Evaluating predictive performance of value-at-risk models in emerging markets: A reality check

被引:99
作者
Bao, Y
Lee, TH [1 ]
Saltoglu, B
机构
[1] Univ Texas, Dept Econ, San Antonio, TX 78249 USA
[2] Univ Calif Riverside, Dept Econ, Riverside, CA 92521 USA
[3] Marmara Univ, Dept Econ, TR-81040 Istanbul, Turkey
关键词
CaViaR; coverage probability; filtering; quantile loss; reality check; stress testing; VaR;
D O I
10.1002/for.977
中图分类号
F [经济];
学科分类号
02 ;
摘要
We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions-unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White (2000), we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997-1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)-based models do better in the crisis period. Filtering often. appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis. Copyright (c) 2006 John Wiley & Sons, Ltd.
引用
收藏
页码:101 / 128
页数:28
相关论文
共 69 条
[1]  
[Anonymous], DOBBS J
[2]  
[Anonymous], NONPARAMETRIC SEMIPA
[3]  
[Anonymous], NONRANDOM WALK DOWN
[4]  
[Anonymous], J DERIVATIVES
[5]  
[Anonymous], 2002, EUR FINANC MANAG, DOI DOI 10.1111/1468-036X.00175
[6]   RESIDUAL LIFE TIME AT GREAT AGE [J].
BALKEMA, AA ;
DEHAAN, L .
ANNALS OF PROBABILITY, 1974, 2 (05) :792-804
[7]   LIMIT-THEOREMS FOR MAXIMUM TERM IN STATIONARY-SEQUENCES [J].
BERMAN, SM .
ANNALS OF MATHEMATICAL STATISTICS, 1964, 35 (02) :502-+
[8]   Subsampling the distribution of diverging statistics with applications to finance [J].
Bertail, P ;
Haefke, C ;
Politis, DN ;
White, H .
JOURNAL OF ECONOMETRICS, 2004, 120 (02) :295-326
[9]  
Bierens H.J., 2001, EMPIR ECON, V26, P307, DOI [10.1007/s001810000059, DOI 10.1007/S001810000059]
[10]   GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY [J].
BOLLERSLEV, T .
JOURNAL OF ECONOMETRICS, 1986, 31 (03) :307-327