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Estimation of VaR and Expected Shortfall for Stock Returns
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 Title & Authors
Estimation of VaR and Expected Shortfall for Stock Returns
Kim, Ji-Hyun; Park, Hwa-Young;
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 Abstract
Various estimators of two risk measures of a specific financial portfolio, Value-at-Risk and Expected Shortfall, are compared for each case of 1-day and 10-day horizons. We use the Korea Composite Stock Price Index data of 20-year period including the year 2008 of the global financial crisis. Indexes of five foreign stock markets are also used for the empirical comparison study. The estimator considering both the heavy tail of loss distribution and the conditional heteroscedasticity of time series is of main concern, while other standard and new estimators are considered too. We investigate which estimator is best for the Korean stock market and which one shows the best overall performance.
 Keywords
Value-at-Risk(VaR);heavy-tailed distribution;generalized Pareto distribution;conditional heteroscedasticity;
 Language
Korean
 Cited by
1.
다중회귀모형을 이용한 주가상승률에 영향을 미치는 변인분석 -코스피200을 중심으로-,최국렬;사공재현;

Journal of the Korean Data Analysis Society, 2011. vol.13. 6, pp.2935-2944
 References
1.
문성주, 양성국 (2006). 이분산성 및 두꺼운 꼬리분포를 가진 금융시계열의 위험추정: VaR와 ES를 중심으로, <재무관리연구>, 23, 189-208.

2.
Artzner, P., Delbaen, F., Eber, J. and Heath, D. (1999). Coherent measures of risk, Mathematical Finance, 9, 203-228. crossref(new window)

3.
Balkema, A. A. and de Haan, L. (1974). Residual lifetime at great age, Annals of Probability, 2, 792-804. crossref(new window)

4.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. crossref(new window)

5.
Fama, E. F. (1965). The behavior of stock market prices, Journal of Business, 38, 34-105. crossref(new window)

6.
Frey, R. and McNeil, A. (2002). VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights, Journal of Banking and Finance, 26, 1317-1334. crossref(new window)

7.
Gencay, R., Selcuk, F. and Ulugulyagci (2003). High volatility, thick tails and extreme value theory in VaR estimation, Insurance: Mathematics and Economics, 33, 337-356. crossref(new window)

8.
McNeil, A. and Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach, Journal of Empirical Finance, 7, 271-300. crossref(new window)

9.
McNeil, A., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton University Press.

10.
McNeil, A. and Saladin, T. (1997). The peaks over thresholds method for estimaing high quantiles of loss distributions, Proceedings of 28th International ASTIN Colloquium.

11.
McNeil, A. for S-Plus original; R port by Scott Ulman (2007). QRMlib: Provides R-language code to examine Quantitative Risk Management concepts. R package version 1.4.2. http://www.ma.hw.ac.uk/ mcneil/book/index.html.

12.
Pickands, J. (1975). Statistical inference using extreme order statistics, Annals of Statistics, 3, 119-131. crossref(new window)

13.
R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

14.
Rootzen, H. and Tajvidi, N. (1996). Extreme value statistics and wind storm losses: A case study, Scandinavian Actuarial Journal, 70-94.

15.
Seymour, A. and Polakow, D. (2003). A coupling of extreme-value theory and volatility updating with value-at-risk estimation in emerging markets: A South African test, Multinational Finance Journal, 7, 3-23.