BFS 2002

Poster Presentation

Change detection of stochastic volatility processes

Gábor Molnár-Sáska , Zsuzsanna Vágó, László Gerencsér

We propose to estimate stochastic volatility models using recently developed methods for the statistical analysis of Hidden Markov Models. The realization of HMM-s developed by Borkar in 1992 can be used to establish a link between HMM-s and linear stochastic systems, and $L$-mixing processes, assuming that the underlying Markov chain satisfies the Doeblin condition. This connection is exploited to design a promising change-point detection method for stochastic volatility models, extending a similar method for ARMA-processes. Our approach is compared with the recent results of Berkes, Horváth and Kokoszka on the estimation of GARCH processes.