Measurement of extreme market risk: Insights from a comprehensive literature review

References

• Abad, P., Benitob, S., & Lópezc, C. (2014). A comprehensive review of value at risk methodologies. The Spanish Review of Financial Economics, 12(1), 15–24. https://doi.org/10.1016/j.srfe.2013.06.001
   Google Scholar
• Alexander & Sheedy, E. (2008). Why VaR models fail and what can be done. Macquarie University Applied Finance Center Research Paper no. 34.
   Google Scholar
• Alexander, C. (2008). Market risk analysis (Vol. ume IV, Value-at-Risk Models). John Wiley & Sons Ltd.
   Google Scholar
• Alexander, C., & Lazar, E. (2006). Normal mixture GARCH(1,1): Applications to exchange rate modelling. Journal of Applied Econometrics, 21(3), 307–336. https://doi.org/10.1002/jae.849
   Web of Science ®Google Scholar
• Alexander, C., & Sheedy, E. (2008). Developing a stress testing framework based on market risk models. Journal of Banking & Finance, 32(10), 2220–2236. https://doi.org/10.1016/j.jbankfin.2007.12.041
   Web of Science ®Google Scholar
• Allen, D., Singh, A., & Powell, R. (2015). EVT and tail-risk modelling: Evidence from market indices and volatility series. The North American Journal of Economics and Finance, 26(C), 355–369. https://doi.org/10.1016/j.najef.2013.02.010
   Google Scholar
• Altun, E., Tatlidil, H., Ozel, G., & Nadarajah, S. (2018). Does the assumption on innovation process play an important role for filtered historical simulation model? Journal of Risk and Financial Management, 11(7), 2–13. https://doi.org/10.3390/jrfm11010007
   Google Scholar
• Amédée-Manesme, C. O., Barthélémy, F., & Maillard, D. (2019, March). Computation of the corrected cornish-fisher expansion using the response surface methodology: Application to VaR and CVaR. Annals of Operations Research, 281(1), 423–453. https://doi.org/10.1007/s10479-018-2792-4
   Web of Science ®Google Scholar
• Amenc, N., Martellini, L., & Vaissé, M. (2003). Benefits and risks of alternative investment strategies. Journal of Asset Management, 4(2), 96–118. https://doi.org/10.1057/palgrave.jam.2240097
   Google Scholar
• Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. Statistical Methodology, 1(2), 105–128. https://doi.org/10.1016/j.stamet.2004.08.004
   Google Scholar
• Angelidis, T., & Degiannakis, S. (2005). Modeling risk for long and short trading positions. The Journal of Risk Finance, 6(3), 226–238. https://doi.org/10.1108/15265940510599838
   Google Scholar
• Antonelli, S., & Iovino, M. G. (2002). Optimization of Monte Carlo procedures for value at risk estimates. Economic Notes, 31(1), 59–78. https://doi.org/10.1111/1468-0300.00072
   Google Scholar
• Aragones, J., Blanco, C., & Dowd, K. (2001). Incorporating stress tests into market risk ( pp. 44–49). Risk Week.
   Google Scholar
• Asai, M., McAleer, M., & Medeiros, M. (2012). Modelling and forecasting noisy realized volatility. Computational Statistics & Data Analysis, 56(1), 217-230. https://doi.org/10.1016/j.csda.2011.06.024
   Google Scholar
• Assaf, A. (2015). Value-at-risk analysis in the MENA equity markets: Fat tails and conditional asymmetries in returns distributions. Journal of Multinational Financial Management, 29(3), 30–45. https://doi.org/10.1016/j.mulfin.2014.11.002
   Google Scholar
• Bae, T., & Iscoe, I. (2012). Large-sample confidence intervals for risk measures of location scale families. Journal of Statistical Planning and Inference, 142(7), 2032–2046. https://doi.org/10.1016/j.jspi.2012.01.008
   Web of Science ®Google Scholar
• Bali, T. (2003). An extreme value approach to estimating volatility and value at risk. The Journal of Business, 76(1), 83–108. https://doi.org/10.1086/344669
   Web of Science ®Google Scholar
• Bali, T., Mo, H., & Tang, Y. (2008). The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR. Journal of Banking & Finance, 32(2), 269–282. https://doi.org/10.1016/j.jbankfin.2007.03.009
   Web of Science ®Google Scholar
• Bali, T., & Neftci, S. (2003). Disturbing extremal behavior of spot rate dynamics. Journal of Empirical Finance, 10(4), 455–477. https://doi.org/10.1016/S0927-5398(02)00070-1
   Google Scholar
• Bali, T., & Theodossiou, P. (2007). A conditional-SGT-VaR approach with alternative GARCH models. Annals of Operations Research, 151(1), 241–267. https://doi.org/10.1007/s10479-006-0118-4
   Web of Science ®Google Scholar
• Bao, Y., Lee, T., & Saltoglu, B. (2006). Evaluating predictive performance of value‐at‐risk models in emerging markets: A reality check. Journal of Forecasting, 25(2), 101–128. https://doi.org/10.1002/for.977
   Web of Science ®Google Scholar
• Barone-Adesi, G., Giannopoulos, K., & Vosper, L. (2002). Backtesting derivative portfolios with Filtered Historical Simulation (FHS). European Financial Management, 8(1), 31–58. https://doi.org/10.1111/1468-036X.00175
   Google Scholar
• Basel Committee on Banking Supervision (BCBS). (2019). Standards: Minimum capital requirements for market risk. BIS.
