IDEAS home Printed from https://ideas.repec.org/e/c/poo13.html
   My authors  Follow this author

Roel C.A. Oomen

Citations

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

Working papers

  1. 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.

    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. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    3. Ilze Kalnina & Natalia Sizova, 2015. "Estimation of volatility measures using high frequency data (in Russian)," Quantile, Quantile, issue 13, pages 3-14, May.
    4. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    5. 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.
    6. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    7. 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.
    8. 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.
    9. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    10. 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.
    11. 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.
    12. Robert Azencott & Peng Ren & Ilya Timofeyev, 2017. "Realized volatility and parametric estimation of Heston SDEs," Papers 1706.04566, arXiv.org, revised Mar 2020.
    13. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    14. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Post-Print hal-00732537, HAL.
    15. Per A. Mykland & Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Papers 2012-W02, Economics Group, Nuffield College, University of Oxford.
    16. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    17. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    18. Yin Liao & Heather M. Anderson, 2011. "Testing for co-jumps in high-frequency financial data: an approach based on first-high-low-last prices," Monash Econometrics and Business Statistics Working Papers 9/11, Monash University, Department of Econometrics and Business Statistics.
    19. Li, Gang & Zhang, Chu, 2016. "On the relationship between conditional jump intensity and diffusive volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 196-213.
    20. Frowin Schulz & Karl Mosler, 2011. "The effect of infrequent trading on detecting price jumps," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 27-58, March.
    21. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    22. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    23. 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.
    24. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    25. Mancini, Cecilia, 2013. "Measuring the relevance of the microstructure noise in financial data," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2728-2751.
    26. Kim, Jihyun & Park, Joon & Wang, Bin, 2020. "Estimation of Volatility Functions in Jump Diffusions Using Truncated Bipower Increments," TSE Working Papers 20-1096, Toulouse School of Economics (TSE).
    27. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    28. Riza Demirer & Konstantinos Gkillas & Christos Kountzakis & Amaryllis Mavragani, 2020. "Risk Appetite and Jumps in Realized Correlation," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    29. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    30. José E. Figueroa-López & Jeffrey Nisen, 2019. "Second-order properties of thresholded realized power variations of FJA additive processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(3), pages 431-474, October.
    31. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    32. Gkillas, Konstantinos & Boako, Gideon & Vortelinos, Dimitrios & Vasiliadis, Lavrentios, 2020. "Non-parametric quantile dependencies between volatility discontinuities and political risk," Finance Research Letters, Elsevier, vol. 32(C).
    33. Qi Wang & Jos'e E. Figueroa-L'opez & Todd Kuffner, 2019. "Bayesian Inference on Volatility in the Presence of Infinite Jump Activity and Microstructure Noise," Papers 1909.04853, arXiv.org.
    34. 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.
    35. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    36. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    37. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2010. "Jump-robust volatility estimation using nearest neighbor truncation," Staff Reports 465, Federal Reserve Bank of New York.
    38. Konstantinos Gkillas & Rangan Gupta & Chi Keung Marco Lau & Tahir Suleman, 2018. "Jumps Beyond the Realms of Cricket: India’s Performance in One Day Internationals and Stock Market Movements," Working Papers 201871, University of Pretoria, Department of Economics.
    39. Cecilia Mancini, 2012. "Measuring the relevance of the microstructure noise in financial data," Working Papers - Mathematical Economics 2012-09, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    40. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    41. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    42. 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.
    43. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    44. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    45. Robert Azencott & Peng Ren & Ilya Timofeyev, 2020. "Realised volatility and parametric estimation of Heston SDEs," Finance and Stochastics, Springer, vol. 24(3), pages 723-755, July.
    46. Christophe Boucher & Gilles de Truchis & Elena Ivona Dumitrescu & Sessi Tokpavi, 2017. "Testing for Extreme Volatility Transmission with Realized Volatility Measures," Working Papers hal-04141651, HAL.
    47. 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.
    48. Shin Kanaya & Taisuke Otsu, 2011. "Large Deviations of Realized Volatility," Cowles Foundation Discussion Papers 1798, Cowles Foundation for Research in Economics, Yale University.
    49. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    50. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    51. Yingjie Dong & Yiu-Kuen Tse, 2017. "Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility," Econometrics, MDPI, vol. 5(4), pages 1-19, November.
    52. Christophe Boucher & Gilles de Truchis & Elena Dumitrescu & Sessi Tokpavi, 2017. "Testing for Extreme Volatility Transmission with Realized Volatility Measures," EconomiX Working Papers 2017-20, University of Paris Nanterre, EconomiX.
    53. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    54. 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.
    55. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    56. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

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

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. 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.
    4. 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.
    5. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    6. 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.
    7. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    8. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    9. 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.
    10. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    11. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    12. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Working Papers in Economics 14/10, University of Canterbury, Department of Economics and Finance.
    13. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
    15. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
    16. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    17. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    18. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    19. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    20. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    21. 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.
    22. 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.
    23. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    24. 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.
    25. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    26. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.
    27. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    28. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    29. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    30. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    31. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    32. Taras Bodnar & Nikolaus Hautsch & Yarema Okhrin & Nestor Parolya, 2024. "Consistent Estimation of the High-Dimensional Efficient Frontier," Papers 2409.15103, arXiv.org.
    33. Dong Hwan Oh & Andrew J. Patton, 2021. "Dynamic Factor Copula Models with Estimated Cluster Assignments," Finance and Economics Discussion Series 2021-029r1, Board of Governors of the Federal Reserve System (U.S.), revised 06 May 2022.
    34. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    35. 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.
    36. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    37. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
    38. 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.
    39. 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.
    40. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    41. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    42. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    43. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    44. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2021. "High-dimensional estimation of quadratic variation based on penalized realized variance," Papers 2103.03237, arXiv.org.
    45. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    46. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.
    47. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    48. 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).

