Implicit Bayesian Inference Using Option Prices

Gael Margaret Martin, Catherine S. Forbes () and Vance Lindsay Martin

No 5/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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 fit, 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.

Keywords: Bayesian Option Pricing; Leptokurtosis; Skewness; GARCH Option Pricing; Option Price Prediction; Hedging Errors. (search for similar items in EconPapers)
JEL-codes: C11 C16 G13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-fin, nep-fmk and nep-rmg
Date: 2003-02
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Related works:
Working Paper: Implicit Bayesian Inference Using Option Prices (2000)
Journal Article: Implicit Bayesian Inference Using Option Prices (2005)
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