Rate of convergence for parametric estimation in a stochastic volatility model

Marc Hoffmann ()

Stochastic Processes and their Applications, 2002, vol. 97, issue 1, 147-170

Abstract: We consider the following hidden Markov chain problem: estimate the finite-dimensional parameter [theta] in the equation when we observe discrete data Xi/n at times i=0,...,n from the diffusion . The processes (Wt)t[set membership, variant][0,1] and (Bt)t[set membership, variant][0,1] are two independent Brownian motions; asymptotics are taken as n-->[infinity]. This stochastic volatility model has been paid some attention lately, especially in financial mathematics. We prove in this note that the unusual rate n-1/4 is a lower bound for estimating [theta]. This rate is indeed optimal, since Gloter (CR Acad. Sci. Paris, t330, Série I, pp. 243-248), exhibited n-1/4 consistent estimators. This result shows in particular the significant difference between "high frequency data" and the ergodic framework in stochastic volatility models (compare Genon-Catalot, Jeantheau and Laredo (Bernoulli 4 (1998) 283; Bernoulli 5 (2000) 855; Bernoulli 6 (2000) 1051 and also Sørensen (Prediction-based estimating functions. Technical report, Department of Theoretical Statistics, University of Copenhagen, 1998)).

Keywords: Stochastic; volatility; models; Discrete; sampling; High; frequency; data; Non-parametric; Bayesian; estimation (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (7)

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