Measurement of Risk Based on QR-GARCH-EVT Model

[1]Morgan J.P, (1996), Risk Metrics technical Document, New York.MorganJ.P1996Risk Metrics technical DocumentNew YorkSearch in Google Scholar

[2]Artzner P., Delbaen F., Jean-Marc E., (1999), Coherent measures of risk, Mathematical Finance, Vol.9:203–228.ArtznerP.DelbaenF.Jean-MarcE.1999Coherent measures of riskMathematical Finance920322810.1017/CBO9780511615337.007Search in Google Scholar

[3]Engel R.F. and NG V.K., (1993), Measuring and testing the impact of news on volatility, Journal of Finance, Vol.5:749–1778.EngelR.F.NGV.K.1993Measuring and testing the impact of news on volatilityJournal of Finance5749177810.3386/w3681Search in Google Scholar

[4]Lin J.J. and Zhang Z.Y., (2003), Application of ARCH model in calculating the value at risk of China's stock market, Theory and practice of system engineering, Vol.5: 20–25.LinJ.J.ZhangZ.Y.2003Application of ARCH model in calculating the value at risk of China's stock marketTheory and practice of system engineering52025Search in Google Scholar

[5]Koenker R. and Bassett G., (1982), Robust Tests for Heteroscedasticity Based on Regression Quantiles, Econometrica, Vol.50:43–61.KoenkerR.BassettG.1982Robust Tests for Heteroscedasticity Based on Regression QuantilesEconometrica50436110.2307/1912528Search in Google Scholar

[6]Bollerslev T., (1986), Generalized Autoregressive Conditional Heteroskedasticity, Journal of Ecomometrics, Vol.131:307–327.BollerslevT.1986Generalized Autoregressive Conditional HeteroskedasticityJournal of Ecomometrics13130732710.1016/0304-4076(86)90063-1Search in Google Scholar

[7]Angelidis T., Benos A., Degiannakis S., (2004), The use of GARCH models in VaR estimation, Statistical Methodology, Vol.1: 105–128.AngelidisT.BenosA.DegiannakisS.2004The use of GARCH models in VaR estimationStatistical Methodology110512810.1016/j.stamet.2004.08.004Search in Google Scholar

[8]Roger K., (1978), Quantile Regression, Cambridge U. Press, 1978.RogerK.1978Quantile RegressionCambridge U. Press1978Search in Google Scholar

[9]Chen S.X. and Tang C.Y., (2005), Nonparametric Inference of Value at Risk for Dependent Financial Return, Journal of Financial Econometrics, Vol.3:227–255.ChenS.X.TangC.Y.2005Nonparametric Inference of Value at Risk for Dependent Financial ReturnJournal of Financial Econometrics322725510.1093/jjfinec/nbi012Search in Google Scholar

[10]Chen X.H., Koenker R., Xiao Z.J., (2009), Copula-based nonlinear quantile autoregression, Econometrics Journal, Royal Economic Society, Vol.12:50–67.ChenX.H.KoenkerR.XiaoZ.J.2009Copula-based nonlinear quantile autoregressionEconometrics Journal, Royal Economic Society12506710.1920/wp.cem.2008.2708Search in Google Scholar

[11]Yang J. and Zhang S.Z., (2014), Dynamic VaR model based on Quantile probability distribution and its response, Statistics and decision, Vol.24:72–75.YangJ.ZhangS.Z.2014Dynamic VaR model based on Quantile probability distribution and its responseStatistics and decision247275Search in Google Scholar

[12]Jian Z.H. and Zeng Y.F., (2016), Research on overnight risk of Shanghai and Shenzhen 300 stock index futures based on CAViaR model, China's management science, Vol.24:1–9.JianZ.H.ZengY.F.2016Research on overnight risk of Shanghai and Shenzhen 300 stock index futures based on CAViaR modelChina's management science2419Search in Google Scholar

[13]Ihsan, Ullah, Badshah, (2012), Quantile Regression Analysis of the Asymmetric Return-Volatility Relation, Journal of Futures Markets, Vol.3:235–265.Ihsan, Ullah, Badshah2012Quantile Regression Analysis of the Asymmetric Return-Volatility RelationJournal of Futures Markets3235265Search in Google Scholar

