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Bayesian analysis of constant elasticity of variance models

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  • Jennifer S. K. Chan
  • S. T. Boris Choy
  • Anna B. W. Lee

Abstract

This paper focuses on the estimation of some models in finance and in particular, in interest rates. We analyse discretized versions of the constant elasticity of variance (CEV) models where the normal law showing up in the usual discretization of the diffusion part is replaced by a range of heavy‐tailed distributions. A further extension of the model is to allow the elasticity of variance to be a parameter itself. This generalized model allows great flexibility in modelling and simplifies the model implementation considerably using the scale mixtures representation. The mixing parameters provide a means to identify possible outliers and protect inference by down‐weighting the distorting effects of these outliers. For parameter estimation, Bayesian approach is adopted and implemented using the software WinBUGS (Bayesian inference using Gibbs sampler). Results from a real data analysis show that an exponential power distribution with a random shape parameter, which is highly leptokurtic compared with the normal distribution, forms the best CEV model for the data. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Jennifer S. K. Chan & S. T. Boris Choy & Anna B. W. Lee, 2007. "Bayesian analysis of constant elasticity of variance models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(1), pages 83-96, January.
    • Handle: RePEc:wly:apsmbi:v:23:y:2007:i:1:p:83-96
    DOI: 10.1002/asmb.639
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