국면전환 GARCH 모형을 이용한 코스피 변동성 분석

Abstract

Abstract Volatility forecasting in financial markets is an important issue because it is directly related to the profitof return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalizedautoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is notsuitable 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, weanalyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data fromJanuary 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with aleverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH modelsfor in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-samplefitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflectfinancial market structural changes into modeling and volatility forecasting.Keywords: conditional heteroskedasticity, Markov regime switching model, structural change