A Fractionally Integrated Wishart Stochastic Volatility Model

Manabu Asai and Michael McAleer

No 848, KIER Working Papers from Kyoto University, Institute of Economic Research

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.

Keywords: Di usion process; Multivariate stochastic volatility; Long memory; Fractional Brownian motion; Generalized Method of Moments. (search for similar items in EconPapers)
JEL-codes: C32 C51 G13 (search for similar items in EconPapers)
Pages: 29pages
Date: 2013-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: A fractionally integrated Wishart stochastic volatility model (2017)
Working Paper: A Fractionally Integrated Wishart Stochastic Volatility Model (2013)
Working Paper: A Fractionally Integrated Wishart Stochastic Volatility Model (2013)
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