderrahim Taamouti & Roméo Tédongap, 2014. "Risk Premium, Variance Premium, and the Maturity Structure of Uncertainty," Review of Finance, European Finance Association, vol. 18(1), pages 219-269.
    2. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.

  22. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Subsampling realised kernels," Economics Papers 2006-W10, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. 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.
    2. Francis X. Diebold & Georg Strasser, 2013. "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(4), pages 1304-1337.
    3. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    4. Eleftheria Kafousaki & Stavros Degiannakis, 2023. "Forecasting VIX: the illusion of forecast evaluation criteria," Economics and Business Letters, Oviedo University Press, vol. 12(3), pages 231-240.
    5. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
    6. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    7. 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 Dec 2023.
    8. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    9. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    10. Diebold, Francis X. & Strasser, Georg H., 2008. "On the correlation structure of microstructure noise in theory and practice," CFS Working Paper Series 2008/32, Center for Financial Studies (CFS).
    11. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Realized Volatility Using Subsample Averaging," Working Papers 201410, University of California at Riverside, Department of Economics.
    12. 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.
    13. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    14. Jianfen Feng & Xiaowei Huang & Juyue Hou & Chunxia Wang & Yan Zeng, 2018. "Carbon Bond Pricing And Model Selection," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(02), pages 465-481, March.
    15. Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
    16. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
    17. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Alexandros Garefalakis & Nikolaos Sariannidis, 2020. "Greek sovereign crisis and European exchange rates: effects of news releases and their providers," Annals of Operations Research, Springer, vol. 294(1), pages 515-536, November.
    18. Nikolaus Hautsch & Dieter Hess & David Veredas, 2010. "The impact of macroeconomic news on quote adjustments, noise and informational volatility," Working Papers ECARES 2010-004, ULB -- Universite Libre de Bruxelles.
    19. 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.
    20. 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.
    21. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    22. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    23. Sharma, Prateek & Vipul,, 2016. "Forecasting stock market volatility using Realized GARCH model: International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 222-230.
    24. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
    25. Linlan Xiao & Vigdis Boasson & Sergey Shishlenin & Victoria Makushina, 2018. "Volatility forecasting: combinations of realized volatility measures and forecasting models," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1428-1441, March.
    26. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    27. 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.
    28. 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.
    29. Dimitrios I. Vortelinos, 2015. "Out‐of‐sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini‐futures markets," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 58-67, November.
    30. Greeshma Balabhadra & El Mehdi Ainasse & Pawel Polak, 2023. "High-Frequency Volatility Estimation with Fast Multiple Change Points Detection," Papers 2303.10550, arXiv.org, revised Jun 2024.
    31. Prateek Sharma & Swati Sharma, 2015. "Forecasting gains of robust realized variance estimators: evidence from European stock markets," Economics Bulletin, AccessEcon, vol. 35(1), pages 61-69.
    32. 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.
    33. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    34. 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.
    35. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.
    36. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
    37. Emilio Barucci & Davide Magno & Maria Elvira Mancino, 2012. "Fourier volatility forecasting with high-frequency data and microstructure noise," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 281-293, September.
    38. 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.
    39. 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.
    40. Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
    41. 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).
    42. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    43. Vortelinos, Dimitrios I. & Thomakos, Dimitrios D., 2013. "Nonparametric realized volatility estimation in the international equity markets," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 34-45.

  23. 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.

    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. 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.
    3. Maria Elvira Mancino & Tommaso Mariotti & Giacomo Toscano, 2022. "Asymptotic Normality for the Fourier spot volatility estimator in the presence of microstructure noise," Papers 2209.08967, arXiv.org.
    4. 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.
    5. Kanaya, Shin & Kristensen, Dennis, 2016. "Estimation Of Stochastic Volatility Models By Nonparametric Filtering," Econometric Theory, Cambridge University Press, vol. 32(4), pages 861-916, August.
    6. 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".
    7. 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.
    8. 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.
    9. 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.
    10. Bacry, E. & Delattre, S. & Hoffmann, M. & Muzy, J.F., 2013. "Some limit theorems for Hawkes processes and application to financial statistics," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2475-2499.
    11. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," KIER Working Papers 758, Kyoto University, Institute of Economic Research.
    12. Chen, Richard Y. & Mykland, Per A., 2017. "Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data," Journal of Econometrics, Elsevier, vol. 200(1), pages 79-103.
    13. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    14. Dette, Holger & Golosnoy, Vasyl & Kellermann, Janosch, 2022. "Correcting Intraday Periodicity Bias in Realized Volatility Measures," Econometrics and Statistics, Elsevier, vol. 23(C), pages 36-52.
    15. Boudt, Kris & Dragun, Kirill & Sauri, Orimar & Vanduffel, Steven, 2023. "ETF Basket-Adjusted Covariance estimation," Journal of Econometrics, Elsevier, vol. 235(2), pages 1144-1171.
    16. Nagapetyan, Artur, 2019. "Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 45-61.
    17. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    18. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2013. "On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 59-84.
    19. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2010. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," Working Papers 10-06, Duke University, Department of Economics.
    20. Ole E. Barndorff-Nielsen & David G. Pollard & Neil Shephard, 2010. "Integer-valued Lévy processes and low latency financial econometrics," CREATES Research Papers 2010-66, Department of Economics and Business Economics, Aarhus University.
    21. 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.
    22. Bianchi, Francesco & Gómez-Cram, Roberto & Kind, Thilo & Kung, Howard, 2023. "Threats to central bank independence: High-frequency identification with twitter," Journal of Monetary Economics, Elsevier, vol. 135(C), pages 37-54.
    23. Roberto Pascual & David Veredas, 2009. "Does the open limit order book matter in explaining informational volatility?," ULB Institutional Repository 2013/183777, ULB -- Universite Libre de Bruxelles.
    24. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    25. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
    26. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    27. 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.
    28. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    29. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    30. 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.
    31. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    32. Markus Bibinger & Mathias Vetter, 2015. "Estimating the quadratic covariation of an asynchronously observed semimartingale with jumps," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 707-743, August.
    33. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    34. Song, Xinyu & Kim, Donggyu & Yuan, Huiling & Cui, Xiangyu & Lu, Zhiping & Zhou, Yong & Wang, Yazhen, 2021. "Volatility analysis with realized GARCH-Itô models," Journal of Econometrics, Elsevier, vol. 222(1), pages 393-410.
    35. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    36. Chaker, Selma, 2019. "The signal and the noise volatilities," Research in International Business and Finance, Elsevier, vol. 50(C), pages 79-105.
    37. 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.
    38. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    39. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
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    604. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).
    605. Vortelinos, Dimitrios I. & Thomakos, Dimitrios D., 2013. "Nonparametric realized volatility estimation in the international equity markets," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 34-45.
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    607. Antoine Bouveret & Martin Haferkorn & Gaetano Marseglia & Onofrio Panzarino, 2022. "Flash crashes on sovereign bond markets – EU evidence," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 20, Bank of Italy, Directorate General for Markets and Payment System.
    608. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    609. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    610. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
    611. Randolf Altmeyer & Markus Bibinger, 2014. "Functional stable limit theorems for efficient spectral covolatility estimators," SFB 649 Discussion Papers SFB649DP2014-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    612. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    613. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    614. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    615. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    616. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    617. Dohyun Chun & Donggyu Kim, 2022. "State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 105-124, January.
    618. Minxian Yang, 2014. "The Risk Return Relationship: Evidence from Index Return and Realised Variance Series," Discussion Papers 2014-16, School of Economics, The University of New South Wales.
    619. Donggyu Kim, 2021. "Exponential GARCH-Ito Volatility Models," Papers 2111.04267, arXiv.org.
    620. 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.
    621. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    622. 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).
    623. Haugom, Erik & Lien, Gudbrand & Veka, Steinar & Westgaard, Sjur, 2014. "Covariance estimation using high-frequency data: Sensitivities of estimation methods," Economic Modelling, Elsevier, vol. 43(C), pages 416-425.
    624. Jos'e E. Figueroa-L'opez & Cheng Li, 2016. "Optimal Kernel Estimation of Spot Volatility of Stochastic Differential Equations," Papers 1612.04507, arXiv.org.
    625. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.
    626. Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy," Papers 1811.09312, arXiv.org, revised Jul 2022.
    627. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    628. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.
    629. Jonathan Haynes & Daniel Schmitt & Lukas Grimm, 2019. "Estimating stochastic volatility: the rough side to equity returns," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 449-469, December.
    630. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    631. Jean Jacod & Mark Podolskij & Mathias Vetter, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-61, Department of Economics and Business Economics, Aarhus University.
    632. Avouyi-Dovi, S. & Idier, J., 2010. "Central bank liquidity and market liquidity: the role of collateral provision on the French government debt securities market," Working papers 278, Banque de France.
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    635. Baltussen, Guido & Da, Zhi & Lammers, Sten & Martens, Martin, 2021. "Hedging demand and market intraday momentum," Journal of Financial Economics, Elsevier, vol. 142(1), pages 377-403.
    636. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.
    637. Silvia Goncalves & Nour Meddahi, 2008. "Edgeworth Corrections for Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 139-162.
    638. 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.
    639. 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.

