Estimating high dimensional multivariate stochastic volatility models

Matteo Pelagatti and Giacomo Sbrana

No 428, Working Papers from University of Milano-Bicocca, Department of Economics

Abstract: This paper proposes tree main results that enable the estimation of high dimensional multivariate stochastic volatility models. The first result is the closed-form steady-state Kalman filter for the multivariate AR(1) plus noise model. The second result is an accelerated EM algorithm for parameters estimation. The third result is an estimator of the correlation of two elliptical random variables with time-varying variances that is consistent and asymptotically normal regardless of the variances evolution. Speed and precision of our methodology are evaluated in a simulation experiment. Finally, we implement our method and compare its performance with other approaches in a minimum variance portfolio composed by the constituents of the CAC40 and S&P100 indexes.

Keywords: Riccati equation; EM algorithm; Kalman filter; Correlation estimation; Large covariance matrix; Multivariate stochastic volatility (search for similar items in EconPapers)
Pages: 24
Date: 2020-01, Revised 2020-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:mib:wpaper:428

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