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Research Article Open access CC BY 4.0

Maximum Likelihood Estimation in Nonlinear Fractional Stochastic Volatility Model

Jaya P. N. Bishwal

Asian Research Journal of Mathematics · pp. 1–11 · Published 16 Sep 2017

10.9734/ARJOM/2017/35933

Abstract

We study the strong consistency and asymptotic normality of the maximum likelihood estimator (MLE) of a drift parameter in a stochastic volatility model when both the asset price process and the stochastic volatility are driven by independent fractional Brownian motions. Long memory in volatility is a stylized fact. We compute the nonlinear filter in the MLE using Kitagawa algorithm.

Fractional Brownian motion stochastic volatility model maximum likelihood estimate strong consistency, asymptotic normality nonlinear ltering long-range dependence.

Cited by 1

A New Algorithm for Approximate Maximum Likelihood Estimation in Sub-fractional Chan-Karolyi-Longstaff-Sanders Model

J. Bishwal · 2021

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