References Aas, K., and Haff, I.H. (2006). The generalized hyperbolic skew student’st-distribution. Journal of Financial Econometrics, 4(2), 275-309. Abad, P., Benito, S., Lopez, C., and Sanchez-Granero, M.A. (2016). Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis. Journal of Risk, 18(5), 1-28. Abramowitz, M., and Stegun, I. A. (1972). Handbook of mathematical functions with formulas, graphs, and mathematical tables (Vol. 9). Dover: New York. Acerbi, C. and Szekely, B. (2014). Backtesting Expected Shortfall. Publication of MSCI. https://www.msci.com/www/research-paper/research-insight-backtesting/0128184734. Bali, T.G., and Theodossiou, P. (2007). A conditional-SGT-VaR approach with alternative GARCH model. Annals of Operations Research, 151, 241-267. Basel Committee on Banking Supervision, Standards: Minimum Capital requirements for mar- ket risk (2016). Bank for International Settlements. Bao, Y., Lee, T., and Saltoglu, B. (2006). Evaluating predictive performance of value-at-risk models in emerging markets: a reality check. Journal of Forecasting, 25, 101-128. Bhattacharyya, M., and Ritolia, G. (2008). Conditional VaR using EVT: towards a planned margin scheme. International Review of Financial Analysis, 17(2), 382-395. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. Braione, M., and Scholtes, N.K. (2016). Forecasting Value-at-Risk under different distributional Assumptions. Econometrics, 4(3). Cai, Y., and Krishnamoorthy, K. (2006). Exact size and power properties of five tests for multi- nomial proportions. Communications in Statistics-Simulation and Computation, 35(1), 149-160. Caporin, M. (2008). Evaluating Value-at-Risk measures in the presence of long memory condi- tional volatility. The Journal of Risk, 10(3), 79-110. Choi, P., and Nam, K. (2008). Asymmetric and leptokurtic distribution for heteroscedastic asset returns: the SU-normal distribution. Journal of Empirical finance, 15(1), 41-63. Colletaz, G., Hurlin, C., and Pe´rignon, C. (2013). The Risk Map: A new tool for validating risk models. Journal of Banking and Finance, 37(10), 3843-3854. Corlu, C.G., Meterelliyoz, M., and Tinic¸, M. (2016). Empirical distributions of daily equity index returns: A comparison. Expert System with Applications, 54, 170-192. Diamandis, P.F., Drakos, A.A., Kouretas, G.P., and Zarangas, L. (2011). Value-at-Risk for long and short trading positions: Evidence from developed and emerging equity markets. Interna- tional Review of Financial Analysis, 20, 165-176. Ding, Z., Granger, C.W.J., and Engle, R.F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1, 83-106. Du, Z. and Escanciano, J.C. (2016). Backtesting Expected Shortfall: Accounting for Tail Risk. Management Science, 63(4), 940-958. Engle R.F., and Manganelli, S. (2004). CAViaR: conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, 22, 367-381. Fernandez, C., and Steel, M. (1998). On bayesian modelling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371. Gerlach, R., Chen, C.W.S., Lin, E.M.H., and Lee, W.C.W. (2011). Bayesian forecasting for financial risk management, pre and post the global financial crisis. Journal of Forecasting, 31(8), 661-687. Giacomini, R., and Komunjer, I. (2005). Evaluation and combination of conditional quantile forecasts. Journal of Business and Economic Statistics, 23(4), 416-431. Giot, P., and Laurent, S. (2003a). Value-at-Risk for long and short trading positions. Journal of Applied Econometrics, 18, 641-664. Glosten, L., Jagannathan R., and Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779-1801. Hentschel, L. (1995). All in the family nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, 39, 71-104. Hu, W. (2005). Calibration of multivariate generalized hyperbolic distributions using the EM algorithm, with applications in risk management, portfolio opti- mization and portfolio credit risk. Dissertation in the Florida State University. http://diginole.lib.fsu.edu/islandora/object/fsu:181953/datastream/PDF/view Johnson, N.L. (1949). Systems of frequency curves generated by methods of translations. Biometrika, 36, 149-176. Kratz, M., Lok, Y.H., and McNeil, A.J. (2018). Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall. Journal of Banking and Finance, 88, 393-407. Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives, 2, 174-184. Lambert, P., and Laurent, S. (2001). Modelling financial time series using GARCH-type models with a skewed student distribution for the innovations. Mimeo, Universite´ de Liege. Leccadito, A., Boffelli, S., and Urga, G. (2014). Evaluating the accuracy of value-at-risk fore- casts: New multilevel tests. International Journal of Forecasting, 30, 206-216. Lee, C.F., and Su, J.B. (2015). Value-at-Risk estimation via a semiparametric approach: Ev- idence from the stock markets. Handbook of Financial Econometrics and Statistics. Springer Science Business Media: New York. Lopez, J.A. (1998). Testing your risk tests. Financial Survey (May-Jun), 18-20. Lopez, J.A. (1999). Methods for evaluating Value-at-Risk estimates. Federal Reserve Bank of San Francisco Economic Review, 2, 3-17. Louzis, D.P., Xanthopoulos-Sisinis, S., and Refenes, A.P. (2014). Realized volatility models and alternative Value-at-Risk prediction strategies. Economic Modelling, 40, 101-116. McDonald, J. B., and Newey, W. K. (1988). Partially adaptive estimation of regression models via the generalized t distribution. Econometric theory, 4(3), 428-457. Mittnik, S., and Paolella, M. (2000). Conditional density and value-at-risk prediction of Asian currency exchange rates. Journal of Forecasting, 19, 313-333. Nakajima, J., and Omori, Y. (2012). Stochastic volatility model with leverage and asymmetri- cally heavy-tailed error using GH skew Student’s t-distribution. Computational Statistics and Data Analysis, 56(11), 690-3704. Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econo- metrica, 59(2), 347-370. Nozari, M., Raei, S., Jahanguin, P., and Bahramgiri, M. (2010). A comparison of heavy-tailed estimates and filtered historical simulation: evidence from emerging markets. International Re- view of Business Papers, 6(4), 347-359. Ozun, A., Cifter, A., and Yilmazer, S. (2010). Filtered extreme-value theory for value-at-risk estimation: evidence from Turkey. The Journal of Risk Finance, 11(2), 164-179. Novales, A., and Garcia-Jorcano, L. (2019). Backtesting extreme value theory models of ex- pected shortfall. Quantitative Finance, 19(5), 799-825. DOI: 10.1080/14697688.2018.1535182 Paolella, M. S., and Polak, P. (2015). COMFORT: A common market factor non-Gaussian re- turns model. Journal of Econometrics, 187(2), 593-605. Righi, M.B. and Ceretta, P.S. (2013). Individual and flexible Expected Shortfall backtesting. Journal of Risk Model Validation, 7(3), 3-20. Riskmetrics, T. M. (1996). JP Morgan Technical Document. Sarma, M., Thomas, S., and Shah, A. (2003). Selection of value at risk models. Journal of Forecasting, 22, 337-358. Schwert, W. (1990). Stock volatility and the crash of ’87. Review of Financial Studies, 3, 77-102. Simonato, J. G. (2011). The performance of Johnson distributions for computing value at risk and expected shortfall. The Journal of Derivatives, 19(1), 7-24. Taylor, S.J. (1986). Modelling Financial Time Series. John Wiley and Sons, Inc. Theodossiou, P. (1998). Financial data and skewed generalized t distribution. Management Sci- ence, 44, 1650-1661. Yu, P.L.H., Li, W.K., and Jin, S., (2010). On some models for value-at-risk. Econometric Re- views, 29(5-6), 622-641. Zangari, P. (1996). An improved methodology for measuring VaR. RiskMetrics Monitor, 2nd quarter, 7-25.