Abstract. The use of volatility models to conduct volatility forecasting is gaining momentum in empirical literature. The performance of volatility persistence, as indicated by the estimated parameter φ, in Stochastic Volatility (SV) model is typically high. Since future values in SV models are based on the estimation of the parameters, this may lead to poor volatility forecasts. Furthermore, this high persistence, according to some research scientists, is due to the structure changes (e.g. shift of volatility levels) in the volatility processes, which SV model cannot capture. Hidden Markov Models (HMMs) allow for periods with different volatility levels characterized by the hidden states. This work deals with the problem by bringing in the SV model based on Hidden Markov Models (HMMs), called HMM-SV model. Via hidden states, HMMs allow for periods with different volatility levels characterized by the hidden states. Within each state, SV model is applied to model conditional volatility. Empirical analysis using the proposed HMM-SV models does not only address the structure changes, but also, provides better volatility forecasts and establishes an efficient forecasting structure for volatility modeling.

Keywords: forecasting, hidden Markov model, stochastic volatility, stock exchange.