Maximum Likelihood Estimation in Nonlinear Fractional Stochastic Volatility Model
Asian Research Journal of Mathematics · pp. 1–11 · Published 16 Sep 2017
10.9734/ARJOM/2017/35933Abstract
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.
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