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This paper proposes a flexible stochastic volatility model with jumps and leverage effect by introducing a generalized hyperbolic skew Student’s t-distribution (GHST), which can capture the skewness, heavy-tailedness and leptokurtosis of asset returns. Monte Carlo Markov Chain algorithm combining Gibbs sampler and Metropolis-Hastings algorithm is used for parameter estimation and the method is illustrated with simulated data and CSI300 and S&amp;P500 indices. Meantime, we investigate the sensitivity of Bayesian estimates to prior distributions of parameters in jump component and GHST distribution.

Bayesian estimation for stochastic volatility model with jumps, leverage effect and generalized hyperbolic skew Student's t-distribution