  24. 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. McAleer, M.J. & Jiménez-Martín, J.A. & Pérez-Amaral, T., 2010. "GFC-Robust Risk Management Strategies under the Basel Accord," Econometric Institute Research Papers EI 2010-59, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. 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.
    3. 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.
    4. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    5. James M. Nason & Gregor W. Smith, 2005. "Identifying the New Keynesian Phillips curve," FRB Atlanta Working Paper 2005-01, Federal Reserve Bank of Atlanta.
    6. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CARF F-Series CARF-F-219, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. 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.
    8. Kevin Sheppard & Andrew J. Patton, 2008. "Evaluating Volatility and Correlation Forecasts," Economics Series Working Papers 2008fe22, University of Oxford, Department of Economics.
    9. 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.
    10. 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.
    11. Massimiliano Caporin & Michael McAleer, 2010. "Model Selection and Testing of Conditional and Stochastic Volatility Models," KIER Working Papers 724, Kyoto University, Institute of Economic Research.
    12. 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.
    13. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    14. 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.
    15. 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.
    16. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    17. Christian Dreger & Konstantin Arkadievich Kholodilin, 2013. "Forecasting Private Consumption by Consumer Surveys," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 10-18, January.
    18. 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.

  25. 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. Guglielmo Maria Caporale & Alex Plastun, 2016. "Calendar Anomalies in the Ukrainian Stock Market," CESifo Working Paper Series 5877, CESifo.
    2. 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.
    3. 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.
    4. Yoon-Jae Whang & Young-Hyun Cho & Oliver Linton, 2006. "Are there Monday effects in Stock Returns: A Stochastic Dominance Approach," FMG Discussion Papers dp568, Financial Markets Group.
    5. 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.
    6. 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.
    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. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

  26. 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. Ole Barndorff-Nielsen & Neil Shephard, 2004. "Multipower Variation and Stochastic Volatility," Economics Papers 2004-W30, Economics Group, Nuffield College, University of Oxford.
    2. 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.
    3. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    4. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    6. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    7. 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.
    8. 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.
    9. 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.
    10. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
    11. 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.
    12. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    13. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
    14. Torben G. Andersen & Luca Benzoni, 2010. "Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models," Journal of Finance, American Finance Association, vol. 65(2), pages 603-653, April.
    15. 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.
    16. 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.
    17. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    18. 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.
    19. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    20. Masato Ubukata & Toshiaki Watanabe, 2011. "Pricing Nikkei 225 Options Using Realized Volatility," IMES Discussion Paper Series 11-E-18, Institute for Monetary and Economic Studies, Bank of Japan.
    21. 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.
    22. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. Benlagha, Noureddine & Chargui, Sana, 2017. "Range-based and GARCH volatility estimation: Evidence from the French asset market," Global Finance Journal, Elsevier, vol. 32(C), pages 149-165.
    28. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    29. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.

  27. 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. Imane El Ouadghiri & Remzi Uctum, 2016. "Jumps in equilibrium prices and asymmetric news in foreign exchange markets," Post-Print hal-01386027, HAL.
    2. 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.
    3. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    5. 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.
    6. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
    7. 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.
    8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Ginger Wu, 2006. "Realized Beta: Persistence and Predictability," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 1-39, Emerald Group Publishing Limited.
    9. Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
    10. 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.
    11. 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.
    12. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
    13. 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.
    14. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
    15. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    16. 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.
    17. de Vilder, Robin G. & Visser, Marcel P., 2007. "Volatility Proxies for Discrete Time Models," MPRA Paper 4917, University Library of Munich, Germany.
    18. 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.
    19. 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.
    20. 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.
    21. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    22. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    23. 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.
    24. 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.
    25. 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.
    26. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    27. 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.
    28. 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," OFRC Working Papers Series 2004fe20, Oxford Financial Research Centre.
    29. 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.

  28. Peter Hansen, 2003. "Asymptotic Tests of Composite Hypotheses," Working Papers 2003-09, Brown University, Department of Economics.

    Cited by:

    1. C. Monfardini & J.M.C. Santos Silva, 2006. "What can we learn about correlations from multinomial probit estimates?," Working Papers 558, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Le-Yu Chen & Jerzy Szroeter, 2012. "Testing multiple inequality hypotheses: a smoothed indicator approach," CeMMAP working papers 16/12, Institute for Fiscal Studies.
    3. 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.
    4. Le-Yu Chen & Jerzy Szroeter, 2009. "Hypothesis testing of multiple inequalities: the method of constraint chaining," CeMMAP working papers CWP13/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    6. Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang & Xibin Zhang, 2015. "A new semiparametric test for superior predictive ability," Empirical Economics, Springer, vol. 48(1), pages 389-405, February.
    7. Oliver Linton & Kyungchul Song & Yoon-Jae Whang, 2009. "An Improved Bootstrap Test of Stochastic Dominance," Cowles Foundation Discussion Papers 1713, Cowles Foundation for Research in Economics, Yale University.
    8. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
    9. Park, Cheol-Ho & Irwin, Scott H., 2005. "A Reality Check on Technical Trading Rule Profits in US Futures Markets," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19039, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    10. Sriananthakumar, Sivagowry, 2015. "Approximate Non-Similar critical values based tests vs Maximized Monte Carlo tests," Economic Modelling, Elsevier, vol. 49(C), pages 387-394.
    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. Oliver Linton & Kyungchul Song & Yoon-Jae Whang, 2008. "Bootstrap Tests of Stochastic Dominance with AsymptoticSimilarity on the Boundary," STICERD - Econometrics Paper Series 527, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Romano, Joseph P. & Wolf, Michael, 2013. "Testing for monotonicity in expected asset returns," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 93-116.
    14. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    15. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    16. Christopher J. Bennett, 2009. "Consistent and Asymptotically Unbiased MinP Tests of Multiple Inequality Moment Restrictions," Vanderbilt University Department of Economics Working Papers 0908, Vanderbilt University Department of Economics.
    17. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    18. Christopher J. Bennett, 2009. "p-Value Adjustments for Asymptotic Control of the Generalized Familywise Error Rate," Vanderbilt University Department of Economics Working Papers 0905, Vanderbilt University Department of Economics.
    19. Christian Gourieroux & Joann Jasiak, 2006. "A Degeneracy in the Analysis of Volatility and Covolatility Effects," Working Papers 2006-30, Center for Research in Economics and Statistics.
    20. Nader Trabelsi & Aviral Kumar Tiwari, 2023. "CO2 Emission Allowances Risk Prediction with GAS and GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 775-805, February.
    21. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    22. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    23. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    24. Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang, 2013. "A New Test for Superior Predictive Ability," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

  29. Peter Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models:The Model Confidence Set Approach," Working Papers 2003-05, Brown University, Department of Economics.

    Cited by:

