The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Volatility Forecasting of Korea Composite Stock Price Index with MRS-GARCH Model

국면전환 GARCH 모형을 이용한 코스피 변동성 분석

  • 허진영 (중앙대학교 응용통계학과) ;
  • 성병찬 (중앙대학교 응용통계학과)
  • Received : 2015.01.20
  • Accepted : 2015.03.18
  • Published : 2015.06.30
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Abstract

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

변동성(volatility)은 투자위험을 의미하며 자산의 가격결정이나 포트폴리오 관리 및 투자전략에서 아주 중요한 역할을 한다. 이러한 변동성을 모형화하기 위한 조건부 이분산 모형으로서 전통적인 GARCH(generalized autoregressive conditional heteroskedastic) 모형 및 확장된 형태들이 널리 사용되어지고 있으나, 금융위기와 재정위기와 같은 구조적 변화를 변동성 예측에 반영할 수 없다는 단점을 가지고 있다. 본 논문에서는 이를 극복하기 위한 모형으로서 국면전환 GARCH(Markov regime switching GARCH) 모형을 소개하고, 한국의 일별 KOSPI 수익률에 적용하여 변동성 분석 및 예측을 실시하고, 기존의 GARCH 모형들과 비교하여 그 성능을 평가한다. 그 결과 표본 내(in-sample)의 변동성 적합도 측면에서 국면전환 GARCH 모형이 가장 우수한 성능을 보였으며, 표본 외(out-of-sample) 예측력 측면에서는 국면전환 GARCH 모형이 단기적 예측에서 좋지 않은 성능을 보였으나 장기적 예측에서 우수함을 보였다.

Keywords

References

  1. Hwang, S. and Ryu, H.-S. (2011). An empirical study on the volatility forecasts with regime-switching GARCH model in the Korean stock market, Korean Journal of Financial Studies, 40, 171-194.
  2. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  3. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation, Econometrica, 50, 987-1008. https://doi.org/10.2307/1912773
  4. Glosten, L. R., Jagannathan, R. and Runkle, D. (1993). Relationship between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  5. Gray, S. F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, 42, 27-62. https://doi.org/10.1016/0304-405X(96)00875-6
  6. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica, 57, 357-384. https://doi.org/10.2307/1912559
  7. Hamilton, J. D. (1994). Time Series Analysis, Princeton University Press, Princeton.
  8. Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH, Empirical Economics, 27, 363-394. https://doi.org/10.1007/s001810100100
  9. Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models, Studies in Nonlinear Dynamics and Econometrics, 9, 1-53.
  10. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. https://doi.org/10.2307/2938260
  11. Pagan, A. R. and Schwert, G. W. (1990). Alternative models for conditional stock volatility, Journal of Econometrics, 45, 267-290. https://doi.org/10.1016/0304-4076(90)90101-X

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