- Volume 29 Issue 1
DOI QR Code
Comparison of semiparametric methods to estimate VaR and ES
조건부 Value-at-Risk와 Expected Shortfall 추정을 위한 준모수적 방법들의 비교 연구
- Received : 2015.12.15
- Accepted : 2015.12.23
- Published : 2016.02.29
Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.
Value-at-Risk;expected shortfall;CAViaR method;CARE method;Gaussian QMLE;back-testing method
- Christoffersen, P. (1998). Evaluating interval forecasts, International Economic Review, 39, 841-862. https://doi.org/10.2307/2527341
- Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap, Chapman and Hall, New York.
- Francq, C. and Zakoian, J.-M. (2004). Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes, Bernoulli, 10, 605-637. https://doi.org/10.3150/bj/1093265632
- Engle, R. F. and Manganelli, S. (2004). CAViaR: conditional autoregressive value at risk by regression quantiles, Journal of Business & Economic Statistics, 22, 367-381. https://doi.org/10.1198/073500104000000370
- Koenker, R. and Bassett, G. (1978). Regression Quantiles, Econometrica, 46, 33-50. https://doi.org/10.2307/1913643
- Lee, S. and Noh, J. (2013). Forecasting value-at-risk by encompassing CAViaR models via information criteria, Journal of the Korean Data and Information Science Society, 24, 1531-1541. https://doi.org/10.7465/jkdi.2013.24.6.1531
- Kim, M. and Lee, S. (2016). Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation, Computational Statistics & Data Analysis, 94, 1-19. https://doi.org/10.1016/j.csda.2015.07.011
- Manganelli, S. and Engle, R. F. (2004). A Comparison of Value-at-Risk Models in Finance, In G. Szego(ed.), Risk Measures for the 21st Century, Chichester, Wiley, UK.
- McNeil, A. J. 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. https://doi.org/10.1016/S0927-5398(00)00012-8
- Newey, W. K. and Powell, J. L. (1987). Asymmetric least squares estimation and testing, Econometrica, 55, 819-847. https://doi.org/10.2307/1911031
- Taylor, J. W. (2008). Estimating value at risk and expected shortfall using expectiles, Journal of Financial Econometrics, 6, 231-252.
Supported by : 한국연구재단