   Google Scholar
• Bekiros, S., & Georgoukos, D. (2005). Estimation of value-at-risk by extreme value and conventional methods: A comparative evaluation of their predictive performance. Journal of International Financial Markets Institutions and Money, 5(3), 209–228. https://doi.org/10.1016/j.intfin.2004.05.002
   Google Scholar
• BenSaïda, A., & Slim, S. (2016). Highly flexible distributions to fit multiple frequency financial returns. Physica A: Statistical Mechanics and Its Applications, 442, 203–216. https://doi.org/10.1016/j.physa.2015.09.021
   Web of Science ®Google Scholar
• Berk, J., & DeMarzo, P. (2012). Corporate finance. Pearson Education.
   Google Scholar
• Best, P. 1999. Implementing value at risk Chichester. John Wiley and Sons.
   Google Scholar
• Bhattacharyya, M., & Ritolia, G. (2008). Conditional VaR using EVT - Towards a planned margin scheme. International Review of Financial Analysis, 17(2), 382–395. https://doi.org/10.1016/j.irfa.2006.08.004
   Google Scholar
• Boudoukh, J., Richardson, M., & Whitelaw, R. F. (1998). The best of both worlds: A hybrid approach to calculating value at risk. Risk, 11(5), 64–67.
   Google Scholar
• Boudt, K., Carl, P., & Peterson, B. G. (2013). Asset allocation with conditional value-at-risk budgets. Journal of Risk, 15(3), 39–68. https://doi.org/10.21314/JOR.2013.258
   Web of Science ®Google Scholar
• Brooks, C., Clare, A., Molle, J., & Persand, G. (2004). A comparison of extreme value approaches for determining value at risk. Journal of Empirical Finance, 12(2), 339–352. https://doi.org/10.1016/j.jempfin.2004.01.004
   Google Scholar
• Brownlees, C., & Gallo, G. (2011). Shrinkage Estimation of Semiparametric Multiplicative Error Models. International Journal of Forecasting, 27(2), 365-378. https://doi.org/10.1016/j.ijforecast.2010.04.005
   Google Scholar
• Bystrom, H. (2004). Managing extreme risks in tranquil and volatile markets using conditional extreme value theory. International Review of Financial Analysis, 13(2), 133–152. https://doi.org/10.1016/j.irfa.2004.02.003
   Google Scholar
• Chan, K., & Gray, P. (2006). Using extreme value theory to measure value-at-risk for daily electricity spot prices. International Journal of Forecasting, 22(2), 283–300. https://doi.org/10.1016/j.ijforecast.2005.10.002
   Web of Science ®Google Scholar
• Chavez-Demoulin, V., Embrechts, P., & Sardy, S. (2014). Extreme-quantile tracking for financial time series. Journal of Econometrics, 181(1), 44–52. https://doi.org/10.1016/j.jeconom.2014.02.007
   Web of Science ®Google Scholar
• Chen, C., Gerlach, R., Lin, E., and Lee, W. (2012). Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis. Journal of Forecasting. 31(8), 661–687. https://doi.org/10.1002/for.1237
   Google Scholar
• Chen, C., Gerlach, R., Lin, E., & Lee, W. (2012). Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis. Journal of Forecasting, 311, 661–687. https://doi.org/10.1002/for.1237
   Google Scholar
• Cheng, W., & Hung, J. (2011). Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns. Journal of Empirical Finance, 18(1), 160–173. https://doi.org/10.1016/j.jempfin.2010.05.004
   Web of Science ®Google Scholar
• Chernozhukov, V., Fernandez-Val, I., & Galichon, A. (2010). Quantile and Probability Curves Without Crossing. Econometrica, 78(3), 1093–1125. https://doi.org/10.3982/ECTA7880
   Web of Science ®Google Scholar
• Chou, H., & Wang, D. (2014). Estimation of tail-related value-at-risk measures: Range based extreme value approach. Quantitative Finance, 14(2), 293–304. https://doi.org/10.1080/14697688.2013.819113
   Web of Science ®Google Scholar
• Choudhry, M. (2013). An introduction to value-at-risk. John Wiley & Sons Ltd.
