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Bayesian option pricing using asymmetric GARCH

Author

Listed:
  • BAUWENS, LUC

    (Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium)

  • LUBRANO, Michel

    (GREQAM, CNRS)

Abstract

This paper shows how one can compute option prices from a Bayesian inference viewpoint, using an econometric model for the dynamics of the return and of the volatility of the underlying asset. The proposed evaluation of an option is the predictive expectation of its payoff function. The predictive distribution of this function provides a natural metric with respect to which the predictive option price, or other option evaluations, can be gauged. The proposed method is compared to the Black and Scholes evaluation, in which a predictive mean volatility is plugged, but which does not provide a natural metric. The methods are illustrated using an asymmetric GARCH model with a data set on a stock index in Brussels. The persistence of the volatility process is linked to the prediction horizon and to the option maturity.

Suggested Citation

  • BAUWENS, LUC & LUBRANO, Michel, 1997. "Bayesian option pricing using asymmetric GARCH," LIDAM Discussion Papers CORE 1997059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1997059
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    Cited by:

    1. Lanne, Markku & Luoto, Jani, 2008. "Robustness of the risk-return relationship in the U.S. stock market," Finance Research Letters, Elsevier, vol. 5(2), pages 118-127, June.
    2. HAFNER, Christian & HERWARTZ, Helmut, 1998. "Volatility impulse response functions for multivariate GARCH models," LIDAM Discussion Papers CORE 1998047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Jacek Osiewalski & Mateusz Pipień, 2004. "Bayesian Pricing of an European Call Option Using a GARCH Model with Asymmetries," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 177/2004 - Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 14, pages 219-238, University of Lodz.
    4. Mateusz Pipień, 2005. "Dynamic Bayesian Inference in GARCH Processes with Skewed-t and Stable Conditional Distributions," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 15, pages 251-269, University of Lodz.
    5. Abel Rodríguez & Enrique ter Horst & Samuel Malone, 2015. "Bayesian Inference for a Structural Credit Risk Model with Stochastic Volatility and Stochastic Interest Rates," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 839-867.

    More about this item

    Keywords

    Bayesian; GARCH; option pricing; simulation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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