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

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. 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.
    4. 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.
    5. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    6. 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.
    7. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    8. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    9. 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.
    10. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    11. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    12. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Working Papers in Economics 14/10, University of Canterbury, Department of Economics and Finance.
    13. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
    15. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
    16. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    17. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    18. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    19. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    20. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    21. 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.
    22. 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.
    23. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    24. 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.
    25. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    26. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.
    27. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    28. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    29. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    30. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," CREATES Research Papers 2014-42, Department of Economics and Business Economics, Aarhus University.
    31. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    32. Taras Bodnar & Nikolaus Hautsch & Yarema Okhrin & Nestor Parolya, 2024. "Consistent Estimation of the High-Dimensional Efficient Frontier," Papers 2409.15103, arXiv.org.
    33. Dong Hwan Oh & Andrew J. Patton, 2021. "Dynamic Factor Copula Models with Estimated Cluster Assignments," Finance and Economics Discussion Series 2021-029r1, Board of Governors of the Federal Reserve System (U.S.), revised 06 May 2022.
    34. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    35. 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.
    36. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    37. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
    38. 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.
    39. 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.
    40. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    41. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    42. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    43. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.
    44. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2021. "High-dimensional estimation of quadratic variation based on penalized realized variance," Papers 2103.03237, arXiv.org.
    45. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    46. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.
    47. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    48. 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).

  4. Roel C.A. Oomen, 2004. "Statistical Models for High Frequency Security Prices," Econometric Society 2004 North American Winter Meetings 77, Econometric Society.

    Cited by:

    1. Rangel, José Gonzalo, 2011. "Macroeconomic news, announcements, and stock market jump intensity dynamics," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1263-1276, May.
    2. John M. Maheu & Thomas McCurdy, 2003. "News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns," CIRANO Working Papers 2003s-38, CIRANO.
    3. Asger Lunde & Peter Reinhard Hansen, 2004. "Realized Variance and IID Market Microstructure Noise," Econometric Society 2004 North American Summer Meetings 526, Econometric Society.
    4. Jeffrey R. Russell & Federico M. Bandi, 2004. "Microstructure noise, realized volatility, and optimal sampling," Econometric Society 2004 Latin American Meetings 220, Econometric Society.

  5. Roel C.A. OOMEN, 2001. "Using high frequency stock market index data to calculate, model and forecast realized return variance," Economics Working Papers ECO2001/06, European University Institute.

    Cited by:

    1. Adam Clements & Yin Liao, 2014. "The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index," NCER Working Paper Series 101, National Centre for Econometric Research.
    2. 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.
    3. Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process," Economic Modelling, Elsevier, vol. 30(C), pages 212-216.
    4. 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.
    5. 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.
    6. GIOT, Pierre & LAURENT, Sébastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," LIDAM Reprints CORE 1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    8. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    9. Adam E Clements & Yin Liao, 2013. "Modeling and forecasting realized volatility: getting the most out of the jump component," NCER Working Paper Series 93, National Centre for Econometric Research.
    10. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    11. van Mierlo, J.G.A., 2001. "Over de verhouding tussen overheid, marktwerking en privatisering. Een economische meta-analyse," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    12. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
    13. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Viroj Jienwatcharamongkhol, 2019. "Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    14. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    15. 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.
    16. Roxana Halbleib & Valeri Voev, 2011. "Forecasting multivariate volatility using the VARFIMA model on realized covariance cholesky factors," ULB Institutional Repository 2013/195065, ULB -- Universite Libre de Bruxelles.
    17. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    18. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.

  6. Roel Oomen, 2001. "Using High Frequency Data to Calculate, Model and Forecast Realized Volatility," Computing in Economics and Finance 2001 75, Society for Computational Economics.

    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. Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process," Economic Modelling, Elsevier, vol. 30(C), pages 212-216.
    3. GIOT, Pierre & LAURENT, Sébastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," LIDAM Reprints CORE 1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    5. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    6. van Mierlo, J.G.A., 2001. "Over de verhouding tussen overheid, marktwerking en privatisering. Een economische meta-analyse," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    7. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    8. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    9. 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.

Articles

  1. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.

    Cited by:

