FrançaisEnglish

Érudit | Dépôt de documents >
CIRPÉE - Centre interuniversitaire sur le risque, les politiques économiques et l'emploi >
Cahiers de recherche du CIRPÉE >

Please use this identifier to cite or link to this item:

https://depot.erudit.org/id/003090dd

Title: Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models
Authors: Rombouts, Jeroen V.K.
Stentoft, Lars
Keywords: Bayesian inference
option pricing
finite mixture models
out-of-sample prediction
GARCH models
Issue Date: 2009-08
Series/Report no.: Cahiers du CIRPÉE;09-26
Abstract: While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order moments of the risk neutral distribution. Parameter inference using Gibbs sampling is explained and we detail how to compute risk neutral predictive densities taking into account parameter uncertainty. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared to a benchmark model in terms of dollar losses and the ability to explain the smirk in implied volatilities.
URI: https://depot.erudit.org/id/003090dd
Appears in Collections:Cahiers de recherche du CIRPÉE

Files in This Item:

CIRPEE09-26.pdf, (Adobe PDF ; 413,39 kB)

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

 

About Érudit | Subscriptions | RSS | Terms of Use | Contact us |

Consortium Érudit ©  2014