    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. 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.
    3. 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.
    4. Małgorzata Doman & Ryszard Doman, 2013. "Dynamic linkages between stock markets: the effects of crises and globalization," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 12(2), pages 87-112, August.
    5. 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.
    6. Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019. "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive 2019_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    7. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    8. Politis, Dimitris N & Thomakos, Dimitrios D, 2008. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," University of California at San Diego, Economics Working Paper Series qt982208kx, Department of Economics, UC San Diego.
    9. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    10. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    11. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    12. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    13. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    14. D Aromi & A Clements, 2018. "Media attention and crude oil volatility: Is there any 'new' news in the newspaper?," NCER Working Paper Series 118, National Centre for Econometric Research.
    15. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
    16. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    17. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    18. G. C. Livingston & Darfiana Nur, 2023. "Bayesian inference of multivariate-GARCH-BEKK models," Statistical Papers, Springer, vol. 64(5), pages 1749-1774, October.
    19. 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).
    20. 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.
    21. 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.
    22. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    23. Ralf Becker & Adam Clements & Robert O'Neill, 2018. "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns," Econometrics, MDPI, vol. 6(1), pages 1-27, February.
    24. Malgorzata Doman & Ryszard Doman, 2013. "The Dynamics and Strength of Linkages between the Stock Markets in the Czech Republic, Hungary and Poland after their EU Accession," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 13, pages 5-32.
    25. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    26. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    27. 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.
    28. Yu, Jize & Zhang, Li & Peng, Lijuan & Wu, Rui, 2023. "Which component of air quality index drives stock price volatility in China: a decomposition-based forecasting method," Finance Research Letters, Elsevier, vol. 51(C).
    29. J. Eduardo Vera-Vald'es, 2017. "On Long Memory Origins and Forecast Horizons," Papers 1712.08057, arXiv.org.
    30. Caporin, M. & McAleer, M.J., 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Econometric Institute Research Papers EI2012-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    31. L. Bauwens & E. Otranto, 2020. "Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models," Working Paper CRENoS 202007, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    32. R. Khalfaoui & M. Boutahar, 2012. "Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," Working Papers halshs-00793068, HAL.
    33. Francesco Audrino & Robert Fernholz & Roberto Ferretti, 2007. "A Forecasting Model for Stock Market Diversity," Annals of Finance, Springer, vol. 3(2), pages 213-240, March.
    34. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    35. Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023. "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, vol. 55(PB).
    36. BAUWENS, Luc & otranto, EDOARDO, 2013. "Modeling the dependence of conditional correlations on volatility," LIDAM Discussion Papers CORE 2013014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    37. Angelos T. Vouldis & Dimitrios P. Louzis, 2018. "Leading indicators of non-performing loans in Greece: the information content of macro-, micro- and bank-specific variables," Empirical Economics, Springer, vol. 54(3), pages 1187-1214, May.
    38. Audrino, Francesco & Trojani, Fabio, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
    39. Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
    40. 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.
    41. Malgorzata Doman & Ryszard Doman, 2011. "The Impact of the Exchange Rate Dynamics on the Dependencies in Global Stock Market," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 73-86.
    42. Syed Ali Asad Rizvi & Stephen J. Roberts & Michael A. Osborne & Favour Nyikosa, 2017. "A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes," Papers 1705.00891, arXiv.org.
    43. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    44. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    45. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    46. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    47. Ali Taiebnia & Shapour Mohammadi, 2023. "Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2045-2062, December.
    48. Clements, Adam & Liao, Yin, 2017. "Forecasting the variance of stock index returns using jumps and cojumps," International Journal of Forecasting, Elsevier, vol. 33(3), pages 729-742.
    49. Audrino, Francesco & Hu, Yujia, 2011. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Economics Working Paper Series 1138, University of St. Gallen, School of Economics and Political Science.
    50. Tobias Eckernkemper, 2018. "Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 63-117.
    51. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
    52. Zhu, Jiaji & Han, Wei & Zhang, Junchao, 2023. "Does climate risk matter for gold price volatility?," Finance Research Letters, Elsevier, vol. 58(PC).
    53. Rohini Grover & Susan Thomas, 2012. "Liquidity Considerations in Estimating Implied Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(8), pages 714-741, August.
    54. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    55. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    56. 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.
    57. 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.
    58. Mauro Bernardi & Leopoldo Catania, 2015. "The Model Confidence Set package for R," CEIS Research Paper 362, Tor Vergata University, CEIS, revised 17 Nov 2015.
    59. Adam Clements & Mark Bernard Doolan, 2020. "Combining multivariate volatility forecasts using weighted losses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 628-641, July.
    60. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    61. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series 80, National Centre for Econometric Research.
    62. Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Working Papers in Economics 11/23, University of Canterbury, Department of Economics and Finance.
    63. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    64. Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
    65. Trino-Manuel Ñíguez & Javier Perote, 2012. "Forecasting Heavy-Tailed Densities with Positive Edgeworth and Gram-Charlier Expansions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(4), pages 600-627, August.
    66. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2015. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," MPRA Paper 64503, University Library of Munich, Germany.
    67. Becker, Christoph & Schmidt, Wolfgang M., 2015. "How past market movements affect correlation and volatility," Journal of International Money and Finance, Elsevier, vol. 50(C), pages 78-107.
    68. Benjamin Poignard & Manabu Asai, 2022. "High-Dimensional Sparse Multivariate Stochastic Volatility Models," Papers 2201.08584, arXiv.org, revised May 2022.
    69. Bauwens, Luc & Otranto, Edoardo, 2020. "Nonlinearities and regimes in conditional correlations with different dynamics," LIDAM Reprints CORE 3128, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    70. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
    71. 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.
    72. Małgorzata Doman & Ryszard Doman, 2014. "Dynamic Linkages in the Pairs (GBP/EUR, USD/EUR) and (GBP/USD, EUR/USD): How Do They Change During a Day?," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(1), pages 33-56, March.
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    74. Renò, Roberto, 2008. "Nonparametric Estimation Of The Diffusion Coefficient Of Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1174-1206, October.
    75. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2013. "On the Benefits of Equicorrelation for Portfolio Allocation," NCER Working Paper Series 99, National Centre for Econometric Research.
    76. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    77. Sauraj Verma, 2021. "Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 130-142, April.
    78. Emilija Dzuverovic & Matteo Barigozzi, 2023. "Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices," Papers 2305.08488, arXiv.org, revised Jul 2024.
    79. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    80. Mila Andreani & Vincenzo Candila & Giacomo Morelli & Lea Petrella, 2021. "Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach," Risks, MDPI, vol. 9(8), pages 1-20, August.
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  30. Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.

    Cited by:

    1. Linton, Oliver & Maasoumi, Esfandiar & Whang, Yoon-Jae, 2002. "Consistent testing for stochastic dominance: a subsampling approach," LSE Research Online Documents on Economics 24927, London School of Economics and Political Science, LSE Library.
    2. Andrew Patton, 2002. "(IAM Series No 001) On the Out-Of-Sample Importance of Skewness and Asymetric Dependence for Asset Allocation," FMG Discussion Papers dp431, Financial Markets Group.
    3. Maasoumi, Esfandiar & Heshmati, Almas, 2005. "Evaluating Dominance Ranking of PSID Incomes by Various Household Attributes," IZA Discussion Papers 1727, Institute of Labor Economics (IZA).
    4. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2007. "Practical Volatility Modeling for Financial Market Risk Management," MPRA Paper 9790, University Library of Munich, Germany, revised 15 May 2008.
    5. Benoit Bellone, 2004. "Une lecture probabiliste du cycle d’affaires américain," Econometrics 0407002, University Library of Munich, Germany, revised 28 Mar 2005.
    6. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 130-168.
    7. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    8. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. 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.
    10. Fong, Wai Mun & Yong, Lawrence H. M., 2005. "Chasing trends: recursive moving average trading rules and internet stocks," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 43-76, January.
    11. Bouoiyour, Jamal & Selmi, Refk, 2014. "Exchange Uncertainty and Export Performance in Egypt: New Insights from Wavelet Decomposition and Optimal GARCH Model," MPRA Paper 59568, University Library of Munich, Germany, revised 2014.
    12. Daniel King & Ferdi Botha, 2014. "Modelling Stock Return Volatility Dynamics in Selected African Markets," Working Papers 410, Economic Research Southern Africa.
    13. esposito, francesco paolo & cummins, mark, 2015. "Multiple hypothesis testing of market risk forecasting models," MPRA Paper 64986, University Library of Munich, Germany.
    14. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics.
    15. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    16. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    17. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
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    19. Eugenie Hol & Siem Jan Koopman & Borus Jungbacker, 2004. "Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements," Computing in Economics and Finance 2004 342, Society for Computational Economics.
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    21. Dilip Nachane & Jose Clavel, 2008. "Forecasting interest rates: a comparative assessment of some second-generation nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 493-514.
    22. Boyle, Gerry & Conniffe, Denis & McQuinn, Kieran, 2005. "A New Mean Standard Deviation Utility Function and the Behaviour Towards Risk of Specialist Irish Agricultural Producers: 1988-1997," Research Technical Papers 5/RT/05, Central Bank of Ireland.
    23. Rambaccussing, Dooruj, 2010. "A real-time trading rule," MPRA Paper 27148, University Library of Munich, Germany.
    24. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, University Library of Munich, Germany.
    25. Roche, M.J. & McQuinn, K., 2002. "Grain Price Volatility in a Small Open Economy," Economics Department Working Paper Series n1130202.pdf, Department of Economics, National University of Ireland - Maynooth.
    26. Bruneau, C. & De Bandt, O. & Flageollet, A., 2003. "Forecasting Inflation in the Euro Area," Working papers 102, Banque de France.
    27. 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.
    28. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    29. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    30. Benoît Bellone, 2006. "Une lecture probabiliste du cycle d’affaires américain," Économie et Prévision, Programme National Persée, vol. 172(1), pages 63-81.
    31. Artis, Michael & Nachane, Dilip M & Hoffmann, Mathias & Clavel, Jose Garcia, 2007. "Analyzing Strongly Periodic Series in the Frequency Domain: A Comparison of Alternative Approaches with Applications," CEPR Discussion Papers 6517, C.E.P.R. Discussion Papers.

  31. 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. 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.
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    3. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
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    5. Thushari N. Vidanage & Fabrizio Carmignani & Tarlok Singh, 2017. "Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia," South Asian Journal of Macroeconomics and Public Finance, , vol. 6(2), pages 157-177, December.
    6. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    7. 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.
    8. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    9. McMillan, David G., 2009. "The confusing time-series behaviour of real exchange rates: Are asymmetries important?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 692-711, October.
    10. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    11. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
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    14. Bai, Xiwen & Lam, Jasmine Siu Lee, 2021. "Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
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    17. Wei, Yu & Wang, Yizhi & Vigne, Samuel A. & Ma, Zhenyu, 2023. "Alarming contagion effects: The dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    18. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020. "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
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    22. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
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    Cited by:

    1. Hjelm, Göran & Johansson, Martin W, 2002. "Structural Change in Fiscal Policy and The Permanence of Fiscal Contractions - The Case of Denmark and Ireland," Working Papers 2002:11, Lund University, Department of Economics.
    2. Philippe Andrade & Catherine Bruneau & Stephane Gregoir, 2000. "Testing for the Cointegration Rank when Some Cointegrating Directions are Shifting," Econometric Society World Congress 2000 Contributed Papers 1605, Econometric Society.
    3. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2001. "A simple method for testing cointegration subject to regime changes," NIPE Working Papers 15/2001, NIPE - Universidade do Minho.
    4. Vasco J. Gabriel & Luis F. Martins, 2000. "The Properties of Cointegration Tests in Models with Structural Change," NIPE Working Papers 1/2000, NIPE - Universidade do Minho.
    5. Andrade, Philippe & Bruneau, Catherine & Gregoir, Stephane, 2005. "Testing for the cointegration rank when some cointegrating directions are changing," Journal of Econometrics, Elsevier, vol. 124(2), pages 269-310, February.
    6. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2002. "Residual-based tests for cointegration and multiple regime shifts," NIPE Working Papers 7/2002, NIPE - Universidade do Minho.