    1. 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.
    2. Bibinger, Markus & Jirak, Moritz & Reiss, Markus, 2014. "Improved volatility estimation based on limit order books," SFB 649 Discussion Papers 2014-053, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Selma Chaker & Nour Meddahi, 2013. "A Distributional Approach to Realized Volatility," Staff Working Papers 13-49, Bank of Canada.
    4. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    5. Max O. Souza & Yuri Thamsten, 2021. "On regularized optimal execution problems and their singular limits," Papers 2101.02731, arXiv.org, revised Aug 2023.
    6. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    7. Jim Gatheral & Thibault Jaisson & Mathieu Rosenbaum, 2014. "Volatility is rough," Papers 1410.3394, arXiv.org.
    8. Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    9. Paul Bilokon & Yitao Qiu, 2023. "Transformers versus LSTMs for electronic trading," Papers 2309.11400, arXiv.org.
    10. 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.
    11. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    12. Frédéric Abergel & Aymen Jedidi, 2015. "Long-Time Behavior of a Hawkes Process--Based Limit Order Book," Post-Print hal-01121711, HAL.
    13. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    14. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
    15. Gabriel G. Drimus, 2012. "Options on realized variance by transform methods: a non-affine stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1679-1694, November.
    16. Guido Russi, 2012. "Estimating the Leverage Effect Using High Frequency Data," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 1-24, February.
    17. Giacomo Toscano & Maria Cristina Recchioni, 2022. "Bias-optimal vol-of-vol estimation: the role of window overlapping," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 137-185, June.
    18. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    19. Trent Spears & Stefan Zohren & Stephen Roberts, 2020. "Investment sizing with deep learning prediction uncertainties for high-frequency Eurodollar futures trading," Papers 2007.15982, arXiv.org.
    20. Ioane Muni Toke & Nakahiro Yoshida, 2017. "Modelling intensities of order flows in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 683-701, May.
    21. Frédéric Abergel & Aymen Jedidi, 2013. "A Mathematical Approach to Order Book Modelling," Post-Print hal-00621253, HAL.
    22. Masamitsu Ohnishi & Makoto Shimoshimizu, 2024. "Trade execution games in a Markovian environment," Papers 2405.07184, arXiv.org.
    23. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    24. Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
    25. Xie, Haibin & Yu, Chengtan, 2020. "Realized GARCH models: Simpler is better," Finance Research Letters, Elsevier, vol. 33(C).
    26. Giacomo Toscano & Maria Cristina Recchioni, 2020. "Bias optimal vol-of-vol estimation: the role of window overlapping," Papers 2004.04013, arXiv.org, revised Jul 2021.
    27. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    28. David Evangelista & Yuri Thamsten, 2023. "Approximately optimal trade execution strategies under fast mean-reversion," Papers 2307.07024, arXiv.org, revised Aug 2023.
    29. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    30. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
    31. Matthieu Garcin & Martino Grasselli, 2020. "Long vs Short Time Scales: the Rough Dilemma and Beyond," Papers 2008.07822, arXiv.org, revised Nov 2021.
    32. Andrew Todd & Peter Beling & William Scherer, 2016. "Crossed and Locked Quotes in a Multi-Market Simulation," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-19, March.
    33. Omar El Euch & Jim Gatheral & Radov{s} Radoiv{c}i'c & Mathieu Rosenbaum, 2018. "The Zumbach effect under rough Heston," Papers 1809.02098, arXiv.org.
    34. 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.
    35. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    36. Christian Bayer & Peter Friz & Jim Gatheral, 2016. "Pricing under rough volatility," Quantitative Finance, Taylor & Francis Journals, vol. 16(6), pages 887-904, June.

  2. Jim Griffin & Roel Oomen, 2008. "Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 230-253.

    Cited by:

    1. Juan C. Henao-Londono & Sebastian M. Krause & Thomas Guhr, 2021. "Price response functions and spread impact in correlated financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(4), pages 1-20, April.
    2. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.
    3. , 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
    4. 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.
    5. Vuorenmaa, Tommi A., 2008. "Decimalization, Realized Volatility, and Market Microstructure Noise," MPRA Paper 8692, University Library of Munich, Germany.
    6. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    7. Henker, Thomas & Husodo, Zaäfri A., 2010. "Noise and efficient variance in the Indonesia Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 18(2), pages 199-216, April.
    8. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    9. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    10. 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.
    11. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
    12. Ilia Negri & Yoichi Nishiyama, 2010. "Goodness of fit test for ergodic diffusions by tick time sample scheme," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 81-95, April.
    13. 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.
    14. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    15. 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.
    16. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    17. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
    18. Henao-Londono, Juan C. & Guhr, Thomas, 2022. "Foreign exchange markets: Price response and spread impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    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.
    20. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2010. "Jump-robust volatility estimation using nearest neighbor truncation," Staff Reports 465, Federal Reserve Bank of New York.
    21. 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.
    22. Mardi Dungey & Michael McKenzie & Vanessa Smith, 2007. "Empirical Evidence On Jumps In The Term Structure Of The Us Treasury Market," CAMA Working Papers 2007-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    23. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    24. Chang, Patrick & Pienaar, Etienne & Gebbie, Tim, 2021. "The Epps effect under alternative sampling schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    25. Hua, Jia-Chen & Chen, Lijian & Falcon, Liberty & McCauley, Joseph L. & Gunaratne, Gemunu H., 2015. "Variable diffusion in stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 221-233.
    26. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
    27. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    28. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
    29. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    30. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    31. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "The Epps effect under alternative sampling schemes," Papers 2011.11281, arXiv.org, revised Aug 2021.
    32. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    33. Ilia Negri & Yoichi Nishiyama, 2010. "Review on Goodness of Fit Tests for Ergodic Diffusion Processes by Different Sampling Schemes," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(1‐2), pages 91-106, February.
    34. Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
    35. Hsieh Fushing & Shu-Chun Chen & Chii-Ruey Hwang, 2012. "Discovering stock dynamics through multidimensional volatility phases," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 213-230, September.
    36. Istvan Barra & Siem Jan Koopman & Agnieszka Borowska, 2016. "Bayesian Dynamic Modeling of High-Frequency Integer Price Changes," Tinbergen Institute Discussion Papers 16-028/III, Tinbergen Institute, revised 16 Feb 2018.
    37. Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
    38. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.

  3. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.

