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Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models

  • Michael McAleer

    (Erasmus University Rotterdam,Tinbergen Institute,Kyoto University,Complutense University of Madrid)

  • Manabu Asai

    (Faculty of Economics Soka University)

  • Massimiliano Caporin

    (Department of Economics and Management “Marco Fanno”University of Padova)

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose was to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets is quite large. We contribute to this strand of the literature proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on US market shows that 1% and 5 % Value-at-Risk thresholds based on one-step-ahead forecasts of covariances by the new specification are satisfactory for the period includes the global financial crisis.

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Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 812.

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Date of creation: Apr 2012
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Handle: RePEc:kyo:wpaper:812
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