  33. Hansen, Peter Reinhard, 2000. "The Johansen-Granger Representation Theorem: An Explicit Expression for I(1) Processes," University of California at San Diego, Economics Working Paper Series qt832256dg, Department of Economics, UC San Diego.

    Cited by:

    1. Maciej Bukowski & Grzegorz Koloch & Piotr Lewandowski, 2013. "Shocks and rigidities as determinants of CEE labour markets’ performance," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 21(3), pages 553-581, July.

  34. Peter Reinhard Hansen, 2000. "Structural Changes in the Cointegrated Vector Autoregressive Model," Working Papers 2000-20, Brown University, Department of Economics.

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    1. Mindy L. Mallory & Dermot J. Hayes & Scott H. Irwin, 2010. "How Market Efficiency and the Theory of Storage Link Corn and Ethanol Markets," Center for Agricultural and Rural Development (CARD) Publications 10-wp517, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    2. Håvard Hungnes, 2010. "Identifying Structural Breaks in Cointegrated Vector Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 551-565, August.
    3. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
    4. Byron Gangnes & Craig Parsons, 2004. "Have US-Japan Trade Agreements Made a Difference?," Working Papers 200403, University of Hawaii at Manoa, Department of Economics.
    5. Boswijk, H. P. & Zu, Y., 2013. "Testing for Cointegration with Nonstationary Volatility," Working Papers 13/08, Department of Economics, City University London.
    6. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Testing for co-integration in vector autoregressions with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 158(1), pages 7-24, September.
    7. H. Peter Boswijk & Yang Zu, 2022. "Adaptive Testing for Cointegration With Nonstationary Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 744-755, April.
    8. Lindback, Morten & Osmundsen, Petter & Øglend, Atle, 2013. "Shale Gas and the Relationship between U.S. Natural Gas, Liquified Petroleum Gases and Oil Market," UiS Working Papers in Economics and Finance 2013/5, University of Stavanger.
    9. Tillmann, Peter, 2007. "Inflation regimes in the US term structure of interest rates," Economic Modelling, Elsevier, vol. 24(2), pages 203-223, March.
    10. Carter, Colin A. & Smith, Aaron D., 2004. "The Market Effect of a Food Scare: The Case of Genetically Modified StarLink Corn," Working Papers 11997, University of California, Davis, Department of Agricultural and Resource Economics.
    11. Carlos José Fonseca Marinheiro, 2005. "Sustainability of Portuguese Fiscal Policy in Historical Perspective," Working Papers de Economia (Economics Working Papers) 32, Departamento de Economia, Gestão e Engenharia Industrial, Universidade de Aveiro.
    12. Atle Oglend & Morten E. Lindback & Petter Osmundsen, 2016. "Shale Gas Boom Affecting the Relationship Between LPG and Oil Prices," The Energy Journal, , vol. 37(1), pages 211-232, January.
    13. Changli He & Timo Terasvirta & Andres Gonzalez, 2009. "Testing Parameter Constancy in Stationary Vector Autoregressive Models Against Continuous Change," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 225-245.
    14. Caporin, Massimiliano & Fontini, Fulvio, 2017. "The long-run oil–natural gas price relationship and the shale gas revolution," Energy Economics, Elsevier, vol. 64(C), pages 511-519.
    15. Mahdi Asgari & Sayed H. Saghaian & Michael R. Reed, 2020. "The Impact of Energy Sector on Overshooting of Agricultural Prices," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 589-606, March.
    16. da Fonseca, Eder Lucio & Alencar, Airlane Pereira & Morettin, Pedro Alberto, 2019. "Time-varying cointegration model using wavelets," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 260-267.
    17. Perron, Pierre & Zhu, Xiaokang, 2005. "Structural breaks with deterministic and stochastic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 65-119.
    18. Zurbruegg, R. & Allsopp, L., 2004. "Purchasing power parity and the impact of the East Asian currency crisis," Journal of Asian Economics, Elsevier, vol. 15(4), pages 739-758, August.
    19. Marçal, Emerson Fernandes, 2014. "Desalinhamentos Cambiais, Interdependência, Crises, Guerras cambiais: Uma avaliação empírica," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(2), June.
    20. Miller, Stephen M. & Martins, Luis Filipe & Gupta, Rangan, 2019. "A Time-Varying Approach Of The Us Welfare Cost Of Inflation," Macroeconomic Dynamics, Cambridge University Press, vol. 23(2), pages 775-797, March.
    21. Ikechukwu D Nwaka & Kalu E Uma & Gulcay Tuna, 2015. "Trade openness and unemployment: Empirical evidence for Nigeria," The Economic and Labour Relations Review, , vol. 26(1), pages 117-136, March.
    22. Joscha Beckmann & Ansgar Belke & Michael Kühl, 2009. "How Stable Are Monetary Models of the Dollar-Euro Exchange Rate?: A Time-Varying Coefficient Approach," Discussion Papers of DIW Berlin 944, DIW Berlin, German Institute for Economic Research.
    23. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    24. Mahamitra Das & Nityananda Sarkar, 2020. "Revisiting the Anomalous Relationship between Inflation and Real Estate Investment Trust Returns in Presence of Structural Breaks: Empirical Evidence from the USA and the UK," International Journal of Economics and Financial Issues, Econjournals, vol. 10(1), pages 250-258.
    25. Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    26. Carter, Colin A. & Smith, Aaron D., 2006. "Estimating the Market Effect of a Food Scare: The Case of Genetically Modified StarLink Corn," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25447, International Association of Agricultural Economists.
    27. H. Peter Boswijk & Giuseppe Cavaliere & Luca De Angelis & A. M. Robert Taylor, 2023. "Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models," Econometric Reviews, Taylor & Francis Journals, vol. 42(9-10), pages 725-757, November.
    28. Fanelli, Luca & Paruolo, Paolo, 2007. "Speed of Adjustment in Cointegrated Systems," MPRA Paper 9174, University Library of Munich, Germany.
    29. Fabrizio Iacone, 2009. "A Semiparametric Analysis of the Term Structure of the US Interest Rates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 475-490, August.
    30. Takamitsu Kurita, 2019. "A Recursive Monte Carlo Study of Structural-Break Sensitivity of Adjustment Coefficients in Cointegrated VAR Systems," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 251-270, June.
    31. Eleyan, Mohammed I.Abu & Çatık, Abdurrahman Nazif & Balcılar, Mehmet & Ballı, Esra, 2021. "Are long-run income and price elasticities of oil demand time-varying? New evidence from BRICS countries," Energy, Elsevier, vol. 229(C).
    32. Tillmann, Peter, 2003. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Bonn Econ Discussion Papers 27/2003, University of Bonn, Bonn Graduate School of Economics (BGSE).
    33. Till Strohsal & Enzo Weber, 2014. "Mean-variance cointegration and the expectations hypothesis," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 1983-1997, November.
    34. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.
    35. Sugita, Katsuhiro & 杉田, 勝弘, 2006. "Bayesian Analysis of Dynamic Multivariate Models with Multiple Structural Breaks," Discussion Papers 2006-14, Graduate School of Economics, Hitotsubashi University.
    36. David Bernstein & Bent Nielsen, 2014. "Asymptotic theory for cointegration analysis when the cointegration rank is deficient," Economics Papers 2014-W06, Economics Group, Nuffield College, University of Oxford.
    37. Kurozumi, Eiji & 黒住, 英司 & Arai, Yoichi & 荒井, 洋一, 2005. "Efficient Estimation and Inference in Cointegrating Regressions with Structural Change," Discussion Papers 2004-09, Graduate School of Economics, Hitotsubashi University.
    38. Lukáš ČECHURA & Tereza TAUSSIGOVÁ, 2013. "Avian influenza and structural change in the Czech poultry industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(1), pages 38-47.
    39. Karsten Schweikert, 2022. "Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors," Papers 2201.05430, arXiv.org, revised Sep 2024.
    40. Yoichi Arai & Eiji Kurozumi, 2007. "Testing for the Null Hypothesis of Cointegration with a Structural Break," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 705-739.
    41. Barbara Rossi, 2007. "Expectations hypotheses tests at Long Horizons," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 554-579, November.
    42. Maki, Daiki, 2012. "Tests for cointegration allowing for an unknown number of breaks," Economic Modelling, Elsevier, vol. 29(5), pages 2011-2015.
    43. Emerson Fernandes Marçal & Priscila Fernandes Ribeiro, 2011. "Levado pelos Fundamentos? Estimando o Desalinhamento Cambial Norte-Americano a partir de Técnicas de Cointegração," Discussion Papers 1674, Instituto de Pesquisa Econômica Aplicada - IPEA.
    44. Emanuele BACCHIOCCHI & Luca FANELLI, 2012. "Identification in structural vector autoregressive models with structural changes," Departmental Working Papers 2012-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    45. Das, Mahamitra & Sarkar, Nityananda, 2019. "Revisiting the Anomalous Relationship between Inflation and REIT Returns in Presence of Structural Breaks: Empirical Evidence from the USA and the UK," MPRA Paper 95130, University Library of Munich, Germany, revised 05 Nov 2019.
    46. Kejriwal, Mohitosh & Perron, Pierre, 2010. "Testing for Multiple Structural Changes in Cointegrated Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 503-522.
    47. Götz, Linde & Qiu, Feng & Gervais, Jean-Philippe & Glauben, Thomas, 2013. "Export Restrictions and Multiple Spatial Price Equilibria: Export Quotas for Wheat in Ukraine," 53rd Annual Conference, Berlin, Germany, September 25-27, 2013 156135, German Association of Agricultural Economists (GEWISOLA).
    48. Marçal, Emerson Fernandes, 2013. "Exchange rate misalignments, interdependence, crises, and currency wars: an empirical assessment," Textos para discussão 348, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    49. Emanuele Bacchiocchi & Efrem Castelnuovo & Luca Fanelli, 2014. "Gimme a break! Identification and estimation of the macroeconomic effects of monetary policy shocks in the U.S," "Marco Fanno" Working Papers 0181, Dipartimento di Scienze Economiche "Marco Fanno".
    50. Damásio, Bruno & Nicolau, João, 2024. "Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    51. Bruinshoofd, W.A. & Candelon, B. & Raabe, K., 2005. "Banking sector strength and the transmission of currency crises," Research Memorandum 022, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    52. Peter Reinhard Hansen, 2008. "Reduced-Rank Regression: A Useful Determinant Identity," CREATES Research Papers 2008-02, Department of Economics and Business Economics, Aarhus University.
    53. Emerson Fernandes Marçal & Fernando Barbi, 2010. "“Quo Vadis Real? Estimating the Brazilian Real Exchange Rate Misalignment in Vector Error Correction Model with Structural Change”," Working Papers 10-2010, Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto.
    54. Sarno, Lucio & Daniel l Thornton & Giorgio Valente, 2003. "Federal Funds Rate Prediction," Royal Economic Society Annual Conference 2003 183, Royal Economic Society.
    55. Pereira, Pedro L. Valls, 2009. "Testing the long-run implications of the expectation hypothesis using cointegration techniques with structural change," Textos para discussão 175, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    56. PeterTillmann, 2004. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Computing in Economics and Finance 2004 53, Society for Computational Economics.
    57. Joscha Beckmann & Ansgar Belke & Michael Kühl, 2011. "Cointegration, structural breaks and monetary fundamentals of the Dollar/Yen Exchange," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 17(4), pages 397-412, November.
    58. Angelos Kanas, 2008. "Modeling regime transition in stock index futures markets and forecasting implications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 649-669.
    59. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
    60. Moss Charles B. & Schmitz Andrew, 2004. "Delineating the Relevant U.S. Sweetener Markets," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 2(1), pages 1-19, January.
    61. Joscha Beckmann & Ansgar Belke & Michael Kühl, 2011. "The dollar-euro exchange rate and macroeconomic fundamentals: a time-varying coefficient approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 147(1), pages 11-40, April.
    62. Martins, Luis F. & Gabriel, Vasco J., 2014. "Modelling long run comovements in equity markets: A flexible approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 288-295.
    63. Candelon, B. & Cubadda, G., 2005. "Testing for parameter stability in dynamic models across frequencies," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    64. Peter H. Sullivan, 2013. "Finding a Connection Between Exchange Rates and Fundamentals, How Should We Model Revisions to Forecasting Strategies?," 2013 Papers psu387, Job Market Papers.
    65. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, vol. 45(1), pages 605-625, August.
    66. Chowdhury, Kushal Banik & Garg, Bhavesh, 2022. "Has COVID-19 intensified the oil price–exchange rate nexus?," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 280-298.
    67. Sugita, Katsuhiro & 杉田, 勝弘, 2006. "Time Series Analysis of the Expectations Hypothesis for the Japanese Term Structure of Interest Rates in the Presence of Multiple Structural Breaks," Discussion Papers 2006-15, Graduate School of Economics, Hitotsubashi University.
    68. Georgoutsos, Dimitris & Kounitis, Thomas, 2016. "Treasury yields and credit spread dynamics: A regime-switching approach," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 39-51.
    69. Marçal, Emerson Fernandes & Pereira, Pedro L. Valls, 2012. "Evaluating the existence of structural change in the brazilian term structure of interest: evidence based on cointegration models with structural break," Textos para discussão 314, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    70. Bakucs, Zoltan & Ferto, Imre & Benedeka, Zsofia & Molnar, Adrienn, 2015. "Determinants of Horizontal Milk Producer Price Integration," 2015 Conference, August 9-14, 2015, Milan, Italy 212042, International Association of Agricultural Economists.
    71. Hsu, Chih-Chiang, 2008. "A note on tests of partial parameter stability in the cointegrated system," Economics Letters, Elsevier, vol. 99(3), pages 500-503, June.
    72. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks (Subsequently published in "Econometric Reviews", Volume 26, Issue 6 November 2007, pages 705 - 739. )," CARF F-Series CARF-F-022, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    73. Martins Luis Filipe & Gabriel Vasco J., 2013. "Time-varying cointegration, identification, and cointegration spaces," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 199-209, April.
    74. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    75. Joscha Beckmann & Michael Kühl, 2017. "The Role for Long-run Target Values of the Exchange Rate in the Bank of Japan's Policy Reaction Function," The World Economy, Wiley Blackwell, vol. 40(9), pages 1836-1865, September.
    76. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    77. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.
    78. Gotz, Linde & Qiu, Feng & Gervais, Jean-Philippe & Glauben, Thomas, 2012. "Export Restrictions And Multiple Spatial Price Equilibria When International Prices Spike: Export Quotas For Wheat In Ukraine," 2012: New Rules of Trade? December 2012, San Diego, California 143179, International Agricultural Trade Research Consortium.