    Cited by:

    1. Yu-Min Yen, 2013. "Testing Jumps via False Discovery Rate Control," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    2. Imane El Ouadghiri & Remzi Uctum, 2016. "Jumps in equilibrium prices and asymmetric news in foreign exchange markets," Post-Print hal-01386027, HAL.
    3. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The memory of stock return volatility: Asset pricing implications," Journal of Financial Markets, Elsevier, vol. 47(C).
    4. Tim Bollerslev & Michael Gibson & Hao Zhou, 2007. "Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities," CREATES Research Papers 2007-16, Department of Economics and Business Economics, Aarhus University.
    5. Turan G. Bali & Armen Hovakimian, 2009. "Volatility Spreads and Expected Stock Returns," Management Science, INFORMS, vol. 55(11), pages 1797-1812, November.
    6. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    7. Caporin, Massimiliano & Kolokolov, Alexey & Renò, Roberto, 2016. "Systemic co-jumps," SAFE Working Paper Series 149, Leibniz Institute for Financial Research SAFE.
    8. 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.
    9. Mardi Dungey & Lyudmyla Hvozdyk, 2010. "Cojumping: Evidence from the US Treasury Bond and Futures Markets," NCER Working Paper Series 56, National Centre for Econometric Research, revised 20 Jul 2010.
    10. Dräger, Lena & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The Long Memory of Equity Volatility and the Macroeconomy: International Evidence," Hannover Economic Papers (HEP) dp-667, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Imane El Ouadghiri & Remzi Uctum, 2015. "Jumps in Equilibrium Prices and Asymmetric News in Foreign Exchange Markets," Working Papers hal-04141414, HAL.
    12. , 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
    13. Gao, Ya & Han, Xing & Li, Youwei & Xiong, Xiong, 2019. "Overnight Momentum, Informational Shocks, and Late-Informed Trading in China," MPRA Paper 96784, University Library of Munich, Germany.
    14. Wang, Bin & Zheng, Xu, 2022. "Testing for the presence of jump components in jump diffusion models," Journal of Econometrics, Elsevier, vol. 230(2), pages 483-509.
    15. John Cotter & Stuart Gabriel & Richard Roll, 2011. "Integration and Contagion in US Housing Markets," Working Papers 201131, Geary Institute, University College Dublin.
    16. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    17. Kapadia, Nishad & Zekhnini, Morad, 2019. "Do idiosyncratic jumps matter?," Journal of Financial Economics, Elsevier, vol. 131(3), pages 666-692.
    18. Barunik, Jozef & Vacha, Lukas, 2018. "Do co-jumps impact correlations in currency markets?," Journal of Financial Markets, Elsevier, vol. 37(C), pages 97-119.
    19. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2017. "The Long Memory of Equity Volatility: International Evidence," Hannover Economic Papers (HEP) dp-614, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    20. Eric Jacquier & Cedric Okou, 2013. "Disentangling Continuous Volatility from Jumps in Long-Run Risk-Return Relationships," CIRANO Working Papers 2013s-14, CIRANO.
    21. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "High-Frequency Jump Tests: Which Test Should We Use?," Papers 1708.09520, arXiv.org, revised Jan 2020.
    22. Gnabo, Jean-Yves & Hvozdyk, Lyudmyla & Lahaye, Jérôme, 2014. "System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 147-174.
    23. Fung, Scott & Obaid, Khaled & Tsai, Shih-Chuan, 2024. "Information acquisition and processing skills of institutions and retail investors around information shocks," Journal of Empirical Finance, Elsevier, vol. 77(C).
    24. Dion Bongaerts & Richard Roll & Dominik Rösch & Mathijs van Dijk & Darya Yuferova, 2022. "How Do Shocks Arise and Spread Across Stock Markets? A Microstructure Perspective," Management Science, INFORMS, vol. 68(4), pages 3071-3089, April.
    25. Bilel Sanhaji & Julien Chevallier, 2023. "Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum," Econometrics, MDPI, vol. 11(3), pages 1-36, August.
    26. Li, Gang & Zhang, Chu, 2016. "On the relationship between conditional jump intensity and diffusive volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 196-213.
    27. Cui, Xin & Sensoy, Ahmet & Nguyen, Duc Khuong & Yao, Shouyu & Wu, Yiyao, 2022. "Positive information shocks, investor behavior and stock price crash risk," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 493-518.
    28. Frowin Schulz & Karl Mosler, 2011. "The effect of infrequent trading on detecting price jumps," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 27-58, March.
    29. Milan Fičura, 2019. "Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(4), pages 385-401.
    30. 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.
    31. Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
    32. Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
    33. Jian, Zhihong & Zhu, Zhican & Zhou, Jie & Wu, Shuai, 2020. "Intraday price jumps, market liquidity, and the magnet effect of circuit breakers," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 168-186.
    34. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2017. "Jumps in Commodity Markets," Hannover Economic Papers (HEP) dp-615, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    35. Aitor Ciarreta & Peru Muniain & Ainhoa Zarraga, 2020. "Realized volatility and jump testing in the Japanese electricity spot market," Empirical Economics, Springer, vol. 58(3), pages 1143-1166, March.
    36. Wing Hong Chan & Ranjini Jha & Madhu Kalimipalli, 2009. "The Economic Value Of Using Realized Volatility In Forecasting Future Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 231-259, September.
    37. Mancini, Cecilia, 2013. "Measuring the relevance of the microstructure noise in financial data," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2728-2751.
    38. Aitor Ciarreta & Peru Muniainy & Ainhoa Zarraga, 2017. "Modelling Realized Volatility in Electricity Spot Prices: New insights and Application to the Japanese Electricity Market," ISER Discussion Paper 0991, Institute of Social and Economic Research, Osaka University.
    39. Arouri, Mohamed & M’saddek, Oussama & Nguyen, Duc Khuong & Pukthuanthong, Kuntara, 2019. "Cojumps and asset allocation in international equity markets," Journal of Economic Dynamics and Control, Elsevier, vol. 98(C), pages 1-22.
