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December 2000 Stochastic volatility models as hidden Markov models and statistical applications
Valentine Genon-Catalot, Thierry Jeantheau, Catherine Larédo
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Bernoulli 6(6): 1051-1079 (December 2000).


This paper deals with the fixed sampling interval case for stochastic volatility models. We consider a two-dimensional diffusion process (Yt, Vt), where only (Yt) is observed at n discrete times with regular sampling interval ͉. The unobserved coordinate (Vt) is ergodic and rules the diffusion coefficient (volatility) of (Yt). We study the ergodicity and mixing properties of the observations (Y). For this purpose, we first present a thorough review of these properties for stationary diffusions. We then prove that our observations can be viewed as a hidden Markov model and inherit the mixing properties of (Vt). When the stochastic differential equation of (Vt) depends on unknown parameters, we derive moment-type estimators of all the parameters, and show almost sure convergence and a central limit theorem at rate n1/2. Examples of models coming from finance are fully treated. We focus on the asymptotic variances of the estimators and establish some links with the small sampling interval case studied in previous papers.


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Valentine Genon-Catalot. Thierry Jeantheau. Catherine Larédo. "Stochastic volatility models as hidden Markov models and statistical applications." Bernoulli 6 (6) 1051 - 1079, December 2000.


Published: December 2000
First available in Project Euclid: 5 April 2004

zbMATH: 0966.62048
MathSciNet: MR1809735

Keywords: Diffusion processes , Discrete-time observations , Hidden Markov models , Mixing , Parametric inference , stochastic volatility

Rights: Copyright © 2000 Bernoulli Society for Mathematical Statistics and Probability

Vol.6 • No. 6 • December 2000
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