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Abstract:
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In this talk, we consider a class of multivariate non-Gaussian time series models which can be used for analysis of financial time-series. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods which allow us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. To illustrate our methodology, we use simulated data examples and a real application of multivariate time series for modeling the joint dynamics of stochastic volatility in financial indexes, the VIX and VXN.
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