Estimation of the long memory parameter in stochastic volatility models by quadratic variations

Abstract

We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.

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ROSE is devoted to the theory of random operators and stochastic analysis. Contributions on theoretical aspects, as well as on physical and technical applications are considered for publication. The scope includes the general theory of linear random operators, the theory of random matrices, chaos in classical and quantum mechanics, stochastic differential equations, Brownian motion theory and many other topics.

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