Triantafyllopoulos, K. (2008) Multivariate stochastic volatility with Bayesian dynamic linear models. Journal of Statistical Planning and Inference, 138 (4). pp. 1021-1037. ISSN 0378-3758Full text not available from this repository. (Request a copy)
This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount the variances element by element and therefore allowing a flexible and pragmatic variance modelling approach. Diagnostic tests and sequential model monitoring are discussed in some detail. The proposed estimation theory is applied to a four-dimensional time series, comprising spot prices of aluminium, copper, lead and zinc of the London metal exchange. The empirical findings suggest that the proposed Bayesian procedure can be effectively applied to financial data, overcoming many of the disadvantages of existing volatility models.
|Keywords:||Time series; Volatility; Multivariate; Dynamic linear model; Bayesian; Forecasting; State space; Kalman filter; GARCH; London metal exchange|
|Academic Units:||The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
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|Depositing User:||Mrs Megan Hobbs|
|Date Deposited:||23 Mar 2010 16:09|
|Last Modified:||07 Jun 2010 11:09|
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