Combined Model of Markov Switching and Asymmetry of Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity for Early Detection of Financial Crisis in Hong Kong

Sugiyanto Sugiyanto (Universitas Sebelas Maret)
Sri Subanti (Universitas Sebelas Maret)
Isnandar Slamet (Universitas Sebelas Maret)
Etik Zukhronah (Universitas Sebelas Maret)
Irwan Susanto (Universitas Sebelas Maret)
Winita Sulandari (Universitas Sebelas Maret)
Nabila Churin Aprilia (Universitas Sebelas Maret)

International Journal of Computing Science and Applied Mathematics-IJCSAM
Vol. 10 No. 2 (2024)

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.