    40. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    41. Chen, Xi & Wang, Junbo & Wu, Chunchi, 2022. "Jump and volatility risk in the cross-section of corporate bond returns," Journal of Financial Markets, Elsevier, vol. 60(C).
    42. Filipović, Damir & Gourier, Elise & Mancini, Loriano, 2016. "Quadratic variance swap models," Journal of Financial Economics, Elsevier, vol. 119(1), pages 44-68.
    43. Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
    44. Jan Hanousek & Evžen Kočenda & Jan Novotný, 2016. "Shluková analýza skoků na kapitálových trzích [Cluster Analysis of Jumps on Capital Markets]," Politická ekonomie, Prague University of Economics and Business, vol. 2016(2), pages 127-144.
    45. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    46. Gongyue Jiang & Gaoxiu Qiao & Feng Ma & Lu Wang, 2022. "Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1518-1548, August.
    47. Bibinger, Markus & Winkelmann, Lars, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers 2013-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    48. Dovonon, Prosper & Goncalves, Silvia & Hounyo, Ulrich & Meddahi, Nour, 2017. "Bootstrapping high-frequency jump tests," IDEI Working Papers 870, Institut d'Économie Industrielle (IDEI), Toulouse.
    49. José E. Figueroa-López & Jeffrey Nisen, 2019. "Second-order properties of thresholded realized power variations of FJA additive processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(3), pages 431-474, October.
    50. K. Victor Chow & Wanjun Jiang & Bingxin Li & Jingrui Li, 2020. "Decomposing the VIX: Implications for the predictability of stock returns," The Financial Review, Eastern Finance Association, vol. 55(4), pages 645-668, November.
    51. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    52. Chen, Zhongdong & Craig, Karen Ann, 2023. "Active attention, retail investor base, and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    53. Han, Seung-Oh & Huh, Sahn-Wook & Park, Jeayoung, 2023. "Detecting jumps amidst prevalent zero returns: Evidence from the U.S. Treasury securities," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 276-307.
    54. Yacine Aït-Sahalia & Jean Jacod, 2010. "Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data," NBER Working Papers 15808, National Bureau of Economic Research, Inc.
    55. Cecilia Mancini, 2012. "Measuring the relevance of the microstructure noise in financial data," Working Papers - Mathematical Economics 2012-09, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    56. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    57. Nkwoma, Inekwe John, 2017. "Futures-Based Measures Of Monetary Policy And Jump Risk," Macroeconomic Dynamics, Cambridge University Press, vol. 21(2), pages 384-405, March.
    58. Charles S. Bos & Paweł Janus & Siem Jan Koopman, 2012. "Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 354-389, 2012 06.
    59. Duong, Diep & Swanson, Norman R., 2015. "Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction," Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
    60. Hassan Zada & Huma Maqsood & Shakeel Ahmed & Muhammad Zeb Khan, 2023. "Information shocks, market returns and volatility: a comparative analysis of developed equity markets in Asia," SN Business & Economics, Springer, vol. 3(1), pages 1-22, January.
    61. M. Frömmel & X. Han & F. Van Gysegem, 2013. "News, Liquidity Dynamics and Intraday Jumps: Evidence from the HUF/EUR market," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/848, Ghent University, Faculty of Economics and Business Administration.
    62. Evans, Kevin P., 2011. "Intraday jumps and US macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2511-2527, October.
    63. Bin Wu & Pengzhan Chen & Wuyi Ye, 2021. "Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1055-1073, July.
    64. Amengual, Dante & Xiu, Dacheng, 2018. "Resolution of policy uncertainty and sudden declines in volatility," Journal of Econometrics, Elsevier, vol. 203(2), pages 297-315.
    65. Aït-Sahalia, Yacine & Jacod, Jean & Li, Jia, 2012. "Testing for jumps in noisy high frequency data," Journal of Econometrics, Elsevier, vol. 168(2), pages 207-222.
    66. González-Urteaga, Ana & Muga, Luis & Santamaria, Rafael, 2015. "Momentum and default risk. Some results using the jump component," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 185-193.
    67. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.
    68. John Cotter & Stuart Gabriel & Richard Roll, 2012. "Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust," Papers 1208.0371, arXiv.org.
    69. Sun, Bianxia & Gao, Yang, 2020. "Market liquidity and macro announcement around intraday jumps: Evidence from Chinese stock index futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    70. Zhou, Haigang & Zhu, John Qi, 2019. "Firm characteristics and jump dynamics in stock prices around earnings announcements," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    71. Fan, Yingying & Fan, Jianqing, 2011. "Testing and detecting jumps based on a discretely observed process," Journal of Econometrics, Elsevier, vol. 164(2), pages 331-344, October.
    72. Hassan Zada & Arshad Hassan & Wing-Keung Wong, 2021. "Do Jumps Matter in Both Equity Market Returns and Integrated Volatility: A Comparison of Asian Developed and Emerging Markets," Economies, MDPI, vol. 9(2), pages 1-26, June.
    73. Jiang, George J. & Zhu, Kevin X., 2017. "Information Shocks and Short-Term Market Underreaction," Journal of Financial Economics, Elsevier, vol. 124(1), pages 43-64.
    74. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    75. Haigang Zhou & John Zhu, 2011. "Jump risk and cross section of stock returns: evidence from China’s stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 35(3), pages 309-331, July.
    76. Worapree Maneesoonthorn & Gael M Martin & Catherine S Forbes, 2018. "Dynamic price jumps: The performance of high frequency tests and measures, and the robustness of inference," Monash Econometrics and Business Statistics Working Papers 17/18, Monash University, Department of Econometrics and Business Statistics.
    77. Dumitru, Ana-Maria & Hizmeri, Rodrigo & Izzeldin, Marwan, 2019. "Forecasting the Realized Variance in the Presence of Intraday Periodicity," EconStor Preprints 193631, ZBW - Leibniz Information Centre for Economics.
