Multivariate Stochastic Volatility with Dynamic Cross Leverage

Sebastian Trojan (sebastian.trojan@student.unisg.ch)

No 1424, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

Abstract: WA multivariate stochastic volatility (MSV) model based on a Cholesky-type decomposition of the covariance matrix to model dynamic correlation in the observation and transition error as well as in cross leverage terms is proposed. The empirically relevant asymmetric concept of cross leverage is defined as a nonzero correlation between the ith asset return at time t and the jth log-volatility at time t+1. Volatilities and covariances are modeled separately, which makes an interpretation of leverage parameters straightforward. The model is applied on a three-dimensional portfolio consisting of the S&P 500 sector indices Financials, Industrials and Healthcare, spanning the recent financial crisis 2008/09. During and in the aftermath of market turmoil, increased cross leverage effects, higher unconditional kurtosis and stronger correlated information flow are observed. However, there is risk of overfitting and restricting time variation to elements governing dynamics of the observation error may be advisable.

Keywords: Multivariate stochastic volatility; dynamic correlation; cross leverage; Cholesky decomposition; nonlinear state space model; Markov chain Monte Carlo; block sampler; particle filter (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C58 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2014-08
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 (5)

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