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All Journal International Journal of Computing Science and Applied Mathematics
Sulandari, Winita
Universitas Sebelas Maret
Computer Science & IT Education Mathematics

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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; Subanti, Sri; Slamet, Isnandar; Zukhronah, Etik; Susanto, Irwan; Sulandari, Winita; Aprilia, Nabila Churin
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21943

Abstract

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.
Forecasting of Indonesian Crude Prices using ARIMA and Hybrid TSR-ARIMA Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Slamet, Isnandar; Sugiyanto, Sugiyanto; Susanto, Irwan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21946

Abstract

Forecasting of Indonesian crude prices (ICP) is crucial for the government and policymakers. It helps them develop appropriate economic policies, budget allocations, and energy strategies. Forecasting methods that can be used are Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). This study aims to forecast ICP using ARIMA and hybrid TSR-ARIMA models. The data used in this study is the ICP per month, from January 2017 to November 2022. The data is divided into two groups, the data from January 2017 to December 2020 is used as training data, and the data from January 2021 to November 2022 is used as testing data. The MAPE values for the testing data of the TSR-ARIMA(2,1,0) and ARIMA(2,1,0) models are 8.24% and 17.37% respectively. Based on this, it can be concluded that the TSR-ARIMA(2,1,0) model is better than the ARIMA(2,1,0) model for forecasting ICP.
Co-Authors Aprilia, Nabila Churin Irwan Susanto Isnandar Slamet, Isnandar Sri Subanti Sugiyanto, Sugiyanto Zukhronah, Etik