Maximum likelihood estimation with dynamic measurement errors and application to interest rate modeling

Stochastic volatility (SV) model is widely applied in the extension of the constant volatility in Black-Scholes option pricing.In this paper, we extend the SV model driven by fractional Brownian motion (FBM). A crucial problem in its application is how the unknown parameters in the model are to be estimated. We propose the innovation algorithm, and follow by the maximum likelihood estimation approach, which enables us to derive the estimators of parameters involved in this model.We will also present the simulation outcomes to illustrate the efficiency and reliability of the proposed method