Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models (2012)
Type of ContentDiscussion / Working Papers
PublisherCollege of Business and Economics
University of Canterbury. Department of Economics and Finance
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.
CitationAsai, M., Caporin, M., McAleer, M. (2012) Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models. Department of Economics and Finance. 37pp..
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsblock structures; multivariate stochastic volatility; curse of dimensionality; leverage effects; multi-factors; heavy-tailed distribution
ANZSRC Fields of Research35 - Commerce, management, tourism and services::3502 - Banking, finance and investment::350208 - Investment and risk management
38 - Economics::3802 - Econometrics::380202 - Econometric and statistical methods
Showing items related by title, author, creator and subject.
Chang, C-L.; Allen, D.E.; McAleer, M.; Amaral, T.P. (University of Canterbury. Department of Economics and Finance, 2013)The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modeling and Management” ...
Allen, D. E.; McAleer, M.; Scharth, M. (University of Canterbury. Department of Economics and Finance, 2014)In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent ...
Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance Asai, M.; McAleer, M. (University of Canterbury. Department of Economics and Finance, 2014)Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for ...