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Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series
Modeling and predicting volatility and its risk premium: a
Bayesian non-Gaussian state space approach
Catherine Scipione Forbes
Department of Econometrics and Business
Statistics, Monash University, Australia
3:30PM - Thursday, April 2, 2009
Room 170, Eighteenth Avenue Bldg. (EA 170)
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.
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