  35. Peter Reinhard Hansen & Zhuo (Albert) Huang & Howard Howan Shek, "undated". "Realized GARCH: A Complete Model of Returns and Realized Measures of Volatility," CREATES Research Papers 2010-13, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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".
    2. Heejoon Han & Dennis Kristensen, 2012. "Asymptotic Theory for the QMLE in GARCH-X Models with Stationary and Non-Stationary Covariates," CREATES Research Papers 2012-25, Department of Economics and Business Economics, Aarhus University.
    3. Brendan K. Beare & Lawrence D. W. Schmidt, 2016. "An Empirical Test of Pricing Kernel Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 338-356, March.
    4. 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.
    5. 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.
    6. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    7. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.
    10. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    11. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Manabu Asai & Chia-Lin Chang & Michael McAleer, 2016. "Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers," Documentos de Trabajo del ICAE 2016-15, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    14. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    15. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.
    16. Masato Ubukata & Toshiaki Watanabe, 2011. "Pricing Nikkei 225 Options Using Realized Volatility," IMES Discussion Paper Series 11-E-18, Institute for Monetary and Economic Studies, Bank of Japan.
    17. 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.
    18. Giampiero M. Gallo & Edoardo Otranto, 2012. "Realized Volatility and Change of Regimes," Econometrics Working Papers Archive 2012_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Jul 2012.
    19. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    20. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.

Articles

  1. Ilya Archakov & Peter Reinhard Hansen, 2024. "A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 1099-1113, July.
    See citations under working paper version above.
  2. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2024. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Journal of Financial Econometrics, Oxford University Press, vol. 22(1), pages 187-223.
    See citations under working paper version above.
  3. Tong, Chen & Hansen, Peter Reinhard, 2023. "Characterizing correlation matrices that admit a clustered factor representation," Economics Letters, Elsevier, vol. 233(C).
    See citations under working paper version above.
  4. Peter Reinhard Hansen, 2022. "Relative contagiousness of emerging virus variants: An analysis of the Alpha, Delta, and Omicron SARS-CoV-2 variants [Increased risk of hospitalisation associated with infection with SARS-CoV-2 lin," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 739-761.
    See citations under working paper version above.
  5. 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.
    See citations under working paper version above.
  6. Hansen, Peter Reinhard & Dumitrescu, Elena-Ivona, 2022. "How should parameter estimation be tailored to the objective?," Journal of Econometrics, Elsevier, vol. 230(2), pages 535-558.
    See citations under working paper version above.
  7. Peter Reinhard Hansen & Matthias Schmidtblaicher, 2021. "A Dynamic Model of Vaccine Compliance: How Fake News Undermined the Danish HPV Vaccine Program," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 259-271, January.

    Cited by:

    1. Zenou, Yves & Giulietti, Corrado & Vlassopoulos, Michael, 2021. "When Reality Bites: Local Deaths and Vaccine Take-Up," CEPR Discussion Papers 16791, C.E.P.R. Discussion Papers.
    2. Cem Cakmakli & Yasin Simsek, 2023. "Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model," Papers 2301.13692, arXiv.org.
    3. Belmonte, A & Pickard, H, 2022. "Safe at Last? LATE Effects of a Mass Immunization Campaign on Households’ Economic Insecurity," CAGE Online Working Paper Series 604, Competitive Advantage in the Global Economy (CAGE).
    4. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    5. Serge Blondel & François Langot & Judith E. Mueller & Jonathan Sicsic, 2021. "Preferences and Covid-19 Vaccination Intentions," Working Papers hal-03381425, HAL.
    6. Giulietti, Corrado & Vlassopoulos, Michael & Zenou, Yves, 2023. "When reality bites: Local deaths and vaccine take-up," European Economic Review, Elsevier, vol. 156(C).

