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Multivariate stochastic volatility with Bayesian dynamic linear models

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  • K. Triantafyllopoulos

Abstract

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.

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File URL: http://arxiv.org/pdf/0802.0214
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 0802.0214.

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Date of creation: Feb 2008
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Publication status: Published in Journal of Statistical Planning and Inference (2008), 138(4), pp. 1021-1037
Handle: RePEc:arx:papers:0802.0214

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Cited by:
  1. K. Triantafyllopoulos, 2012. "Multi‐variate stochastic volatility modelling using Wishart autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, Wiley Blackwell, vol. 33(1), pages 48-60, 01.

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