   Google Scholar
• Christoffersen, P. (2003). Elements in financial risk management. Academic Press.
   Google Scholar
• Cieslak, A., Morse, A., & Vissing-Jorgensen, A. (2019). Stock returns over the FOMC cycle. Journal of Finance, 74(5), 1–57. https://doi.org/10.1111/jofi.12818
   Web of Science ®Google Scholar
• Cieslak, A., & Vissing-Jorgensen, A. (2018, September 1). SSRN. The Economics of the FedPut Retrieved December 13, 2019, from: https://papers.ssrn.com/sol3/papers.cfm?Abstract_id=2951402
   Google Scholar
• Clare, A., Seaton, J., Smith, P. N., & Thomas, S. (2013). Breaking into the blackbox: Trend following, stop losses and the frequency of trading–The case of the S&P500. Journal of Asset Management, 14(3), 182–194. https://doi.org/10.1057/jam.2013.11
   Google Scholar
• Clements, M., Galvao, A., & Kim, J. (2008). Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility. Journal of Empirical Finance, 15(4), 729-750. https://doi.org/10.1016/j.jempfin.2007.12.001
   Google Scholar
• Coombs, N., & Van Der Heide, A. (2020). Financialization as mathematization: The calculative and regulatory consequences of risk management. In P. Mader, D. Mertens, & N. Van Der Zwan (Eds.), International Handbook of Financialization (pp. 358–368). The Routledge.
   Google Scholar
• Corlu, C., Meterelliyoz, M., & Tinicc, M. (2016). Empirical distributions of daily equity index returns: A comparison. Expert Systems with Applications, 54, 170–192. https://doi.org/10.1016/j.eswa.2015.12.048
   Web of Science ®Google Scholar
• Da Silva, A., & De Mendes, B. (2003). Value-at-risk and extreme returns in asian stock markets. International Journal of Busines, 8(1), 17–40.
   Google Scholar
• Dahlen, K., Solibakke, P., Westgaard, S., & Naess, A. (2015). On the estimation of extreme values for risk assessment and management: The ACER method. International Journal of Business, 20(1), 33–51.
   Web of Science ®Google Scholar
• Danielsson, J., & De Vries, C. (2000). Value-at-risk and extreme returns. Annals of Economics and Statistics, 60, 239–270.
   Google Scholar
• Dimitrakopoulos, D. N., Kavussanos, M. G., & Spyrou, S. (2010). Value at risk models for volatile emerging markets equity portfolios. The Quarterly Review of Economics and Finance, 50(4), 515–526. https://doi.org/10.1016/j.qref.2010.06.006
   Google Scholar
• Dowd, K. (2005). Measuring market risk. Wiley.
   Google Scholar
• Ellis, C. (2017). Scenario-based stress tests: Are they painful enough? Contemporary Economics, 11(2), 219–233. https://doi.org/10.5709/ce.1897-9254.238
   Web of Science ®Google Scholar
• Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer.
   Google Scholar
• Fan, Y., Zhang, Y.-J., Tsai, H.-T., & Wei, Y.-M. (2008). Estimating ‘value at risk’ of crude oil price and its spillover effect using the GED-GARCH approach. Energy Economics, 30(6), 3156–3171. https://doi.org/10.1016/j.eneco.2008.04.002
   Web of Science ®Google Scholar
• Favre, L., & Galeano, J. (2002). Mean-modified value-at-risk optimization with hedge. Journal of Alternative Investment, 5(2), 2–21. https://doi.org/10.3905/jai.2002.319052
   Google Scholar
• Fisher, R., & Tippett, L. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Proceedings of the Cambridge Philosophical Society, 24(2), 180–190. https://doi.org/10.1017/S0305004100015681
   Google Scholar
• Fleming, J., & Kirby, C. (2003). A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility. Journal of Financial Econometrics. 1(3), 365–419. https://doi.org/10.1093/jjfinec/nbg016
   Google Scholar
• Fuentes, F., Herrera, R., & Clements, A. (2018). Modelig extreme risks in commodities and commodity currencies. Pacific-Basin Finance Journal, 51(C), 106–120. https://doi.org/10.1016/j.pacfin.2018.06.003
   Google Scholar
• Fuss, R., Kaiser, D. G., & Adams, Z. (2007). Value at risk, GARCH modelling and the forecasting of hedge fund return volatility. Journal of Derivatives & Hedge Funds, 13(1), 2–25. https://doi.org/10.1057/palgrave.jdhf.1850048
   Google Scholar
• Gencay, R., & Selcuk, F. (2004). Extreme value theory and value-at-risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2), 287–303.