  8. Ilya Archakov & Peter Reinhard Hansen, 2021. "A New Parametrization of Correlation Matrices," Econometrica, Econometric Society, vol. 89(4), pages 1699-1715, July.
    See citations under working paper version above.
  9. P Gorgi & P R Hansen & P Janus & S J Koopman, 2019. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 1-32.
    See citations under working paper version above.
  10. Zhuo Huang & Tianyi Wang & Peter Reinhard Hansen, 2017. "Option Pricing with the Realized GARCH Model: An Analytical Approximation Approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(4), pages 328-358, April.

    Cited by:

    1. Wu, Xinyu & Xia, Michelle & Zhang, Huanming, 2020. "Forecasting VaR using realized EGARCH model with skewness and kurtosis," Finance Research Letters, Elsevier, vol. 32(C).
    2. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2021. "Forecasting the volatility of asset returns: The informational gains from option prices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 862-880.
    3. Haibin Xie & Xinyu Wu & Pengying Fan, 2021. "Accelerating FHS Option Pricing Under Linear GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 395-411, August.
    4. Yan Liu & Xiong Zhang, 2023. "Option Pricing Using LSTM: A Perspective of Realized Skewness," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    5. Fang Liang & Lingshan Du & Zhuo Huang, 2023. "Option pricing with overnight and intraday volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1576-1614, November.
    6. Chen Tong & Zhuo Huang & Tianyi Wang, 2022. "Do VIX futures contribute to the valuation of VIX options?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1644-1664, September.
    7. Wenjun Zhang & Jin E. Zhang, 2020. "GARCH Option Pricing Models and the Variance Risk Premium," JRFM, MDPI, vol. 13(3), pages 1-21, March.
    8. Mozumder, Sharif & Frijns, Bart & Talukdar, Bakhtear & Kabir, M. Humayun, 2024. "On practitioners closed-form GARCH option pricing," International Review of Financial Analysis, Elsevier, vol. 94(C).
    9. Peter Reinhard Hansen & Zhuo Huang & Chen Tong & Tianyi Wang, 2024. "Realized GARCH, CBOE VIX, and the Volatility Risk Premium," Journal of Financial Econometrics, Oxford University Press, vol. 22(1), pages 187-223.
    10. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Aug 2024.
    11. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
    12. Junting Liu & Qi Wang & Yuanyuan Zhang, 2024. "VIX option pricing through nonaffine GARCH dynamics and semianalytical formula," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1189-1223, July.
    13. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    14. Chen Tong & Zhuo Huang, 2021. "Pricing VIX options with realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1180-1200, August.
    15. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2021. "Option Pricing with State-dependent Pricing Kernel," Papers 2112.05308, arXiv.org, revised Apr 2022.
    16. Moritz Jirak, 2021. "Edgeworth expansions for volatility models," Papers 2111.00529, arXiv.org, revised Sep 2022.
    17. Tianyi Wang & Sicong Cheng & Fangsheng Yin & Mei Yu, 2022. "Overnight volatility, realized volatility, and option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1264-1283, July.
    18. Fang Liang & Lingshan Du, 2024. "Option pricing with dynamic conditional skewness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1154-1188, July.
    19. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 271-285.
    20. Qiao, Gaoxiu & Yang, Jiyu & Li, Weiping, 2020. "VIX forecasting based on GARCH-type model with observable dynamic jumps: A new perspective," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    21. Xie, Haibin & Yu, Chengtan, 2020. "Realized GARCH models: Simpler is better," Finance Research Letters, Elsevier, vol. 33(C).
    22. Qiao, Gaoxiu & Jiang, Gongyue & Yang, Jiyu, 2022. "VIX term structure forecasting: New evidence based on the realized semi-variances," International Review of Financial Analysis, Elsevier, vol. 82(C).
    23. 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).
    24. Fangsheng Yin & Yang Bian & Tianyi Wang, 2021. "A short cut: Directly pricing VIX futures with discrete‐time long memory model and asymmetric jumps," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 458-477, April.

  11. Peter Reinhard Hansen & Zhuo Huang, 2016. "Exponential GARCH Modeling With Realized Measures of Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 269-287, April.
    See citations under working paper version above.
  12. Peter Reinhard Hansen & Allan Timmermann, 2015. "Equivalence Between Out‐of‐Sample Forecast Comparisons and Wald Statistics," Econometrica, Econometric Society, vol. 83, pages 2485-2505, November.
    See citations under working paper version above.
  13. Hansen, Peter Reinhard, 2015. "A martingale decomposition of discrete Markov chains," Economics Letters, Elsevier, vol. 133(C), pages 14-18.
    See citations under working paper version above.
  14. Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2014. "Realized Beta Garch: A Multivariate Garch Model With Realized Measures Of Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 774-799, August.

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    3. Niels S. Grønborg & Asger Lunde & Kasper V. Olesen & Harry Vander Elst, 2018. "Realizing Correlations Across Asset Classes," CREATES Research Papers 2018-37, Department of Economics and Business Economics, Aarhus University.
    4. Sofiane Aboura & Julien Chevallier, 2014. "Cross-Market Spillovers with ‘Volatility Surprise’," EconomiX Working Papers 2014-46, University of Paris Nanterre, EconomiX.
    5. Chen, Qihao & Huang, Zhuo & Liang, Fang, 2023. "Measuring systemic risk with high-frequency data: A realized GARCH approach," Finance Research Letters, Elsevier, vol. 54(C).
    6. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    7. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    8. Peter Reinhard Hansen & Yiyao Luo, 2023. "Robust Estimation of Realized Correlation: New Insight about Intraday Fluctuations in Market Betas," Papers 2310.19992, arXiv.org.
    9. Song, Shijia & Tian, Fei & Li, Handong, 2021. "An intraday-return-based Value-at-Risk model driven by dynamic conditional score with censored generalized Pareto distribution," Journal of Asian Economics, Elsevier, vol. 74(C).
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    25. Fang Liang & Lingshan Du & Zhuo Huang, 2023. "Option pricing with overnight and intraday volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1576-1614, November.
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    6. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
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    239. Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
    240. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
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    244. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    245. Nikolaos Stoupos & Apostolos Kiohos, 2021. "BREXIT referendum’s impact on the financial markets in the UK," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 157(1), pages 1-19, February.
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  17. 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.
  18. 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.
  19. 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.
  20. 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. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. 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.
    3. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," KIER Working Papers 758, Kyoto University, Institute of Economic Research.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Evzen Kocenda & Vit Bubak & Filip Zikes, 2011. "Volatility Transmission in Emerging European Foreign Exchange Markets," William Davidson Institute Working Papers Series wp1020, William Davidson Institute at the University of Michigan.
    10. 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.
    11. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2009. "Asymmetry and Leverage in Realized Volatility," CARF F-Series CARF-F-167, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    12. 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.
    13. Ole E Barndorff-Nielsen & Peter Hansen & Asger Lunde & Neil Shephard, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," OFRC Working Papers Series 2006fe05, Oxford Financial Research Centre.
    14. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Eduardo Rossi & Paolo Santucci de Magistris, 2018. "Indirect inference with time series observed with error," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 874-897, September.
    16. 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.
    17. 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.
    18. 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.
    19. Bibinger, Markus & Hautsch, Nikolaus & Malec, Peter & Reiss, Markus, 2014. "Estimating the spot covariation of asset prices: Statistical theory and empirical evidence," CFS Working Paper Series 477, Center for Financial Studies (CFS).
    20. 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.
    21. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    22. Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
    23. Jim Griffin & Jia Liu & John M. Maheu, 2021. "Bayesian Nonparametric Estimation of Ex Post Variance [Out of Sample Forecasts of Quadratic Variation]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 823-859.
    24. 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.
    25. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.
    26. 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.
    27. 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.
    28. Li, Z. M. & Laeven, R. J. A. & Vellekoop, M. H., 2019. "Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data," Cambridge Working Papers in Economics 1952, Faculty of Economics, University of Cambridge.
    29. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    30. 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.
    31. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    32. 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.
    33. 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.
    34. Markus Bibinger & Markus Reiss & Nikolaus Hautsch & Peter Malec, 2014. "Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence," SFB 649 Discussion Papers SFB649DP2014-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    35. 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.
    36. 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).

  21. 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.
  22. 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. Yuriy Gorodnichenko & Michael Weber, 2016. "Are Sticky Prices Costly? Evidence from the Stock Market," American Economic Review, American Economic Association, vol. 106(1), pages 165-199, January.
    3. 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.
    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.

  23. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.

