Azizan, Cindy Putri (2025) Deteksi Krisis Keuangan Indonesia Menggunakan Markov Switching Exponential Generalized Autoregressive Conditional Heteroskedasticity dengan Efek Asimetris Berdasarkan Kurs USD terhadap Rupiah. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Krisis keuangan adalah kondisi runtuhnya pasar kredit secara tiba-tiba atau terjadinya guncangan sistemik yang memicu ketegangan dalam sistem keuangan. Besarnya dampak krisis keuangan terhadap stabilitas perekonomian, baik di tingkat nasional maupun global, menyebabkan International Monetary Fund (IMF) menekankan perlunya suatu perangkat sistem yang mampu mendeteksi potensi krisis sedini mungkin. Sistem pendeteksian krisis sangat penting bagi otoritas keuangan dalam hal identifikasi tanda-tanda awal ketidakstabilan melalui pemantauan secara sederhana terhadap indikator krisis keuangan, yang memungkinkan pengambilan langkah preventif sehingga dapat meminimalkan dampak negatif terhadap perekonomian. Pada penelitian ini, dilakukan pendeteksian krisis keuangan di Indonesia berdasarkan indikator kurs USD terhadap rupiah melalui model Markov Switching Exponential Generalized Autoregressive Conditional Heteroskedasticity (MS-EGARCH) yang memperhitungkan efek asimetris. Data yang digunakan adalah kurs USD terhadap rupiah periode bulanan pada Januari 1990 hingga Desember 2024. Model MS-EGARCH dibentuk berdasarkan spesifikasi model volatilitas EGARCH(1,1) dengan melibatkan dua regime, yaitu regime volatilitas rendah dan regime volatilitas tinggi. Hasil analisis menunjukkan bahwa model MS-EGARCH(2,1,1) mendeteksi krisis keuangan pada periode April 1992–Agustus 1992, Desember 1992–Maret 1993, Juni 1994–Februari 1995, Juli 1997–Desember 1997, September 2008, Oktober 2008, Januari 2013, Februari 2013, Juli 2013, Agustus 2013, April 2017, Mei 2017, Februari 2020, dan Maret 2020. Adapun berdasarkan hasil peramalan nilai smoothed probability, Indonesia diperkirakan tidak akan mengalami krisis keuangan pada tahun 2025.
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A financial crisis is a condition characterized by sudden collapse of credit markets or systemic shocks that triggers tensions within the financial system. The significant impact of financial crises on economic stability, both nationally and globally, has led the International Monetary Fund (IMF) to emphasize the need for a system capable of early detection of potential crises. Early detection systems are crucial for financial authorities to identify early signs of instability through simple monitoring of financial crisis indicators, allowing preventive measures to minimize negative economic impacts. In this study, financial crisis detection in Indonesia is conducted based on the USD to rupiah exchange rate indicator using the Markov Switching Exponential Generalized Autoregressive Conditional Heteroskedasticity (MS-EGARCH) model, which accounts for asymmetric effects. The data used are monthly USD to rupiah exchange rates from January 1990 to December 2024. The MS-EGARCH model is specified based on the EGARCH(1,1) volatility model involving two regimes: low volatility and high volatility regimes. The analysis results show that the MS-EGARCH(2,1,1) model detects financial crises during the periods of April 1992–August 1992, December 1992–March 1993, June 1994–February 1995, July 1997–December 1997, September 2008, October 2008, January 2013, February 2013, July 2013, August 2013, April 2017, May 2017, February 2020, and March 2020. Based on the forecasted smoothed probability values, Indonesia is predicted not to experience a financial crisis in 2025.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Efek Asimetris, Krisis Keuangan, Kurs USD terhadap Rupiah, MS-EGARCH Asymmetric Effect, Financial Crisis, MS-EGARCH, USD to Rupiah Exchange Rate |
| Subjects: | H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. Q Science > QA Mathematics > QA274.7 Markov processes--Mathematical models. |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Cindy Putri Azizan |
| Date Deposited: | 01 Aug 2025 08:43 |
| Last Modified: | 01 Aug 2025 08:43 |
| URI: | http://repository.its.ac.id/id/eprint/125564 |
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