Department of Economics and Business Economics

TY - JOUR

T1 - Bayesian Option Pricing using Mixed Normal Heteroskedasticity Models

AU - Rombouts, Jeroen

AU - Stentoft, Lars

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - Option pricing using mixed normal heteroscedasticity models is considered. It is explained how to perform inference and price options in a Bayesian framework. The approach allows to easily compute risk neutral predictive price densities which take into account parameter uncertainty. In an application to the S&P 500 index, classical and Bayesian inference is performed on the mixture model using the available return data. Comparing the ML estimates and posterior moments small differences are found. When pricing a rich sample of options on the index, both methods yield similar pricing errors measured in dollar and implied standard deviation losses, and it turns out that the impact of parameter uncertainty is minor. Therefore, when it comes to option pricing where large amounts of data are available, the choice of the inference method is unimportant. The results are robust to different specifications of the variance dynamics but show however that there might be scope for using Bayesian methods when considerably less data is available for inference.

AB - Option pricing using mixed normal heteroscedasticity models is considered. It is explained how to perform inference and price options in a Bayesian framework. The approach allows to easily compute risk neutral predictive price densities which take into account parameter uncertainty. In an application to the S&P 500 index, classical and Bayesian inference is performed on the mixture model using the available return data. Comparing the ML estimates and posterior moments small differences are found. When pricing a rich sample of options on the index, both methods yield similar pricing errors measured in dollar and implied standard deviation losses, and it turns out that the impact of parameter uncertainty is minor. Therefore, when it comes to option pricing where large amounts of data are available, the choice of the inference method is unimportant. The results are robust to different specifications of the variance dynamics but show however that there might be scope for using Bayesian methods when considerably less data is available for inference.

KW - Bayesian inference

KW - Option pricing

KW - Finite mixture models

U2 - 10.1016/j.csda.2013.06.023

DO - 10.1016/j.csda.2013.06.023

M3 - Journal article

SP - 588

EP - 605

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

ER -