    Cited by:

    1. 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.
    2. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," OFRC Working Papers Series 2009fe03, Oxford Financial Research Centre.
    3. 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.
    4. Jérémy Leymarie & Christophe Hurlin & Antoine Patin, 2018. "Loss Functions for LGD Models Comparison," Post-Print hal-01923050, HAL.
    5. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    6. Jui-Cheng Hung & Tien-Wei Lou & Yi-Hsien Wang & Jun-De Lee, 2013. "Evaluating and improving GARCH-based volatility forecasts with range-based estimators," Applied Economics, Taylor & Francis Journals, vol. 45(28), pages 4041-4049, October.
    7. Leonidas Tsiaras, 2010. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," CREATES Research Papers 2010-34, Department of Economics and Business Economics, Aarhus University.
    8. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2010. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," Working Papers 10-06, Duke University, Department of Economics.
    9. Kaeck, Andreas & Rodrigues, Paulo & Seeger, Norman J., 2017. "Equity index variance: Evidence from flexible parametric jump–diffusion models," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 85-103.
    10. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    11. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    12. Heejoon Han & Myung D. Park & Shen Zhang, 2015. "A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 209-219, April.
    13. Politis, Dimitris N & Thomakos, Dimitrios D, 2008. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," University of California at San Diego, Economics Working Paper Series qt982208kx, Department of Economics, UC San Diego.
    14. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    15. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," LIDAM Discussion Papers CORE 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
    17. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CARF F-Series CARF-F-145, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    18. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    19. Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process," MPRA Paper 80489, University Library of Munich, Germany.
    20. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    21. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    22. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    23. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    24. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    25. Cordis, Adriana S. & Kirby, Chris, 2014. "Discrete stochastic autoregressive volatility," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 160-178.
    26. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    27. 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.
    28. 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.
    29. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    30. 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.
    31. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    32. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    33. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Realized Volatility Using Subsample Averaging," Working Papers 201410, University of California at Riverside, Department of Economics.
    34. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    1. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
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    541. Markku Lanne, 2006. "Forecasting Realized Volatility by Decomposition," Economics Working Papers ECO2006/20, European University Institute.
    542. Hasanov, Akram Shavkatovich & Poon, Wai Ching & Al-Freedi, Ajab & Heng, Zin Yau, 2018. "Forecasting volatility in the biofuel feedstock markets in the presence of structural breaks: A comparison of alternative distribution functions," Energy Economics, Elsevier, vol. 70(C), pages 307-333.
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    1. Håvard Hungnes, 2010. "Identifying Structural Breaks in Cointegrated Vector Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 551-565, August.
    2. Nielsen, Mikkel Slot, 2020. "On non-stationary solutions to MSDDEs: Representations and the cointegration space," Stochastic Processes and their Applications, Elsevier, vol. 130(5), pages 3154-3173.
    3. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    4. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2021. "Inference in heavy-tailed non-stationary multivariate time series," Papers 2107.13894, arXiv.org.
    5. Nhlangwini, Pamela & Mongale, Itumeleng Pleasure, 2019. "Mining Production and Economic Growth Nexus," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(3), pages 103-116.
    6. Hallin, M. & Werker, B.J.M. & van den Akker, R., 2015. "Optimal Pseudo-Gaussian and Rank-based Tests of the Cointegration Rank in Semiparametric Error-correction Models," Discussion Paper 2015-001, Tilburg University, Center for Economic Research.
    7. Edwige Burdeau, 2017. "Evaluating risks in the French office market with new sources of data on commercial property prices," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    8. Battulga Gankhuu, 2024. "Gordon Growth Model with Vector Autoregressive Process," Papers 2406.19424, arXiv.org, revised Jul 2024.
    9. Mikael Juselius & Moshe Kim & Staffan Ringbom, 2015. "Do markup dynamics reflect fundamentals or changes in conduct?," Empirical Economics, Springer, vol. 48(3), pages 1119-1147, May.
    10. Massimo Franchi & Paolo Paruolo, 2017. "A general inversion theorem for cointegration," DSS Empirical Economics and Econometrics Working Papers Series 2017/3, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    11. Linde, Jesper & Adolfson, Malin & LASEEN, PER & Villani, Mattias, 2007. "Evaluating An Estimated New Keynesian Small Open Economy Model," CEPR Discussion Papers 6027, C.E.P.R. Discussion Papers.
    12. Hansen, Peter Reinhard, 2015. "A martingale decomposition of discrete Markov chains," Economics Letters, Elsevier, vol. 133(C), pages 14-18.
    13. Marañon, Matias & Kumral, Mustafa, 2021. "Empirical analysis of Chile's copper boom and the Dutch Disease through causality and cointegration tests," Resources Policy, Elsevier, vol. 70(C).
    14. Jarner, Søren F. & Jallbjørn, Snorre, 2020. "Pitfalls and merits of cointegration-based mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 80-93.
    15. Takamitsu Kurita & Mototsugu Shintani, 2023. "Johansen Test with Fourier-Type Smooth Nonlinear Trends in Cointegrating Relations," CIRJE F-Series CIRJE-F-1216, CIRJE, Faculty of Economics, University of Tokyo.
    16. Ahlgren, Niklas & Juselius, Mikael, 2009. "Tests for Cointegration Rank and the Initial Condition," Working Papers 539, Hanken School of Economics.
    17. Anders Rygh Swensen, 2022. "On causal and non‐causal cointegrated vector autoregressive time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 178-196, March.
    18. Paruolo Paolo, 2005. "Design of vector autoregressive processes for invariant statistics," Economics and Quantitative Methods qf0504, Department of Economics, University of Insubria.
    19. Alexander Chudik & Vanessa Smith, 2013. "The GVAR approach and the dominance of the U.S. economy," Globalization Institute Working Papers 136, Federal Reserve Bank of Dallas.
    20. 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.
    21. Takamitsu Kurita & Bent Nielsen, 2019. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms," Econometrics, MDPI, vol. 7(4), pages 1-35, October.
    22. Jerome Coffinet & Etienne Kintzler, 2019. "Is the Office Market Overvalued? A Simple Framework Applied to France," International Real Estate Review, Global Social Science Institute, vol. 22(2), pages 275-306.
    23. Hallin, Marc & van den Akker, Ramon & Werker, Bas J.M., 2016. "Semiparametric error-correction models for cointegration with trends: Pseudo-Gaussian and optimal rank-based tests of the cointegration rank," Journal of Econometrics, Elsevier, vol. 190(1), pages 46-61.
    24. Swensen, Anders Rygh, 2011. "A bootstrap algorithm for testing cointegration rank in VAR models in the presence of stationary variables," Journal of Econometrics, Elsevier, vol. 165(2), pages 152-162.
    25. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2020. "Determining the rank of cointegration with infinite variance," Discussion Papers 20/01, University of Nottingham, Granger Centre for Time Series Econometrics.
    26. Carsten Trenkler & Enzo Weber, 2013. "Testing for codependence of cointegrated variables," Applied Economics, Taylor & Francis Journals, vol. 45(15), pages 1953-1964, May.
    27. McCauley, Joseph L. & Bassler, Kevin E. & Gunaratne, Gemunu H., 2009. "Is integration I(d) applicable to observed economics and finance time series?," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 101-108, June.
    28. Takamitsu Kurita & B. Nielsen, 2018. "Partial cointegrated vector autoregressive models with structural breaks in deterministic terms," Economics Papers 2018-W03, Economics Group, Nuffield College, University of Oxford.

  28. 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.

