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A Fractionally Integrated Wishart Stochastic Volatility Model

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  • Manabu Asai

    (Faculty of Economics Soka University, Japan and Wharton School University of Pennsylvania)

  • Michael McAleer

    (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Institute of Economic Research Kyoto University, Japan and Department of Quantitative Economics Complutense University of Madrid, Spain)

Abstract

There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of the FIWSV model in order to obtain a closed form expression of moments. We conduct a two-step procedure, namely estimating the parameter of fractional integration via log-periodgram regression in the rst step, and estimating the remaining parameters via the generalized method of moments in the second step. Monte Carlo results for the procedure shows reasonable performances in nite samples. The empirical results for the bivariate data of the S&P 500 and FTSE 100 indexes show that the data favor the new FIWSV processes rather than one-factor and two-factor models of Wishart autoregressive processes for the covariance structure.

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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 848.

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Length: 29pages
Date of creation: Feb 2013
Date of revision:
Handle: RePEc:kyo:wpaper:848

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Keywords: Di usion process; Multivariate stochastic volatility; Long memory; Fractional Brownian motion; Generalized Method of Moments.;

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