Assessing the risk forecasts for Japanese stock market
Japan and the World Economy, cilt.14, sa.1, ss.63-85, 2002 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 14 Sayı: 1
- Basım Tarihi: 2002
- Doi Numarası: 10.1016/s0922-1425(01)00080-9
- Dergi Adı: Japan and the World Economy
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
- Sayfa Sayıları: ss.63-85
- Anahtar Kelimeler: VaR, ARCH, historical simulation, variance-covariance method, Monte Carlo method, non-parametric quantile regression, extreme value theory, GEV, GPD, hill estimator, data snooping, predictive ability, reality check, loss functions
- Boğaziçi Üniversitesi Adresli: Evet
Özet
We evaluate predictive performance of a selection of value-at-risk (VaR) models for Japanese stock market data. We consider traditional VaR models such as Riskmetrics method, historical simulation, variance-covariance method, Monte Carlo method, and their variants which are integrated with various ARCH models. Also considered are more recent models based on non-parametric quantile regression and extreme value theory (EVT). We apply these methods to the Japanese stock market index (1984-2000) and compare their performances in terms of various evaluation criteria using the method of White [Econometrica 68 (5) (2000) 1097-1126] for three out-of-sample periods of 1995-1996, 1997-1998, and 1999-2000. © 2002 Elsevier Science B.V. All rights reserved.