   Web of Science ®Google Scholar
• Gerlach, R.H., Chen, C.W.S., & Chan, N.Y.C. (2011). Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets. Journal of Business & Economic Statistics, 29(4), 481–492. https://doi.org/10.1198/jbes.2010.08203
   Web of Science ®Google Scholar
• Gettinby, G., Sinclair, C., Power, D., & Brown, R. (2006). An analysis of the distribution of extremes in indices of share returns in the US, UK and Japan from 1963 to 2000. International Journal of Finance & Economics, 11(2), 97–113.
   Web of Science ®Google Scholar
• Gettinby, G., Sinclair, C., Power, D., & Brown, R. A. (2004). An analysis of the distribution of the extreme share returns in the UK from 1975 to 2000. Journal of Business Finance & Accounting, 31(5–6), 607–645. https://doi.org/10.1111/j.0306-686X.2004.00551.x
   Google Scholar
• Giamouridis, D., & Ntoula, I. (2009). A comparison of alternative approaches for determining the downside risk of hedge fund strategies . The Journal of Futures Markets, 29(3), 244–269. https://doi.org/10.1002/fut.20358
   Web of Science ®Google Scholar
• Giot, P., & Laurent, S. (2003). Value-at-Risk for long and short trading positions. Journal of Applied Econometrics, 18(6), 641–664. https://doi.org/10.1002/jae.710
   Web of Science ®Google Scholar
• Gkillas, T., & Katsiampa, P. (2018). An application of extreme value theory to cryptocurrencies. Economics Letters, 164(3), 109–111. https://doi.org/10.1016/j.econlet.2018.01.020
   Web of Science ®Google Scholar
• Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2000a). Importance sampling and stratification for value-at-risk. In Y. S. Abu-Mostafa, B. LeBaron, A. Lo, & A. Weigend (Eds.), Computational finance 1999 (Proceedings of the sixth international conference on computational finance) (pp. 7–24). Massachussets Institute of Technology (MIT) Press.
   Google Scholar
• Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2001). Efficient Monte Carlo methods for value-at-risk. In C. Alexander (Ed.), Mastering risk (Vol. 2, pp. 7–20). Financial Times - Pearson Education.
   Google Scholar
• Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2000b). Variance reduction techniques for estimating value-at-risk. Management Science, 46(10), 1349–1364. https://doi.org/10.1287/mnsc.46.10.1349.12274
   Web of Science ®Google Scholar
• Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2002). Portfolio value-at-risk with heavy-tailed risk factors. Mathematical Finance, 12(3), 239–269. https://doi.org/10.1111/1467-9965.00141
   Web of Science ®Google Scholar
• Gónzalez-Rivera, G., Lee, T., Mishra, S. (2004). Forecasting Volatility: A Reality Check Based on Option Pricing, Utility Function, Value-at-Risk, and Predictive Likelihood. International Journal of Forecasting, 20, 629-645. https://doi.org/10.1016/j.ijforecast.2003.10.003
   Google Scholar
• Gottesman, A., & Leobrock, M. (2017). Understanding systemic risk in global financial markets. John Wiley & Sons.
   Google Scholar
• Gueyié, J. P., & Amvella, S. P. (2006). Optimal portfolio allocation using funds of hedge funds. The Journal of Wealth Management Fall, 9(2), 85–95. https://doi.org/10.3905/jwm.2006.644221
   Google Scholar
• Guner, B., Mitov, I., & Racheva-Yotova, B. (2012). Fat-Tailed models for risk estimation. In F. J. Faboozi (Ed.), Encyclopedia of financial models (Vol. ume II, pp. 731–755). John Wiley & Sons, Inc.
   Google Scholar
• Hansen, B. (1994). Autoregressive Conditional Density Estimation. International Economic Review, 35(3), 705–730. https://doi.org/10.2307/2527081
   Web of Science ®Google Scholar
• Harris, R., Kucukozmen, C., & Yilmaz, F. (2004). Skewness in the conditional distribution of daily equity returns. Applied Financial Economics, 14(3), 195–202. https://doi.org/10.1080/0960310042000187379
   Google Scholar
• Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Economic Policy Review, 2(1), 39–69.
   Google Scholar
• Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3(5), 331–341. https://doi.org/10.1214/aos/1176343247
   Web of Science ®Google Scholar
• Hong, L. J., Hu, Z., & Liu, G. (2014). Monte Carlo Methods for value-at-risk and conditional value-at-risk: A review. ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Emerging Methodologies and Applications, 24(4), 22: 1–22:37.
