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GARCH 모형을 이용한 한국 원화의 환율변동성 추정

원문정보

Exchange Rate Volatility Estimation Using GARCH Models, with Reference to Korean Won Against US Dollar

모수원, 이광배

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초록

영어

Exchange rate volatility estimation is considered as an important concept in many economic and financial applications like currency rate risk management, asset pricing, and portfolio allocation. This paper attempts to explore the comparative ability of different statistical and econometric volatility forecasting models in the context of Korean won against US dollar, using 1,677 daily observations over a period of 7 years from June 1, 2009 to February 19, 2016. These data series are obtained from one of the most reliable sources in Korea, i.e., BOK online database. In this study, daily returns are the first difference in logarithm of closing prices of won exchange rate of successive days. This paper uses the Generalized Autoregressive Conditional Heteroskedastic (GARCH) models to estimate volatility (conditional variance) in the daily log won value. Three different models are considered in this study. The volatility of the won exchange rate returns is modeled by using univariate GARCH models. These models are GARCH(1, 1), EGARCH(1, 1) and GJR(1, 1) for log difference of won exchange rate return series against US dollar. The models include both symmetric and asymmetric that capture the most common stylized facts about won exchange returns such as volatility clustering and leverage effect. In addition to the issues associated with the volatility itself, there is also the matter of the asymmetric nature of it, where the downward movements in the foreign exchange markets are marked by a higher volatility than upward movements of the same amplitude. It is evident from the findings that asymmetric models are superior to symmetric models in providing a better fit for the exchange rate volatility because of leverage effect.

목차

Abstract
 Ⅰ. 서론
 Ⅱ. 변동성 모형 도입
 Ⅲ. 변동성 모형 추정과 충격반응
 Ⅳ. 결론
 참고문헌

저자정보

  • 모수원 Soo-Won Mo. 목포대학교 무역학과 교수
  • 이광배 Kwang-Bae Lee. 순천대학교 물류학과 교수

참고문헌

자료제공 : 네이버학술정보

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