VaR Estimation with Asymmetric GARCH Models

• Main Authors: Hong, Chao-Heng, 洪肇亨
• Other Authors: Liu, Mei-Ying
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/91254896042078049226

碩士 === 東吳大學 === 企業管理學系 === 91 === Some well-known characteristics are common to many financial time series. Volatility clustering is often observed (i.e. large changes tend to be followed by large changes and small changes tend to be followed by small changes; see Mandelbrot, 1963, for early evidence). Second, financial time series often exhibit leptokurtosis, meaning that the distribution of their returns is fat-tailed (i.e. the kurtosis exceed the kurtosis of a standard Gaussian distribution, see Mandelbrot, 1963, or Fama, 1965). Moreover, the so-called “leverage effect”, first noted in Black (1976), refers to the fact that changes in stock prices tend to be negatively correlated with changes in volatility (i.e. volatility is higher after negative shocks than after positive shocks of same magnitude).In this paper we estimate Value-at-Risk (VaR) for four daily indices (GBI+, S&P 500, GBP-USD, ^XAU) return using a collection of parametric models of the GARCH-type (GARCH, EGARCH, GJR and APARCH) based on two distributions (Normal, Student-t). We explore and compare two possible sources of forecasts improvements: asymmetry in the conditional variance, fat-tailed distributions. Our results suggest that that APARCH give better forecasts than symmetric GARCH and increased performance of the forecasts is not observed when using Student-t distributions.