   Google Scholar
• Huang, A., & Tseng, T. (2009). Forecast of value at risk for equity indices: An analysis from developed and emerging markets. The Journal of Risk Finance, 10(4), 393–409. https://doi.org/10.1108/15265940910980687
   Google Scholar
• Huang, A. Y. (2010). An optimization process in value-at-risk estimation. Review of Financial Economics, 19(3), 109–116. https://doi.org/10.1016/j.rfe.2010.03.001
   Google Scholar
• Huang, Y. C., Lin, C. H., Chien, C. C., & Lin, B. J. (2004). The tail fatness and value-at-risk analysis of TAIFEX and SGX-DT Taiwan stock index futures. Asia Pacific Management Review, 9(4), 729–750.
   Google Scholar
• Huisman, R., Koedijk, K., Kool, C., & Palm, F. (2001). Tail-Index estimates in small samples. Journal of Business and Economic Statistics, 19(2), 208–216.
   Web of Science ®Google Scholar
• Hull, J., & White, A. (1998a). Value at risk: When daily changes in market variables are not normally distributed. Journal of Derivatives, 5(3), 9–19. https://doi.org/10.3905/jod.1998.407998
   Google Scholar
• Hull, J., & White, A. (1998b). Incorporating volatility updating into the historical simulation method for value-at-risk. Journal of Risk, 1(1), 5–19. https://doi.org/10.21314/JOR.1998.001
   Google Scholar
• Jackson, P., Maude, D. J., & Perraudin, W. (1997). Bank capital and value at risk. Journal of Derivatives, 4(3), 73–89. https://doi.org/10.3905/jod.1997.407972
   Google Scholar
• Jaschke, S. (2002). The Cornish–Fisher expansion in the context of Delta–Gamma-normal approximations. Journal of Risk, 4(4), 33–52. https://doi.org/10.21314/JOR.2002.068
   Google Scholar
• Jin, X., & Zhang, A. X. (2006). Reclaiming Quasi–Monte Carlo efficiency in portfolio value-at-risk simulation through Fourier transform. Management Science, 52(6), 925–938. https://doi.org/10.1287/mnsc.1060.0505
   Web of Science ®Google Scholar
• Jondeau, E., & Rockinger, M. (1999). The tail behavior of sotck returns: Emerging versus mature markets. Bank of France, Study and Research Notes - 66.
   Google Scholar
• Jorion, P. (2010). Risk management. Annual Review of Financial Economics, 2(1), 347–365. https://doi.org/10.1146/annurev-financial-073009-104045
   Web of Science ®Google Scholar
• Karmakar, M. (2013). Estimation of tail-related risk measures in the Indian stock market: An extreme value approach. Review of Financial Economics, 22(3), 79–85. https://doi.org/10.1016/j.rfe.2013.05.001
   Google Scholar
• Karwowski, E., Shabani, M., & Stockhammer, E. (2017). Financialization: Dimensions and determinants. A Cross-Country Study. Economics Discussion Papers 2017-1, School of Economics, Kingston University London.
   Google Scholar
• Kellner, R., & Rösch, D. (2016). Quantifying market risk with value-at-risk or expected shortfall? - Consequences for capital requirements and model risk. Journal of Economic Dynamics and Control, 68(3), 45–63. https://doi.org/10.1016/j.jedc.2016.05.002
   Web of Science ®Google Scholar
• Kemp, M. (2011). Extreme events - robust portfolio construction in the presence of fat tails. John Wiley & Sons.
   Google Scholar
• Koedijk, K., Schafgans, M., & De Vries, C. (1990). The tail index of exchange rate returns. Journal of International Economics, 29(1–2), 93–108. https://doi.org/10.1016/0022-1996(90)90065-T
   Web of Science ®Google Scholar
• Kou, S., & Peng, X.(2014). Expected shortfall or median shortfall, Journal of Financial Engineering (JFE), 1(01), 1–6. https://doi.org/10.1142/S234576861450007X
   Google Scholar
• Kourouma, L., Dupre, D., Sanfilippo, G., & Taramasco, O. (2011). Extreme value at risk and expected shortfall during financial crisis. Hyper Articles en Ligne - Humanity Society and Sciences (HAL) 00658495.
   Google Scholar
• Kuester, K., Mittnik, S., & Paollela, M. (2006). Value-at-risk prediction: A comparison of alternative strategies. Journal of Financial Econometrics, 4(1), 53–89. https://doi.org/10.1093/jjfinec/nbj002
   Google Scholar
• Lee, M., Su, J., & Liu, H. (2008). Value-at-risk in US stock indices with Skewed generalized error distribution. Applied Financial Economics Letters, 4(6), 425–431. https://doi.org/10.1080/17446540701765274
   Google Scholar
• Lee, S., & Lee, T. (2011). Value-at-risk forecasting based on Gaussian mixture ARMA-GARCH model. Journal of Statistical Computation and Simulation, 81(9), 1131–1144. https://doi.org/10.1080/00949651003752320
   Web of Science ®Google Scholar
• Lehar, A., Scheicher, M., & Schittenkopf, C. (2002). GARCH vs. stochastic volatility: Option pricing and risk management. Journal of Banking & Finance, 26(2–3), 323–345. https://doi.org/10.1016/S0378–4266(01)00225–4
   Google Scholar
• Lian, C., Ma, Y., & Wang, C. (2019). Low interest rates and risk taking: Evidence from individual investment decision. Review of Financial Studies, 32(6), 2107–2148.