[14]Westerberg, and Hans S., (2014), The Return to R&D and Seller-buyer Interactions: A Quantile Regression Approach, Ratio Working Papers 231, The Ratio Institute.WesterbergHansS.2014The Return to R&D and Seller-buyer Interactions: A Quantile Regression Approach, Ratio Working Papers 231The Ratio InstituteSearch in Google Scholar

[15]Danielsson J. and De Vries C.G., (1997), Tail Index and Quantile Estimation with Very High Frequency Data, Journal of Empirical Finance, Vol.4:241–257.DanielssonJ.De VriesC.G.1997Tail Index and Quantile Estimation with Very High Frequency DataJournal of Empirical Finance424125710.1016/S0927-5398(97)00008-XSearch in Google Scholar

[16]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, Vol.7, 271–300.McNeilA.J.FreyR.2000Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value ApproachJournal of Empirical Finance727130010.1016/S0927-5398(00)00012-8Search in Google Scholar

[17]Tian X.S. and Guo H.Y., (2004), The application of extreme value theory in risk measurement based on Shanghai stock index 180 Index, Operations research and management science, Vol.1:106–111.TianX.S.GuoH.Y.2004The application of extreme value theory in risk measurement based on Shanghai stock index 180 IndexOperations research and management science1106111Search in Google Scholar

[18]Wei Y., (2008), Risk measurement and a posteriori analysis of stock market, Journal of management science, Vol.11:78–88.WeiY.2008Risk measurement and a posteriori analysis of stock marketJournal of management science117888Search in Google Scholar

[19]Diao D., Tong B., Wu C.F., (2015), Spectrum risk measure based on EVT and its application in risk management, Journal of system engineering, Vol.30:353–369.DiaoD.TongB.WuC.F.2015Spectrum risk measure based on EVT and its application in risk managementJournal of system engineering30353369Search in Google Scholar

[20]Zhou X.H. and Zhang B.S., (2014), Research on extreme risk measurement based on SV-GED model, Journal of Management Engineering, Vol.28:171–178.ZhouX.H.ZhangB.S.2014Research on extreme risk measurement based on SV-GED modelJournal of Management Engineering28171178Search in Google Scholar

[21]Wang H.J., Li D., He X., (2012), Estimation of high conditional quantiles for heavy tailed distributions, American Statistical Association, Vol.107:1453–1464.WangH.J.LiD.HeX.2012Estimation of high conditional quantiles for heavy tailed distributionsAmerican Statistical Association1071453146410.1080/01621459.2012.716382Search in Google Scholar

[22]Xiao Z. and Koenker R., (2009), Conditional quantile estimation for generalized autoregressive conditional heteroscedasticity models, American Statistical Association, Vol.104, 1696–1712.XiaoZ.KoenkerR.2009Conditional quantile estimation for generalized autoregressive conditional heteroscedasticity modelsAmerican Statistical Association1041696171210.1198/jasa.2009.tm09170Search in Google Scholar

[23]DuMouchel W.H., (1983), Estimating the Stable Index in Order to Measure Tail Thickness: A Critique, The Annals of Statistics, Vol.1:1019–1031.DuMouchelW.H.1983Estimating the Stable Index in Order to Measure Tail Thickness: A CritiqueThe Annals of Statistics11019103110.1214/aos/1176346318Search in Google Scholar

[24]Balkema A.A. and De Haan L., (1974), Residual life time at great age, The Annals of Probability, Vol.2:792–804.BalkemaA.A.De HaanL.1974Residual life time at great ageThe Annals of Probability279280410.1214/aop/1176996548Search in Google Scholar

[25]Pickands J., (1975), Statistical inference using extreme order statistics, The Annals of Statistics, Vol.3:119–131.PickandsJ.1975Statistical inference using extreme order statisticsThe Annals of Statistics3119131Search in Google Scholar

[26]Kupiec P.H., (1995), The unique for verifying the accuracy of risk measurement models, Journal of Derivatives, Vol.3:73–84.KupiecP.H.1995The unique for verifying the accuracy of risk measurement modelsJournal of Derivatives3738410.3905/jod.1995.407942Search in Google Scholar