Early Detection of South Korean Financial Crisis using MS-GARCH Based on Term of Trade Indicator

Husna Afanyn Khoirunissa, Sugiyanto Sugiyanto, Sri Subanti

Abstract

Abstract. The 1997 Asian financial crisis, which occurred until 1998, had a significant impact on the economies of Asian countries, including South Korea. The crisis brought down the South Korean currency quickly and sent the economy into sudden decline. Because the impact of the financial crisis was severe and sudden, South Korean requires a system which able to sight crisis signals, therefore that, the crisis will be fended off. One in all the indicators that can detect the financial crisis signals is that the term of trade indicator which has high fluctuation and change in the exchange rate regime. The mixture of Markov Switching and volatility models, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), or MS-GARCH could explain the crisis. The MS-GARCH model was built using data from the South Korean term of trade indicator during January 1990 until March 2020. The findings obtained in this research can be inferred that the best model of the term of trade is MS-GARCH (2,1,1). Term of trade indicator on that model could explain the Asian monetary crisis in 1997 and also the global monetary crisis in 2008. The smoothed probability of term of trade indicators predicts in April till December 2020 period, there will be no signs of the monetary crisis in South Korea.

Keywords: financial crisis, MS-GARCH, South Korea, term of trade indicator

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References

Kaminsky, G., Lizondo, S. and Reinhart, C. M. Leading Indicators of Currency Crises. International Monetary Fund Staff Papers. Vol. 45. No. 1. 1998.

Bollerslev, T. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Economics. Vol. 31. No.3, 307-327. 1986.

Hamilton, J.D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica. Vol. 57. No 2, 357-384. 1989.

Chang, K., Cho, K. Y. and Hong, M. Stock Volatility, Foreign Exchange Rate Volatility and the Global Financial Crisis. Journal of Economic Research. Vol. 5 249-272. 2010.

Sugiyanto and Hidayah, A.Z. Indonesian Financial Crisis Prediction Using Combine Volatility and Markov Regime Switching Model Baseed on Indicators of Real Interest Rate on Deposits and Lending Interest Rate/Deposit Interest Rate. Journal of Physics: Conference Series. Vol. 1373. No. 012045. 2019.

Dickey, D.A. and Fuller, W.A. Distribution of the Estimates for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association. 427-431. 1979.

Tsay, R.S. Analysis of Financial Time Series. John Wiley and Sons Inc., New York. 2002.

Cryer, J. Time Series Analysis. PWS Publishers Duxbury Press. Boston. 1986.

Akaike, H. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control. Vol. 19. No. 2, 716-732. 1974.

Gray, S. Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process. Journal of Financial Economics. Vol. 42 27-62. 1996.

Kim, C.J. and Nelson, R.C. State-Space Models with Regime Switching. The MIT Press, Cambridge. 1999.

Guidolin, M. and Pedio, M. Essentials of Time Series for Financial Applications. Academic Press, London. 2018.

IMF International Financial Statistics https://data.imf.org/ accessed on July 2020.

Kihwan, K. The 1997-98 Korean Financial Crisis: Causes, Policy Response, and Lessons. The International Monetary Fund. Singapore. 2006.

DOI: https://doi.org/10.13057/ijas.v4i2.49169

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