Multivariate stochastic volatility with Bayesian dynamic linear models

Details Multivariate stochastic volatility with Bayesian dynamic linear models Multivariate stochastic volatility with Bayesian dynamic linear models

2008-02-01

arxiv.org/abs/0802.0214v1

Journal of Statistical Planning and Inference (2008), 138(4), pp. 1021-1037

Economy

Quantitative Finance

Statistical Finance

24 pages, 3 figures, 2 tables

Scientific paper

10.1016/j.jspi.2007.03.057

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.

Affiliated with

Also associated with

No associations

Rating

Multivariate stochastic volatility with Bayesian dynamic linear models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Multivariate stochastic volatility with Bayesian dynamic linear models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multivariate stochastic volatility with Bayesian dynamic linear models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-189385