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SUMMARY:Modelling and Predicting the Time-varying Volatility Risk Premium:
  a Bayesian Non-Gaussian State Space Approach - Gael Martin\, Monash Unive
 rsity\, Clayton\, Australia
DTSTART:20080603T160000Z
DTEND:20080603T170000Z
UID:TALK11985@talks.cam.ac.uk
CONTACT:Eva Gottschalk
DESCRIPTION:Modelling and Predicting the Time-varying Volatility Risk Prem
 ium: a Bayesian Non-Gaussian State Space Approach (Gael M. Martin\, Cather
 ine S. Forbes and Nan Qu\, Department of Econometrics and Business Statist
 ics\, Monash University)\n\nThe object of this paper is to model and forec
 ast both stochastic volatility and its associated time-varying risk premiu
 m using a non-Gaussian state space approach. Option and spot market inform
 ation on the unobserved volatility process is captured via non-parametric\
 , ‘model-free' measures of option-implied and spot-price-based volatilit
 y\, with the two non-parametric measures used to define a bivariate observ
 ation 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 sa
 mpling of the latent volatility and its risk premium. In addition to estim
 ating the static and random parameters\, we aim to use sequential Monte Ca
 rlo methods to produce real-time forecasts of both volatility and its risk
  premium.\n
LOCATION:Winstanley Lecture Hall\, Trinity College
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