CIRJE-F-907 "Dynamic Equicorrelation Stochastic Volatility"
Author Name

Kurose, Yuta and Yasuhiro Omori

Date November 2013
Full Paper   PDF file
Remarks  Revised as CIRJE-F-941 (2014).
Abstract
  

A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage effect is proposed and estimated. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling efficiency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The empirical study shows that our novel model provides a substantial improvement in forecasting with respect to out-of-sample hedging performances.