    78. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    79. Torben G. Andersen & Tim Bollerslev & Per Houmann Frederiksen & Morten Ørregaard Nielsen, 2007. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," CREATES Research Papers 2007-21, Department of Economics and Business Economics, Aarhus University.
    80. Aït-Sahalia, Yacine & Xiu, Dacheng, 2016. "Increased correlation among asset classes: Are volatility or jumps to blame, or both?," Journal of Econometrics, Elsevier, vol. 194(2), pages 205-219.
    81. Naeyoung Kang & Jungmu Kim, 2019. "An Empirical Analysis of Bitcoin Price Jump Risk," Sustainability, MDPI, vol. 11(7), pages 1-11, April.
    82. Yacine Aït-Sahalia & Julio Cacho-Diaz & Roger J.A. Laeven, 2010. "Modeling Financial Contagion Using Mutually Exciting Jump Processes," NBER Working Papers 15850, National Bureau of Economic Research, Inc.
    83. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    84. Ana-Maria Dumitru & Giovanni Urga, 2011. "Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 242-255, October.
    85. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    86. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    87. Juho Kanniainen & Ye Yue, 2019. "The Arrival of News and Return Jumps in Stock Markets: A Nonparametric Approach," Papers 1901.02691, arXiv.org.
    88. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    89. Yuewen Xiao & Xiangkang Yin & Jing Zhao, 2020. "Jumps, News, And Subsequent Return Dynamics: An Intraday Study," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(3), pages 705-731, August.
    90. Lena Cleanthous & Pany Karamanou, 2011. "The ECB Monetary Policy and the Current Financial Crisis," Working Papers 2011-1, Central Bank of Cyprus.
    91. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2015. "Modelling systemic price cojumps with Hawkes factor models," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1137-1156, July.
    92. Wen, Zhuzhu & Bouri, Elie & Xu, Yahua & Zhao, Yang, 2022. "Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    93. Yan, Shu, 2011. "Jump risk, stock returns, and slope of implied volatility smile," Journal of Financial Economics, Elsevier, vol. 99(1), pages 216-233, January.
    94. Massimiliano Caporin & Aleksey Kolokolov & Roberto RenoÕ, 2014. "Multi-jumps," "Marco Fanno" Working Papers 0185, Dipartimento di Scienze Economiche "Marco Fanno".
      • Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.
    95. Bjursell, Johan & Gentle, James E. & Wang, George H.K., 2015. "Inventory announcements, jump dynamics, volatility and trading volume in U.S. energy futures markets," Energy Economics, Elsevier, vol. 48(C), pages 336-349.
    96. Hollstein, Fabian & Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Asset prices and “the devil(s) you know”," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 20-35.
    97. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    98. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.
    99. Xin Zhang & Donggyu Kim & Yazhen Wang, 2016. "Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets," Econometrics, MDPI, vol. 4(3), pages 1-26, August.
    100. Jan Hanousek & Jan Novotný, 2014. "Cenové skoky během finanční nejistoty: od intuice k regulační perspektivě [Price Jumps during Financial Crisis: From Intuition to Financial Regulation]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(1), pages 32-48.
    101. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "Dynamic asset price jumps and the performance of high frequency tests and measures," Monash Econometrics and Business Statistics Working Papers 14/17, Monash University, Department of Econometrics and Business Statistics.
    102. Ao Kong & Robert Azencott & Hongliang Zhu & Xindan Li, 2020. "Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis," Papers 2011.04939, arXiv.org, revised Feb 2021.
    103. Li, Gang & Zhang, Chu, 2013. "Diagnosing affine models of options pricing: Evidence from VIX," Journal of Financial Economics, Elsevier, vol. 107(1), pages 199-219.
    104. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2013. "Modelling systemic price cojumps with Hawkes factor models," Papers 1301.6141, arXiv.org, revised Mar 2013.
    105. Pukthuanthong, Kuntara & Roll, Richard, 2012. "Internationally correlated jumps," Working Paper Series 1436, European Central Bank.
    106. Andersen, Torben G. & Li, Yingying & Todorov, Viktor & Zhou, Bo, 2023. "Volatility measurement with pockets of extreme return persistence," Journal of Econometrics, Elsevier, vol. 237(2).
    107. Cui, Yan & Yang, Jun & Zhou, Zhou, 2023. "State-domain change point detection for nonlinear time series regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 3-27.
    108. Song, Yuping & Huang, Jiefei & Zhang, Qichao & Xu, Yang, 2024. "Heterogeneity effect of positive and negative jumps on the realized volatility: Evidence from China," Economic Modelling, Elsevier, vol. 136(C).
    109. Torben B. Rasmussen, 2009. "Jump Testing and the Speed of Market Adjustment," CREATES Research Papers 2009-08, Department of Economics and Business Economics, Aarhus University.
    110. Aït-Sahalia, Yacine & Karaman, Mustafa & Mancini, Loriano, 2020. "The term structure of equity and variance risk premia," Journal of Econometrics, Elsevier, vol. 219(2), pages 204-230.
    111. Jan Novotný & Giovanni Urga, 2018. "Testing for Co-jumps in Financial Markets," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 118-128.
    112. Dumitru, Ana-Maria & Urga, Giovanni, 2016. "Jumps and Information Asymmetry in the US Treasury Market," EconStor Preprints 130148, ZBW - Leibniz Information Centre for Economics.
    113. Gupta, Varun & Perera, Sandun, 2021. "Managing surges in online demand using bandwidth throttling: An optimal strategy amid the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    114. Zhihong Jian & Zhican Zhu & Jie Zhou & Shuai Wu, 2018. "The Magnet Effect of Circuit Breakers: A role of price jumps and market liquidity," Departmental Working Papers 2018-01, The University of Winnipeg, Department of Economics.
    115. Meng, Yongqiang & Li, Xiao & Xiong, Xiong, 2024. "Information shocks and short-term market overreaction: The role of investor attention," International Review of Financial Analysis, Elsevier, vol. 93(C).