   Google Scholar
• Liang, B., & Park, H. (2010). Predicting hedge fund failure: A comparison of risk measures. Journal of Financial and Quantitative Analysis, 45(1), 199–222. https://doi.org/10.1017/S0022109009990482
   Web of Science ®Google Scholar
• Lin, C., Changchien, C., Kao, T., & Kao, W. (2014). High-order moments and extreme value approach for value-at-risk. Journal of Empirical Finance, 29, 1–448. https://doi.org/10.1016/j.jempfin.2014.10.001
   Web of Science ®Google Scholar
• Lin, C., & Shen, S. (2006). Can the student-t distribution provide accurate value at risk? The Journal of Risk Finance, 7(3), 292–300. https://doi.org/10.1108/15265940610664960
   Google Scholar
• Liu, G., Wei, Y., Chen, Y., Yu, J., & Hua, Y. (2018). Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory. Physica A, 499(3), 288–297. https://doi.org/10.1016/j.physa.2018.02.033
   Web of Science ®Google Scholar
• Longin, F. (1996). The asymptotic distribution of extreme stock market returns. Journal of Business, 69(3), 383–408. https://doi.org/10.1086/209695
   Web of Science ®Google Scholar
• Longin, F. (2000). From Value-at-Risk to stress testing: The extreme value approach. Journal of Banking and Finance, 24(7), 1097–1130. https://doi.org/10.1016/S0378-4266(99)00077-1
   Web of Science ®Google Scholar
• Maghyereh, A., & Awartani, B. (2012). Modeling and forecasting value-at-risk in the UAE stock markets: The role of long memory, fat tails and asymmetries in return innovations. Review of Middle East Economics and Finance, 8(1), 1–22. https://doi.org/10.1515/1475-3693.1402
   Google Scholar
• Maillard, D. (2012, January). A user’s guide to the Cornish fisher expansion. Working paper. SSRN. Retrieved July 3, 2019, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1997178
   Google Scholar
• Marimoutou, V., Raggad, B., & Trabelsi, A. (2009). Extreme value theory and value at risk: Application to oil market. Energy Economics, 31(4), 519–530. https://doi.org/10.1016/j.eneco.2009.02.005
   Web of Science ®Google Scholar
• McDonald, J. B., & Newey, W. K. (1988). Partially adaptive estimation of regression models via the generalized T distribution. Econometric Theory, 4(3), 428–457. https://doi.org/10.1017/S0266466600013384
   Web of Science ®Google Scholar
• McNeil, A., Frey, R., & Embrechts, P. (2005). Quantitative risk management. Princeton University Press.
   Google Scholar
• Miftahurrohmah, B., Iriawan, N., & Fithriasari, K. (2017). On the value at risk using bayesian mixture Laplace autoregressive approach for modellingthe Islamic Stock risk investment. Journal of Physics: Conference Series, 855(1), 01–10.
   Google Scholar
• Mina, J., & Ulmer, A. (1999). Delta-Gamma Four Ways (J.P.Morgan/Reuters Working Paper). RiskMetrics Group, J.P.Morgan. http://www.riskmetrics.com/research/working
   Google Scholar
• Mishkin, F. (2009). Is monetary policy effective during financial crises? American Economic Review, 99(2), 573–577. https://doi.org/10.1257/aer.99.2.573
   Web of Science ®Google Scholar
• Muela, S., Martín, C., & Sanz, R. (2017). An application of extreme value theory in estimating liquidity risk. European Research on Management and Business Economics, 23(3), 157–164. https://doi.org/10.1016/j.iedeen.2017.05.001
   Web of Science ®Google Scholar
• Nageswaran, V., & Natarajan, G. (2019). The rise of finance: Causes, consequences and cures. Cambridge University Press.
   Google Scholar
• Navaro, G., & Olmeda, I. (2009). Computational aspects of value at risk. In G. Gregoriou (Ed.), The VaR Implementation Handbook (pp. 167–183). Mc-Graw Hill.
