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Realized volatility: a review

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Author Info
Michael McAleer (University of Western Australia)
Marcelo Cunha Medeiros () (Department of Economics PUC-Rio)

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Abstract

This paper reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. A continuous time specification provides the theoretical foundation for the main results in this literature. Cases with and without microstructure noise are considered, and it is shown how microstructure noise can cause severe problems in terms of consistent estimation of the daily realized volatility. Independent and dependent noise processes are examined. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The finite sample properties are discussed in comparison with their asymptotic properties. A multivariate model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting realized volatilities are considered. The main empirical findings using univariate and multivariate methods are summarized.

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Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 531 Publication status: Forthcoming in Econometric Reviews.

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Length: 56p
Date of creation: Nov 2006
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Handle: RePEc:rio:texdis:531

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  1. repec:mop:credwp:09.05.84 is not listed on IDEAS
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  2. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2009. "Forecasting realized (co)variances with a block structure Wishart autoregressive model," Working Papers 2009-3, Swiss National Bank. [Downloadable!]
  3. Qianqiu Liu, 2009. "On portfolio optimization: How and when do we benefit from high-frequency data?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 560-582. [Downloadable!]
  4. Julien Chevallier & Benoît Sévi, 2009. "On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting," Working Papers halshs-00387286_v1, HAL. [Downloadable!]
  5. Federico M. Bandi & Roberto Reno, 2009. "Nonparametric Stochastic Volatility," Global COE Hi-Stat Discussion Paper Series gd08-035, Institute of Economic Research, Hitotsubashi University. [Downloadable!]
  6. Makoto Takahashi & Yasuhiro Omori & Toshiaki Watanabe, 2007. "Estimating Stochastic Volatility Models Using Daily Returns and Realized Volatility Simultaneously," CIRJE F-Series CIRJE-F-515, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
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  7. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil). [Downloadable!]
  8. Gao, Jiti & McAleer, Michael & Allen, Dave, 2006. "Econometric modelling in finance and risk management: An overview," MPRA Paper 11978, University Library of Munich, Germany, revised Nov 2007. [Downloadable!]
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  9. 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. [Downloadable!]
  10. Fulvio Corsi & Davide Pirino & Roberto Reno, 2009. "Volatility Forecasting: The Jumps Do Matter," Global COE Hi-Stat Discussion Paper Series gd08-036, Institute of Economic Research, Hitotsubashi University. [Downloadable!]
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