Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Description

Multivariate factor stochastic volatility models are increasingly used for the analysis of multivariate financial and economic time series because they can capture the volatility dynamics by a small number of latent factors. The main advantage of such a model is its parsimony, where all variances and covariances of a time series vector are governed by a low-dimensional common factor with the components following independent stochastic volatility models. For high dimensional problems of this kind, Bayesian MCMC estimation is a very efficient estimation method, however, it is associated with a considerable computational burden when the number of assets is moderate to large. To overcome this, we avoid the usual forward-filtering backward-sampling (FFBS) algorithm by sampling ``all without a loop'' (AWOL), consider various reparameterizations such as (partial) non-centering, and apply an ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation at an univariate level, which can be applied directly to heteroscedasticity estimation for latent variables such as factors. To show the effectiveness of our approach, we apply the model to a vector of daily exchange rate data.

Period	2 May 2013 → 4 May 2013
Event title	1st Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance
Event type	Unknown
Degree of Recognition	National