   Google Scholar
• Nikolaev, N., Boshnakov, G., & Zimmer, R. (2013). Heavy-tailed mixture GARCH volatility modeling and value-at-risk estimation. Expert Systems with Applications, 40(6), 2233–2243. https://doi.org/10.1016/j.eswa.2012.10.038
   Web of Science ®Google Scholar
• Odening, M., & Hinrichs, J. (2003). Using extreme value theory to estimate value-at-risk. Agricultural Finance Review, 63(1), 55–73. https://doi.org/10.1108/00215000380001141
   Google Scholar
• Orlowski, L. (2012). Financial crisis and extreme market risks: Evidence from Europe. Review of Financial Economics, 21(3), 120–130. https://doi.org/10.1016/j.rfe.2012.06.006
   Google Scholar
• Ozun, A., Cifter, A., & Yilmazer, S. (2010). Filtered extreme-value theory for value-at-risk estimation: Evidence from Turkey. Journal of Risk Finance, 11(2), 164–179. https://doi.org/10.1108/15265941011025189
   Google Scholar
• Paul, S., & Sharma, P. (2017). Improved VaR forecasts using extreme value theory with the realized GARCH model. Studies in Economics and Finance, 34(2), 238–269. https://doi.org/10.1108/SEF-05-2015-0139
   Web of Science ®Google Scholar
• Pichler, S., & Selitsch, K. (1999). A Comparison of Analytical VaR Methodologies for Portfolios That Include Options (TU Wien Working Paper). Vienna University of Technology (TU Wien). http://www.gloriamundi.org
   Google Scholar
• Pickands, J., III. (1975). Statistical inference using extreme order statistics. Annals of Statististc, 3(1), 119–131.
   Web of Science ®Google Scholar
• Polanski, A., & Stoja, E. (2010). Incorporating higher moments into value-at-risk forecasting. Journal of Forecasting, 29(6), 523–535. https://doi.org/10.1002/for.1155
   Web of Science ®Google Scholar
• Ponwall, R., & Koedijk, K. G. (1999). Capturing downside risk in financial markets: The case of the Asian crisis. Journal of International Money and Finance, 18(6), 853–870. https://doi.org/10.1016/S0261-5606(99)00040-6
   Web of Science ®Google Scholar
• Pritsker, M. (1997). Evaluating value at risk methodologies: Accuracy versus computational time. Journal of Financial Services Research, 12(2/3), 201–242. https://doi.org/10.1023/A:1007978820465
   Web of Science ®Google Scholar
• Ren, F., & Giles, D. (2010). Extreme value analysis of daily Canadian crude oil prices. Applied Financial Economics, 20(12), 941–954. https://doi.org/10.1080/09603101003724323
   Google Scholar
• Roy, I. (2011). Estimating Value at Risk (VaR) using filtered historical simulation in the indian capital market. Reserve Bank of India Occasional Papers, 32(2), 81–98.
   Google Scholar
• Samuel, Y. (2008). Value at risk and conditional extreme value theory via Markov switching models. The Journal of Futures Markets, 28(2), 155–181. https://doi.org/10.1002/fut.20293
   Web of Science ®Google Scholar
• Schoroeck, G. (2002). Risk management and value creation in financial institutions. John Wiley & Sons.
   Google Scholar
• Sener, E., Baronyan, S., & Menguturk, L. (2012). Ranking the Predictive Performances of Value at Risk estimation methods. International Journal of Forecasting, 28(4), 849-873. https://doi.org/10.1016/j.ijforecast.2011.10.002
   Google Scholar
• Siegl, T., & West, A. (2001). Statistical bootstrapping methods in VaR calculation. Applied Mathematical Finance, 8(3), 167–181. https://doi.org/10.1080/13504860110093504
   Google Scholar
• Smith, R. (1987). Estimating tails of probability distributions. Annals of Statistic, 15(3), 1174–1207.
   Web of Science ®Google Scholar
• Straetmans, S., Verschoor, W., & Wolff, C. (2008). Extreme US stock market fluctuations in the wake of 9/11. Journal of Applied Econometrics, 23(1), 17–42. https://doi.org/10.1002/jae.973
   Web of Science ®Google Scholar
• Su, E., & Knowles, T. W. (2006). Asian Pacific stock market volatility modeling and value at risk analysis. Emerging Markets Finance and Trade, 42(2), 18–62. https://doi.org/10.2753/REE1540-496X420202
   Web of Science ®Google Scholar
• Tarullo, D. K. (2014, May 8). Rethinking the aims of prudential regulation. Federal Reserve. Retrieved July 03, 2019, from http://www.federalreserve.gov/newsevents/speech/tarullo20140508a.htm
   Google Scholar
• Tesfalidet, A., Desmond, A. F., Hailu, G., & Singh, R. (2014). Statistical evaluation of value at risk models for estimating agricultural risk. Journal of Statistical and Econometric Methods, 3(1), 1–2.
   Google Scholar
• Theodossiou, P. (2000). Skewed Generalized Error Distribution of Financial Assets and Option Pricing. Working Paper No. 219679, School of Business, Rutgers University.
