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Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise

Citations

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Cited by:

  1. Neil Shephard & Ole E. Barndorff-Nielsen & Department of Mathematical Sciences & University of Aarhus & Denmark, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Series Working Papers 240, University of Oxford, Department of Economics.
  2. Neil Shephard & Ole E. Barndorff-Nielsen & Department of Mathematical Sciences & University of Aarhus, 2004. "Multipower Variation and Stochastic Volatility," Economics Series Working Papers 2004-FE-22, University of Oxford, Department of Economics.
  3. Giuseppe Curci & Fulvio Corsi, 2012. "Discrete sine transform for multi-scale realized volatility measures§," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 263-279, April.
  4. Barndorff-Nielsen, Ole E. & Graversen, Svend Erik & Jacod, Jean & Shephard, Neil, 2006. "Limit Theorems For Bipower Variation In Financial Econometrics," Econometric Theory, Cambridge University Press, vol. 22(4), pages 677-719, August.
  5. Masato Ubukata & Toshiaki Watanabe, 2011. "Pricing Nikkei 225 Options Using Realized Volatility," IMES Discussion Paper Series 11-E-18, Institute for Monetary and Economic Studies, Bank of Japan.
  6. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
  7. Zhang, Lan & Mykland, Per A. & Aït-Sahalia, Yacine, 2011. "Edgeworth expansions for realized volatility and related estimators," Journal of Econometrics, Elsevier, vol. 160(1), pages 190-203, January.
  8. Aït-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2011. "Ultra high frequency volatility estimation with dependent microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 160-175, January.
  9. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
  10. Maheu, John M. & McCurdy, Thomas H., 2011. "Do high-frequency measures of volatility improve forecasts of return distributions?," Journal of Econometrics, Elsevier, vol. 160(1), pages 69-76, January.
  11. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
  12. 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.
  13. 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.
  14. Jeremy Large, 2005. "Estimating quadratic variation when quoted prices jump by a constant increment," OFRC Working Papers Series 2005fe05, Oxford Financial Research Centre.
  15. Borus Jungbacker & Siem Jan Koopman, 2006. "Model-Based Measurement of Actual Volatility in High-Frequency Data," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 183-210, Emerald Group Publishing Limited.
  16. Masato Ubukata & Toshiaki Watanabe, 2013. "Pricing Nikkei 225 Options Using Realized Volatility," Global COE Hi-Stat Discussion Paper Series gd12-273, Institute of Economic Research, Hitotsubashi University.
  17. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
  18. Barndorff-Nielsen, Ole E. & Shephard, Neil & Winkel, Matthias, 2006. "Limit theorems for multipower variation in the presence of jumps," Stochastic Processes and their Applications, Elsevier, vol. 116(5), pages 796-806, May.
  19. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
  20. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
  21. Torben G. Andersen & Luca Benzoni, 2010. "Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models," Journal of Finance, American Finance Association, vol. 65(2), pages 603-653, April.
  22. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
  23. Benlagha, Noureddine & Chargui, Sana, 2017. "Range-based and GARCH volatility estimation: Evidence from the French asset market," Global Finance Journal, Elsevier, vol. 32(C), pages 149-165.
  24. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  25. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  26. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
  27. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
  28. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
  29. Lidan He & Qiang Liu & Zhi Liu & Andrea Bucci, 2024. "Correcting spot power variation estimator via Edgeworth expansion," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(8), pages 921-945, November.
  30. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
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