  4. George J. Jiang & Roel C. A. Oomen, 2007. "Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 1-30.

    Cited by:

    1. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
    2. Stanislav Khrapov, 2011. "Pricing Central Tendency in Volatility," Working Papers w0168, New Economic School (NES).
    3. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    4. Fung, Scott & Obaid, Khaled & Tsai, Shih-Chuan, 2024. "Information acquisition and processing skills of institutions and retail investors around information shocks," Journal of Empirical Finance, Elsevier, vol. 77(C).
    5. Fleming, Jeff & Paye, Bradley S., 2011. "High-frequency returns, jumps and the mixture of normals hypothesis," Journal of Econometrics, Elsevier, vol. 160(1), pages 119-128, January.
    6. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    7. Chiarella, Carl & Hung, Hing & T, Thuy-Duong, 2009. "The volatility structure of the fixed income market under the HJM framework: A nonlinear filtering approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2075-2088, April.
    8. Dempster, M.A.H. & Medova, Elena & Tang, Ke, 2018. "Latent jump diffusion factor estimation for commodity futures," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 35-54.
    9. esposito, francesco paolo & cummins, mark, 2015. "Filtering and likelihood estimation of latent factor jump-diffusions with an application to stochastic volatility models," MPRA Paper 64987, University Library of Munich, Germany.

  5. Oomen, Roel C.A., 2006. "Properties of Realized Variance Under Alternative Sampling Schemes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 219-237, April.

    Cited by:

    1. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    3. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    4. Fukasawa, Masaaki, 2010. "Realized volatility with stochastic sampling," Stochastic Processes and their Applications, Elsevier, vol. 120(6), pages 829-852, June.
    5. Chaboud, Alain P. & Chiquoine, Benjamin & Hjalmarsson, Erik & Loretan, Mico, 2010. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 212-240, March.
    6. Hautsch, Nikolaus & Podolskij, Mark, 2010. "Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence," CFS Working Paper Series 2010/17, Center for Financial Studies (CFS).
    7. Álvaro Cartea & Dimitrios Karyampas, 2009. "Volatility and Covariation of Financial Assets: A High-Frequency Analysis," Birkbeck Working Papers in Economics and Finance 0913, Birkbeck, Department of Economics, Mathematics & Statistics.
    8. 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.
    9. Vít Bubák & Evžen Kocenda & Filip Zikes, 2010. "Volatility Transmission in Emerging European Foreign Exchange Markets," CESifo Working Paper Series 3063, CESifo.
    10. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    11. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.
    12. , 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
    13. Christian T. Brownlees & Giampiero Gallo, 2006. "Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns," Econometrics Working Papers Archive wp2006_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    14. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    15. 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.
    16. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Post-Print hal-00732537, HAL.
    17. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    18. Yacine Ait-Sahalia & Jialin Yu, 2009. "High frequency market microstructure noise estimates and liquidity measures," Papers 0906.1444, arXiv.org.
    19. Aleksey Kolokolov & Giulia Livieri & Davide Pirino, 2022. "Testing for Endogeneity of Irregular Sampling Schemes," CEIS Research Paper 547, Tor Vergata University, CEIS, revised 19 Dec 2022.
    20. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    21. Venter, J.H. & de Jongh, P.J., 2014. "Extended stochastic volatility models incorporating realised measures," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 687-707.
    22. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    23. John M Maheu & Thomas H McCurdy, 2007. "Modeling foreign exchange rates with jumps," Working Papers tecipa-279, University of Toronto, Department of Economics.
    24. Peter C. B. Phillips & Jun Yu, 2009. "Information Loss in Volatility Measurement with Flat Price Trading," Global COE Hi-Stat Discussion Paper Series gd08-039, Institute of Economic Research, Hitotsubashi University.
    25. 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.
    26. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
    27. Hurvich, Cliiford & Wang, Yi, 2006. "A Pure-Jump Transaction-Level Price Model Yielding Cointegration, Leverage, and Nonsynchronous Trading Effects," MPRA Paper 1413, University Library of Munich, Germany.
    28. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Dec 2023.
    29. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
    30. Field, Jonathan & Large, Jeremy, 2008. "Pro-rata matching and one-tick futures markets," CFS Working Paper Series 2008/40, Center for Financial Studies (CFS).
    31. 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.
    32. Filip Zikes & Jozef Barunik & Nikhil Shenai, 2012. "Modeling and Forecasting Persistent Financial Durations," Papers 1208.3087, arXiv.org, revised Apr 2013.
    33. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
    34. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
    35. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    36. Robin de Vilder & Marcel P. Visser, 2007. "Proxies for daily volatility," PSE Working Papers halshs-00588307, HAL.
    37. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    38. 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.
    39. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    40. Aït-Sahalia, Yacine & Mancini, Loriano, 2008. "Out of sample forecasts of quadratic variation," Journal of Econometrics, Elsevier, vol. 147(1), pages 17-33, November.
    41. 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.
    42. Matthieu Garcin, 2021. "Forecasting with fractional Brownian motion: a financial perspective," Working Papers hal-03230167, HAL.
    43. 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.
    44. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    45. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Janeway Institute Working Papers 2423, Faculty of Economics, University of Cambridge.
    46. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    47. 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.
    48. 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.
    49. Mardi Dungey & Michael McKenzie & Vanessa Smith, 2007. "Empirical Evidence On Jumps In The Term Structure Of The Us Treasury Market," CAMA Working Papers 2007-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    50. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    51. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    52. Ahadzie, Richard Mawulawoe & Jeyasreedharan, Nagaratnam, 2020. "Trading volume and realized higher-order moments in the Australian stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    53. Charles S. Bos & Paweł Janus & Siem Jan Koopman, 2012. "Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 354-389, 2012 06.
    54. Chang, Patrick & Pienaar, Etienne & Gebbie, Tim, 2021. "The Epps effect under alternative sampling schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    55. Hua, Jia-Chen & Chen, Lijian & Falcon, Liberty & McCauley, Joseph L. & Gunaratne, Gemunu H., 2015. "Variable diffusion in stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 221-233.
    56. Kim Christensen & Roel Oomen & Roberto Renò, 2016. "The Drift Burst Hypothesis," CREATES Research Papers 2016-28, Department of Economics and Business Economics, Aarhus University.
    57. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
    58. Richard Mawulawoe Ahadzie & Nagaratnam Jeyasreedharan, 2024. "Higher‐order moments and asset pricing in the Australian stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 75-128, March.
    59. 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.
    60. Ata Türkoğlu, 2016. "Normally distributed high-frequency returns: a subordination approach," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 389-409, March.
    61. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    62. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    63. Torben G. Andersen & Tim Bollerslev & Per Houmann Frederiksen & Morten Ørregaard Nielsen, 2007. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," CREATES Research Papers 2007-21, Department of Economics and Business Economics, Aarhus University.
    64. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "The Epps effect under alternative sampling schemes," Papers 2011.11281, arXiv.org, revised Aug 2021.
    65. Rechenthin, Michael & Street, W. Nick, 2013. "Using conditional probability to identify trends in intra-day high-frequency equity pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6169-6188.
    66. Matthieu Garcin, 2021. "Forecasting with fractional Brownian motion: a financial perspective," Papers 2105.09140, arXiv.org, revised Sep 2021.
    67. Chun Liu & John M Maheu, 2010. "Intraday Dynamics of Volatility and Duration: Evidence from the Chinese Stock Market," Working Papers tecipa-401, University of Toronto, Department of Economics.
    68. Hsieh Fushing & Shu-Chun Chen & Chii-Ruey Hwang, 2012. "Discovering stock dynamics through multidimensional volatility phases," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 213-230, September.
    69. Vít Bubák & Filip Žikeš, 2009. "Distribution and Dynamics of Central-European Exchange Rates: Evidence from Intraday Data," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(4), pages 334-359, Oktober.
    70. de Vilder, Robin G. & Visser, Marcel P., 2007. "Volatility Proxies for Discrete Time Models," MPRA Paper 4917, University Library of Munich, Germany.
    71. 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.
    72. Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Effect of Long Memory in Volatility on Stock Market Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 684-700, November.
    73. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    74. Yingjie Dong & Yiu-Kuen Tse, 2017. "Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility," Econometrics, MDPI, vol. 5(4), pages 1-19, November.
    75. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Cambridge Working Papers in Economics 2449, Faculty of Economics, University of Cambridge.
    76. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    77. Arnerić Josip, 2020. "Realized density estimation using intraday prices," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 1-9, May.
    78. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.

