Bayesian estimation of the stochastic volatility model with double exponential jumps

This paper generalizes the stochastic volatility model to allow for the double exponential jumps. To derive the jumps and time-varying volatility in returns, we implement an efficient Markov chain Monte Carlo approach based on the band and sparse matrix algorithms used in Chan and Hsiao (SSRN Electron J., 2013, https://doi.org/10.2139/ssrn.2359838 ) to estimate this model. We illustrate the the methodology using the daily data for the Shanghai Composite Index, Hangseng Index, Nikkei 225 Index and Kospi Index. We find that the stochastic volatility model with double exponential jumps provide better fitness in sample period.