Bayesian analysis of Multivariate Stochastic Volatility models
Date
2017
Authors
Agasa, Lameck
Shitandi, Anakalo
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Multivariate stochastic volatility (MSV) models have gained applicability in Time Series (TS) data for analyzing multivariate financial and economic time series because they capture the volatility dynamics. Bayesian prior works allow analysis of MSV models to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Bayesian MCMC estimation are used for high dimensional problems because it’s a very efficient estimation method, however, it is associated with a considerable computational burden when the dimensionality of the data is moderate to large. Forward-filtering backward-sampling (FFBS) algorithm by sampling is used as it considers reparameterizations. This is applied directly to heteroscedasticity estimation for latent variables. To show the effectiveness of this approach, we apply the model to a vector of daily exchange rate data from Central Bank of Kenya.
Description
Paper presented at the 4th Strathmore International Mathematics Conference (SIMC 2017), 19 - 23 June 2017, Strathmore University, Nairobi, Kenya.
Keywords
Bayesian analysis, Multivariate Stochastic Volatility models