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Seminar: Catherine Scipione Forbes

Statistics Seminar
April 2, 2009
All Day
209 W. Eighteenth Ave. (EA), Room 170

Title

Modeling and predicting volatility and its risk premium: a Bayesian non-Gaussian state space approach

Speaker

Catherine Scipione Forbes, Monash University, Australia

Abstract

The object of this work is to model and forecast both objective volatility and its associated 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 measures used to define a bivariate observation equation in a state space model. The risk premium parameter is specified as a conditionally deterministic dynamic process, driven by past 'observations' on the volatility risk premium. A Bayesian Markov chain Monte Carlo (MCMC) method is devised, with draws from the posterior distribution used to obtain the desired predictive distributions. 

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.