Abstract
This study aims to investigate the forecasting ability of volatility of Set return based on Bayesian Markov-Switching GARCH models in VaR estimation. We examine whether the Bayesian MSGARCH models with two-regime improve the forecasting volatility of VaR model by comparing with their single-regime counterpart. The empirical results show that Bayesian two-regime MS-GJR-GARCH model with a GED distribution is the best fit to the data based on DIC. The model confirms that the two regimes are different in both unconditional volatility levels and the persistence of the volatility process. The backtesting VaR at 5% risk level results also confirms that the Bayesian two-regime MSGARCH model outperforms their single-regime counterpart. Therefore, this study provides an empirical evidence supporting that Bayesian two-regime MSGARCH model appears to improve forecasting SET volatility.
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Boonyakunakorn, P., Pastpipatkul, P., Sriboonchitta, S. (2019). Value at Risk of SET Returns Based on Bayesian Markov-Switching GARCH Approach. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_25
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