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Nonparametric Estimation Methods of Integrated Multivariate Volatilities

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Author Info

  • Toshiya Hoshikawa
  • Keiji Nagai
  • Taro Kanatani
  • Yoshihiko Nishiyama

Abstract

Estimation of integrated multivariate volatilities of an Ito process is an interesting and important issue in finance, for example, in order to evaluate portfolios. New non-parametric estimators have been recently proposed by Malliavin and Mancino (2002) and Hayashi and Yoshida (2005a) as alternative methods to classical realized quadratic covariation. The purpose of this article is to compare these alternative estimators both theoretically and empirically, when high frequency data is available. We found that the Hayashi-Yoshida estimator performs the best among the alternatives in view of the bias and the MSE. The other estimators are shown to have possibly heavy bias mostly toward the origin. We also applied these estimators to Japanese Government Bond futures to obtain the results consistent with our simulation.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 27 (2008)
Issue (Month): 1-3 ()
Pages: 112-138

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Handle: RePEc:taf:emetrv:v:27:y:2008:i:1-3:p:112-138

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Related research

Keywords: High frequency data; Integrated volatility; Nonparametric estimators; Weighted realized volatility;

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Cited by:
  1. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
  2. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
  3. Masato Ubukata & Kosuke Oya, 2007. "Test of Unbiasedness of the Integrated Covariance Estimation in the Presence of Noise," Discussion Papers in Economics and Business 07-03, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).

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