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AND BUSINESS STATISTICS Implicit Bayesian Inference Using Option Prices

By Gael M Martin, Catherine S Forbes, Vance L Martin, Gael M. Martina, Catherine S. Forbesa and Vance L. Martin B

Abstract

A Bayesian approach to option pricing is presented, in which posterior inference about the underlying returns process is conducted implicitly via observed option prices. A range of models allowing for conditional leptokurtosis, skewness and time-varying volatility in returns are considered, with posterior parameter distributions and model probabilities backed out from the option prices. Models are ranked according to several criteria, including out-of-sample Þt, predictive and hedging performance. The methodology accommodates heteroscedasticity and autocorrelation in the option pricing errors, as well as regime shifts across contract groups. The method is applied to intraday option price data on the S&P500 stock index for 1995. Whilst the results provide support for models which accommodate leptokurtosis, no one model dominates according to all criteria considered

Topics: Bayesian Option Pricing, Leptokurtosis, Skewness, GARCH Option Pricing, Option Price Prediction, Hedging Errors. JEL Classifications, C11, C16, G13
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.195.4582
Provided by: CiteSeerX
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