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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. 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.
    3. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    4. Leonidas Tsiaras, 2010. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," CREATES Research Papers 2010-34, Department of Economics and Business Economics, Aarhus University.
    5. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
    6. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    7. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    8. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    9. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    10. Zheng, Tingguo & Xiao, Han & Chen, Rong, 2015. "Generalized ARMA models with martingale difference errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 492-506.
    11. Peter Christoffersen & Bruno Feunou & Yoontae Jeon, 2014. "Option Valuation with Observable Volatility and Jump Dynamics," CREATES Research Papers 2015-07, Department of Economics and Business Economics, Aarhus University.
    12. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Kernel Technique for Forecasting the Variance-Covariance Matrix," Centre for Growth and Business Cycle Research Discussion Paper Series 151, Economics, The University of Manchester.
    13. Alexander Subbotin, 2008. "A multi-horizon scale for volatility," Post-Print halshs-00261514, HAL.
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    15. Lyócsa, Štefan & Todorova, Neda, 2020. "Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 628-645.
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    17. Ballinari, Daniele & Audrino, Francesco & Sigrist, Fabio, 2022. "When does attention matter? The effect of investor attention on stock market volatility around news releases," International Review of Financial Analysis, Elsevier, vol. 82(C).
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    21. Birkelund, Ole Henrik & Haugom, Erik & Molnár, Peter & Opdal, Martin & Westgaard, Sjur, 2015. "A comparison of implied and realized volatility in the Nordic power forward market," Energy Economics, Elsevier, vol. 48(C), pages 288-294.
    22. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    23. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.
    24. Torben G. Andersen & Tim Bollerslev & Xin Huang, 2007. "A Reduced Form Framework for Modeling Volatility of Speculative Prices based on Realized Variation Measures," CREATES Research Papers 2007-14, Department of Economics and Business Economics, Aarhus University.
    25. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
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    33. Linton, O. & Wu, J., 2016. "A coupled component GARCH model for intraday and overnight volatility," Cambridge Working Papers in Economics 1671, Faculty of Economics, University of Cambridge.
    34. Ahoniemi, Katja & Lanne, Markku, 2010. "Realized volatility and overnight returns," Bank of Finland Research Discussion Papers 19/2010, Bank of Finland.
    35. Anne Opschoor & André Lucas, 2019. "Observation-driven Models for Realized Variances and Overnight Returns," Tinbergen Institute Discussion Papers 19-052/IV, Tinbergen Institute.
    36. Dinghai Xu, 2010. "A Threshold Stochastic Volatility Model with Realized Volatility," Working Papers 1003, University of Waterloo, Department of Economics, revised May 2010.
    37. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    38. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.
    39. Liling Deng & Haifang Xiong & Zhiqiang Wang, 2023. "Research on cojumps of electronic commerce overnight factors in volatility prediction based on joint BW test," Electronic Commerce Research, Springer, vol. 23(1), pages 115-135, March.
    40. 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.
    41. Gaurav Raizada & Vartika Srivastava & S. V. D. Nageswara Rao, 2020. "Shall One Sit “Longer” for a Free Lunch? Impact of Trading Durations on the Realized Variances and Volatility Spillovers," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(1), pages 1-28, March.
    42. Chiriac, Roxana & Voev, Valeri, 2008. "Modelling and forecasting multivariate realized volatility," CoFE Discussion Papers 08/06, University of Konstanz, Center of Finance and Econometrics (CoFE).
    43. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
    44. Arnerić, Josip & Matković, Mario & Sorić, Petar, 2019. "Comparison of range-based volatility estimators against integrated volatility in European emerging markets," Finance Research Letters, Elsevier, vol. 28(C), pages 118-124.
    45. 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.
    46. Yi-Hao Lai & Yi-Chiuan Wang & Yu-Ching Chang, 2024. "Forecasting Trading-Session Return Volatility in Taiwan Futures Market: A Periodic Regime Switching with Jump Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(2), pages 285-305, June.
    47. Fang Liang & Lingshan Du & Zhuo Huang, 2023. "Option pricing with overnight and intraday volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1576-1614, November.
    48. Ewald, Christian & Hadina, Jelena & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Yahya, Muhammad, 2023. "Sample frequency robustness and accuracy in forecasting Value-at-Risk for Brent Crude Oil futures," Finance Research Letters, Elsevier, vol. 58(PA).
    49. 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.
    50. Masato Ubukata & Toshiaki Watanabe, 2014. "Pricing Nikkei 225 Options Using Realized Volatility," The Japanese Economic Review, Japanese Economic Association, vol. 65(4), pages 431-467, December.
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    59. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
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    61. Montero, José-María & Naimy, Viviane & Farraj, Nermeen Abi & El Khoury, Rim, 2024. "Natural disasters, stock price volatility in the property-liability insurance market and sustainability: An unexplored link," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
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    63. Bandi, Federico M. & Russell, Jeffrey R., 2011. "Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations," Journal of Econometrics, Elsevier, vol. 160(1), pages 145-159, January.
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    1. Strand,J., 2000. "Tax distortions, household production and black-market work," Memorandum 35/2000, Oslo University, Department of Economics.
    2. Thiess Büttner & Boryana Madzharova & Orlando Zaddach, 2022. "Income Tax Credits for Consumer Services: A Tool for Tackling VAT Evasion?," CESifo Working Paper Series 10054, CESifo.
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    5. Engström, Per & Holmlund, Bertil & Kolm, Ann-Sofie, 2001. "Optimal Taxation in Search Equilibrium with Home Production," Working Paper Series 2001:19, Uppsala University, Department of Economics.
    6. Garibaldi, Pietro & Wasmer, Etienne, 2003. "Equilibrium Search Unemployment, Endogenous Participation and Labour Market Flows," CEPR Discussion Papers 3986, C.E.P.R. Discussion Papers.
    7. Kleven, Henrik Jacobsen & Richter, Wolfram F & Sorensen, Peter Birch, 2000. "Optimal Taxation with Household Production," Oxford Economic Papers, Oxford University Press, vol. 52(3), pages 584-594, July.
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    1. Trenkler, Carsten, 2004. "Determining p-values for Systems Cointegration Tests With a Prior Adjustment for Deterministic Terms," Papers 2004,37, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    2. Philipp Gersing & Leopold Soegner & Manfred Deistler, 2022. "Retrieval from Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR," Papers 2204.05952, arXiv.org, revised Jul 2023.
    3. Hansen, Peter Reinhard, 2000. "The Johansen-Granger Representation Theorem: An Explicit Expression for I(1) Processes," University of California at San Diego, Economics Working Paper Series qt832256dg, Department of Economics, UC San Diego.
    4. Jean-Marie Dufour & Tarek Jouini, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," CIRANO Working Papers 2005s-26, CIRANO.
    5. Taher, Sumaiyah & Masih, Mansur, 2018. "Which market is the driver of the Asian stock markets ?," MPRA Paper 107975, University Library of Munich, Germany.
    6. Alain W. HECQ, 2005. "Common Trends and Common Cycles in Latin America: A 2-step vs an Iterative Approach," Computing in Economics and Finance 2005 258, Society for Computational Economics.
    7. Boswijk, H. P. & Zu, Y., 2013. "Testing for Cointegration with Nonstationary Volatility," Working Papers 13/08, Department of Economics, City University London.
    8. Søren Johansen, 2005. "Interpretation of Cointegrating Coefficients in the Cointegrated Vector Autoregressive Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(1), pages 93-104, February.
    9. H. Peter Boswijk & Yang Zu, 2022. "Adaptive Testing for Cointegration With Nonstationary Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 744-755, April.
    10. H. Peter Boswijk & Michael Jansson & Morten Ørregaard Nielsen, 2012. "Improved Likelihood Ratio Tests for Cointegration Rank in the VAR Model," CREATES Research Papers 2012-39, Department of Economics and Business Economics, Aarhus University.
    11. Tillmann, Peter, 2007. "Inflation regimes in the US term structure of interest rates," Economic Modelling, Elsevier, vol. 24(2), pages 203-223, March.
    12. Centoni, Marco & Cubadda, Gianluca & Hecq, Alain, 2007. "Common shocks, common dynamics, and the international business cycle," Economic Modelling, Elsevier, vol. 24(1), pages 149-166, January.
    13. Bevilacqua, Franco, 2006. "Random walks and cointegration relationships in international parity conditions between Germany and USA for the post Bretton-Woods period," MERIT Working Papers 2006-012, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Max Gillman & Michal Kejak & Giulia Ghiani, 2014. "Money, Banking and Interest Rates: Monetary Policy Regimes with Markov-Switching VECM Evidence," CEU Working Papers 2014_3, Department of Economics, Central European University.
    15. Corsetti, Giancarlo & Konstantinou, Panagiotis, 2009. "What Drives US Foreign Borrowing? Evidence on External Adjustment to Transitory and Permanent Shocks," CEPR Discussion Papers 7134, C.E.P.R. Discussion Papers.
    16. Møller, Niels Framroze, 2008. "Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-29.
    17. Alter, Adrian & Schüler, Yves S., 2012. "Credit spread interdependencies of European states and banks during the financial crisis," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3444-3468.
    18. Tillmann, Peter, 2003. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Bonn Econ Discussion Papers 27/2003, University of Bonn, Bonn Graduate School of Economics (BGSE).
    19. Gerald Eisenkopf & Zohal Hessami & Urs Fischbacher & Heinrich Ursprung, 2011. "Academic Performance and Single-Sex Schooling: Evidence from a Natural Experiment in Switzerland," TWI Research Paper Series 69, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    20. Noureddine Benlagha, 2013. "The Long-run Relationship among Index-linked Bonds and Conventional Bonds," Review of Economics & Finance, Better Advances Press, Canada, vol. 3, pages 15-24, February.
    21. Benlagha, N., 2013. "Co-movement of Index linked bonds and conventional bonds in France: Subprime crisis and Structural Break, 2003-01, 2012-04," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 13(1), pages 55-66.
    22. Alain Hecq & Franz Palm & Jean-Pierre Urbain, 2000. "Testing for Common Cyclical Features in Nonstationary Panel Data Models," CESifo Working Paper Series 248, CESifo.
    23. Trenkler, Carsten, 2009. "Bootstrapping Systems Cointegration Tests With A Prior Adjustment For Deterministic Terms," Econometric Theory, Cambridge University Press, vol. 25(1), pages 243-269, February.
    24. Bevilacqua, Franco, 2006. "Random walks and cointegration relationships in international parity conditions between Germany and USA for the Bretton-Woods period," MERIT Working Papers 2006-016, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    25. Charles G. Renfro, 2009. "The Practice of Econometric Theory," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75571-5, July-Dece.
    26. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    27. Hans Christian Kongsted & Heino Bohn Nielsen, 2004. "Analysing I(2) Systems by Transformed Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 379-397, July.
    28. 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.
    29. Peter Reinhard Hansen, 2008. "Reduced-Rank Regression: A Useful Determinant Identity," CREATES Research Papers 2008-02, Department of Economics and Business Economics, Aarhus University.
    30. PeterTillmann, 2004. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Computing in Economics and Finance 2004 53, Society for Computational Economics.
    31. Andreas Nastansky & Alexander Mehnert & Hans Gerhard Strohe, 2014. "A Vector Error Correction Model for the Relationship between Public Debt and Inflation in Germany," Statistische Diskussionsbeiträge 51, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    32. Alain Hecq & Franz Palm & Jean-Pierre Urbain, 2002. "Separation, Weak Exogeneity, And P-T Decomposition In Cointegrated Var Systems With Common Features," Econometric Reviews, Taylor & Francis Journals, vol. 21(3), pages 273-307.
    33. Peri, Massimo & Baldi, Lucia, 2013. "The effect of biofuel policies on feedstock market: Empirical evidence for rapeseed oil prices in EU," Resource and Energy Economics, Elsevier, vol. 35(1), pages 18-37.
    34. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
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