On Quasi-Maximum Likelihood Estimation of Realized Stochastic Volatility Model via Kalman Filter

Abstract

On estimating financial volatility models, Realized Stochastic Volatility (RSV) models use the information not only returns but also realized volatility measures, and it enables us to estimate the models efficiently.There are two popular approaches for estimating the RSV models; one is the Bayesian Markov chain Monte Carlo technique, and the other is the simulated maximum likelihood method based on the Monte Carlo likelihood.This paper examines a quasi-maximum likelihood method based on the Kalman filer.Especially, this paper compares efficiency of the estimator for the simple RSV model.As asymmetry, long memory, and heavy-tailed distributions are three important features for modeling volatility, the paper introduces various RSV models accommodating these structures, and gives details of the estimation methods based on the Kalman filter.The paper reports empirical results for the various RSV model using stock market indices of U.S., U.K., and Japan.