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MCMC Analysis of Diffusion Models with Application to Finance

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Author Info
Eraker, Bjorn
Abstract

This article proposes a new method for estimation of parameters in diffusion processes from discrete observations. The method is based on Markov-chain Monte Carlo methodology and applies to a wide class of models including systems with unobservable state variables and nonlinearities. The method is applied to the estimation of parameters in one-factor (CEV) interest-rate models and a two-factor model with a latent stochastic volatility component (SV). The CEV model is found to do a poor job in capturing the time-varying volatility interest-rate data. The SV model provides vastly superior fit to that of the CEV model. The article also presents a simulation study that demonstrates that the method provides accurate parameter estimates of the SV model at moderate sample sizes.

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Publisher Info
Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 19 (2001)
Issue (Month): 2 (April)
Pages: 177-91
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Handle: RePEc:bes:jnlbes:v:19:y:2001:i:2:p:177-91

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