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Bayesian Semi-nonparametric ARCH Models

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  • Koop, Gary

Abstract

A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors. An application and a small Monte Carlo study indicate that the methods the author advocates are both feasible and necessary. Copyright 1994 by MIT Press.

Suggested Citation

  • Koop, Gary, 1994. "Bayesian Semi-nonparametric ARCH Models," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 176-181, February.
    • Handle: RePEc:tpr:restat:v:76:y:1994:i:1:p:176-81
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    Cited by:

    1. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    3. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.

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