Estimating parameters of a stochastic volatility model using the expectation-maximization algorithm coupled with a Gaussian particle filter

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Tanit Malakorn
Thanapat Iamtan

Abstract

In this paper, the expectation-maximization algorithm coupled with a Gaussian particle filter for maximum likelihood parameter estimation of a stochastic volatility model is investigated. Two data sets are provided for demonstration purposes: simulated data and daily foreign exchange rates data. Simulation studies illustrate that the parameter estimate trajectories are likely to converge to the true ones. When comparing the empirical results obtained from the conventional method and the proposed method, it can be seen that the parameter estimates from both methods are consistent with each other; however, the computational time is considerably reduced when using the method presented here.

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