Combined Model of Markov Switching and Asymmetry of Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity for Early Detection of Financial Crisis in Hong Kong
Keywords:
Financial Crisis, Early Detection, Export, MS- AGSARMACHAbstract
The financial crisis in Hong Kong occurred in 1997 and 2008. To prevent a crisis or reduce the impact of a crisis, action is needed through early detection of the crisis using export indicator. The combination of Markov Switching and Asymmetric Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity (MS-AGSARMACH) models explains the crisis well. The results show that the MSAGSARMACH(2,1,1) model can explain past and future crises well.
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