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Title: Forecasting performance of Markov-switching GARCH models: A large-scale empirical study Authors:  David Ardia - HEC Montréal (Canada)
Keven Bluteau - Institute of Financial Analysis, University of Neuchatel (Switzerland) [presenting]
Kris Boudt - Vrije Universiteit Brussel and VU Amsterdam (Belgium)
Leopoldo Catania - Aarhus BBS (Denmark)
Abstract: A large-scale empirical study is carried out to evaluate the forecasting performance of Markov-switching GARCH (MSGARCH) models compared with standard single-regime specifications. We find that the need for a Markov-switching mechanism in GARCH models depends on the underlying asset class on which it is applied. For stock data, we find strong evidence for MSGARCH while this is not the case for stock indices and currencies. Moreover, Markov-switching GARCH models with a conditional (skew) Normal distribution are not able to jointly account for the switch in the parameters as well as for the excess of kurtosis exhibited from the data; hence a Markov-switching GARCH model with (skew) Student-t specification is usually required. Finally, accounting for the parameter uncertainty in predictions, via MCMC, is necessary for stock data.