내생적 국면전환을 이용한 GARCH 모형
Additive Endogenous Regime Switching GARCH Model
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초록
본 논문은 GARCH(1,1) 모형에 변동성 수준이 높은 국면과 낮은 국면을 나타내는 모수를 추가한 새로운 모형을 분석한다. 본 연구에서는 최근 소개된 Chang et al. (2017)의 내생적 국면전환 모형(Endogenous Regime Switching Model)을 도입함으로써, 이번 기 금융 시계열의 관측치가 다음 기의 변동성이 높거나 낮은 국면을 결정하는 데에 영향을 미치는 내생성을 허용하는 특징을 지닌다. 미국의 월별 주가지수에 적용한 결과, 내생적 국면전환의 구조가 주가 수익률 변동성의 설명력 및 예측력이 향상시키는 것으로 나타났다. 또한 변동성의 수준이 높거나 낮은 국면을 결정하는 잠재변수(latent factor)를 추출하고, Adaptive-LASSO 방법을 이용하여 FRED-MD 데이터베이스의 고차원 거시경제변수들 중 어떠한 변수들이 주식시장 변동성의 설명 및 예측에 관련이 있는지를 분석한다.
Abstract
This paper investigates a new volatility model, in which a regime switching parameter representing a high or low volatility level is added in the GARCH(1,1) model. Importantly, the model adopts the endogenous regime switching model recently introduced by Chang et al. (2017) and the future transition between states depends on the current state as well as the realization of the underlying financial time series. Application on the U.S. monthly stock index return series shows that the endogenous regime switching mechanism improves the data fitting and forecasting of stock return volatility. Moreover, using the latent factor extracted from the model and the FRED-MD data set, we employ the adaptive LASSO method to examine which macroeconomic variables are related to stock market volatility.
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