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

Manabu Asai, Massimiliano Caporin and Michael McAleer

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: 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.

Keywords: block structures; multivariate stochastic volatility; curse of dimensionality; leverage effects; multi-factors; heavy-tailed distribution (search for similar items in EconPapers)
JEL-codes: C10 C32 C51 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2012-03-01
New Economics Papers: this item is included in nep-ban, nep-ecm and nep-for
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https://repec.canterbury.ac.nz/cbt/econwp/1204.pdf (application/pdf)

Related works:
Journal Article: Forecasting Value-at-Risk using block structure multivariate stochastic volatility models (2015)
Working Paper: Forecasting Value-at-Risk using Block Structure Multivariate Stochastic Volatility Models (2013)
Working Paper: Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models (2012)
Working Paper: Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models (2012)
Working Paper: Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models (2012)
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