MARKOV SWITCHING SV PROCESSES IN MODELLING VOLATILITY OF FINANCIAL TIME SERIES
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This paper presents a Markov Switching Stochastic Volatility model (MSSV) as a specification of potential use in financial econometrics. The model may be viewed as a specific generalization of a well-known SV construction, that allows the parameters of the conditional volatility equation to switch between a predetermined number of states (regimes). The switching mechanism is driven by a homogenous discrete Markov chain. Without significant loss of generality the author restricts his analysis to two regimes only. Then he concentrates on the estimation procedure of a MSSV model, based on the Quasi-Maximum Likelihood approach presented by Smith in . In order to calculate the quasi-log-likelihood function he considers a linear state-space representation of the MSSV model and employs a combination of the Kalman filter and Hamilton's filter procedures. Finally, four MSSV models and a standard SV model are estimated and compared in terms of goodness of fit to the 1-month WIBOR interest rates.
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