Abstract

Modelling and Predicting the Time-varying Volatility Risk Premium: a Bayesian Non-Gaussian State Space Approach (Gael M. Martin, Catherine S. Forbes and Nan Qu, Department of Econometrics and Business Statistics, Monash University)

The object of this paper is to model and forecast both stochastic volatility and its associated time-varying risk premium using a non-Gaussian state space approach. Option and spot market information on the unobserved volatility process is captured via non-parametric, ‘model-free’ measures of option-implied and spot-price-based volatility, with the two non-parametric measures used to define a bivariate observation equation in the state space model. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo (MCMC) algorithm that caters for the non-linearities in the model and for the multi-move sampling of the latent volatility and its risk premium. In addition to estimating the static and random parameters, we aim to use sequential Monte Carlo methods to produce real-time forecasts of both volatility and its risk premium.