Implicit Bayesian Inference Using Option Prices
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.
|Date of creation:||Feb 2003|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC
|Order Information:|| Web: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/ Email: |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- repec:dgr:kubcen:199658 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2003-5. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Simone Grose)
If references are entirely missing, you can add them using this form.