  6. Roel C. A. Oomen, 2005. "Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 555-577.

    Cited by:

    1. Chaboud, Alain P. & Chiquoine, Benjamin & Hjalmarsson, Erik & Loretan, Mico, 2010. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 212-240, March.
    2. Álvaro Cartea & Dimitrios Karyampas, 2009. "Volatility and Covariation of Financial Assets: A High-Frequency Analysis," Birkbeck Working Papers in Economics and Finance 0913, Birkbeck, Department of Economics, Mathematics & Statistics.
    3. 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.
    4. Bertrand Maillet & Jean-Philippe Médecin & Thierry Michel, 2009. "High Watermarks of Market Risks," Post-Print halshs-00425585, HAL.
    5. Wu, Liuren, 2011. "Variance dynamics: Joint evidence from options and high-frequency returns," Journal of Econometrics, Elsevier, vol. 160(1), pages 280-287, January.
    6. Vuorenmaa, Tommi A., 2008. "Decimalization, Realized Volatility, and Market Microstructure Noise," MPRA Paper 8692, University Library of Munich, Germany.
    7. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, June.
    8. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
    9. 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.
    10. 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.
    11. 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.
    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. 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.
    14. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    15. 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.
    16. Marcelo C. Carvalho & Marco Aurélio S. Freire & Marcelo Cunha Medeiros & Leonardo R. Souza, 2006. "Modeling and Forecasting the Volatility of Brazilian Asset Returns: a Realized Variance Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 4(1), pages 55-77.
    17. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
    18. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).
    19. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    20. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
    21. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
    22. 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.
    23. Todorova, Neda & Worthington, Andrew & Souček, Michael, 2014. "Realized volatility spillovers in the non-ferrous metal futures market," Resources Policy, Elsevier, vol. 39(C), pages 21-31.
    24. Vortelinos, Dimitrios I., 2015. "The Greek equity market in European equity portfolios," Economic Modelling, Elsevier, vol. 49(C), pages 144-153.
    25. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    26. Wuyts, Gunther, 2008. "The impact of liquidity shocks through the limit order book," CFS Working Paper Series 2008/53, Center for Financial Studies (CFS).
    27. Josip Arneriæ & Mario Matkoviæ, 2019. "Challenges of integrated variance estimation in emerging stock markets," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(2), pages 713-739.
    28. Bannouh, K. & van Dijk, D.J.C. & Martens, M.P.E., 2008. "Range-based covariance estimation using high-frequency data: The realized co-range," Econometric Institute Research Papers EI 2007-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    29. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    30. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    31. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    32. Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Effect of Long Memory in Volatility on Stock Market Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 684-700, November.
    33. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    34. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
    35. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    36. 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.
    37. 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.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.