   Google Scholar
• Tian, M. Z., & Chan, N. H. (2010). Saddle point approximation and volatility estimation of value-at-risk. Statistica Sinica, 20(3), 1239–1256.
   Web of Science ®Google Scholar
• Tolikas, K. (2008). Value-at-risk and extreme value distributions for financial returns. Journal of Risk, 10(3), 31–77. https://doi.org/10.21314/JOR.2008.174
   Web of Science ®Google Scholar
• Tolikas, K. (2014). Unexpected tails in risk measurement: Some international evidence. Journal of Banking and Finance, 40(3), 476–493.
   Web of Science ®Google Scholar
• Tolikas, K., & Fifield, S. G. M. (2011). Rare event risk in African emerging markets. Managerial Finance, 37(3), 275–294. https://doi.org/10.1108/03074351111113324
   Web of Science ®Google Scholar
• Tolikas, K., & Gettinby, G. (2009). Modeling the distribution of the extreme share returns in Singapore. Journal of Empirical Finance, 16(2), 254–263. https://doi.org/10.1016/j.jempfin.2008.06.006
   Web of Science ®Google Scholar
• Tolikas, K., Koulakiotis, A., & Brown, R. (2007). Extreme risk and value-at-risk in the German stock market. The European Journal of Finance, 13(4), 373–395. https://doi.org/10.1080/13518470600763737
   Web of Science ®Google Scholar
• Tomaskov-Devey, D., & Lin, K. (2011). Income dynamics, economic rents and the ‘financialization of the US economy. American Sociological Review, 76(4), 538–559.
   Web of Science ®Google Scholar
• Venkataraman, S. (1997). Value at risk for a mixture of normal distributions: The use of Quasi-Bayesian estimation techniques. Economic Perspectives, 21(2), 2–13.
   Google Scholar
• Vlaar, P. J. (2000). Value at risk models for Dutch bond portfolios. Journal of Banking and Finance, 24(7), 1131–1154. https://doi.org/10.1016/S0378-4266(99)00068-0
   Web of Science ®Google Scholar
• Walls, W., & Zhang, W. (2006). Using extreme value theory to model electricity price risk with an application to the Alberta power market. Energy Exploration & Exploitation, 23(5), 375–403. https://doi.org/10.1260/014459805775992690
   Web of Science ®Google Scholar
• Wijeyakulasuriya, D., & Wickremasinghe, W. (2015). Measuring extreme market risk: The Sri Lankan context. Asia Pacific Journal of Risk and Insurance, 9(2), 185–201. https://doi.org/10.1515/apjri-2014-0026
   Google Scholar
• Wójcik, D., & Ioannou, S. (2020). COVID‐19 and Finance: Market Developments So Far and Potential Impacts on the Financial Sector and Centres. Journal of Economic and Human Geographic History, 111(3), 387–400.
   Google Scholar
• Wong, C. S., & Li, W. K. (2001). On a mixture autoregressive conditional heteroscedastic model. Journal of the American Statistical Association, 96(455), 982–995. http://www.jstor.org/stable/2670244
   Google Scholar
• Wong, M. (2013). Bubble value at risk: A cuntercyclical risk measurment approach. John Wiley & Sons.
   Google Scholar
• Xu, D., & Wirjanto, T. (2010). An empirical characteristic function approach to VaR under a mixture of normal distribution with time-varying volatility. Journal of Derivatives, 18(2), 39–58. https://doi.org/10.3905/jod.2010.18.1.039
   Web of Science ®Google Scholar
• Youssef, M., Belkacem, L., & Mokni, K. (2015). Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach. Energy Economics, 51(3), 99–110. https://doi.org/10.1016/j.eneco.2015.06.010
   Web of Science ®Google Scholar
• Yu, P., Li, W.K., & Jin, S. (2010). On Some Models for Value-At-Risk. Econometric Reviews, 29 (5–6), 622–641. https://doi.org/10.1080/07474938.2010
   Google Scholar
• Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2020.101528
   Google Scholar
• Zhou, J., & Anderson, R. (2012). Extreme risk measures for international REIT markets. The Journal of Real Estate Finance and Economics, 45(1), 152–170. https://doi.org/10.1007/s11146-010-9252-5
   Web of Science ®Google Scholar
• Zikovic, S., & Filer, R. (2013). Ranking of VaR and ES models: Performance in developed and emerging markets. Czech Journal of Economics and Finance, 63(4), 327–359.
   Google Scholar
• Şener, E., Baronyan, S. and Ali Mengütürk, L. (2012). Ranking the predictive performances of value-at-risk estimation methods. International Journal of Forecasting, 28(4), 849-873. https://doi.org/10.1016/j.ijforecast.2011.10.002
   Google Scholar