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