University of Cambridge > Talks.cam > Cambridge Finance Workshop Series > Modelling and Predicting the Time-varying Volatility Risk Premium: a Bayesian Non-Gaussian State Space Approach

Modelling and Predicting the Time-varying Volatility Risk Premium: a Bayesian Non-Gaussian State Space Approach

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Eva Gottschalk.

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.

This talk is part of the Cambridge Finance Workshop Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2021 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity