Bayesian analysis of dynamic correlation -multivariate stochastic volatility (DC-MSV) model

Abstract

The forex exchange rate is the most volatile aspect in financial studies. This study uses a bayesian approach to estimate dynamic correlation multivariate stochastic volatility, a case of the Kenya stocks. Data was obtained from the Central Bank of Kenya website depicting the daily exchange rates for a period of 12 years (2003-2015). Multivariate Stochastic Volatility (MSV) model was fitted and its residuals exhibited volatility clustering hence the use Dynamic Correlation Multivariate Stochastic Volatility (DC-MSV) was applied to address these characteristics. Using Akaike Information Criterion (AIC) and Deviance Information Criterion (DIC) found that the returns are leptokurtic and have fat tails. The study estimated posterior parameters of the model by using of Markovian chain Monte Carlo (MCMC) iterations that worked well. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients. The dynamic correlation multivariate stochastic volatility was found ideal in estimating volatility in stock exchanges.