Multiscale stochastic volatility model with heavy tails and leverage effects - Zhongxian Men; Tony S. Wirjanto; Adam W. Kolkiewicz (Journal of Risk and Financial Management, 2021)
This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms.
Keywords: 330; Bayesian inference; MCMC; slice sampler