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

Manabu Asai, Massimiliano Caporin and Michael McAleer

No 13-073/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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 is to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets can be very large. We contribute to this strand of the literature by proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on the 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 including 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)
Date: 2013-05-27
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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