基于拔靴滤波历史模拟法的黄金市场VaR测度研究
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摘要
传统历史模拟法(THS,Tranditional Historical Simulation)和滤波历史模拟法(FHS,Filtered Historical Simulation)是国际商业银行使用最多的VaR预测方法。首先,论文在已有研究成果的基础上,构造了BHW(Bootstraped Hull and White)历史模拟法;然后,以国内黄金交易数据为样本,采用6种严谨的后验分析(Backtesting analysis)方法,对BHW方法及几种主流历史模拟法、GARCH模型方法的VaR预测精确性进行了实证分析。论文的主要结论包括:(1)综合论文所采用的几种金融风险测度方法来看,BHW方法表现出了相对较好的精确性,而实务界中广泛使用的THS方法则表现出了最差的精确性;(2)不同的历史模拟法受样本规模因素影响的程度显著不同,例如THS方法和HW方法均不同程度的受到了样本规模因素影响;(3)总体来看,BHW方法表现出了相对较好的风险预测精确性,可以作为测度黄金市场风险的选择之一。
Traditional Historical Simulation and Filtered Historical Simulation are the most used risk measurements among international commercial banks.Another filtered historical simulation approach——BHW is presented in this paper,which is constructed by Hull and White(1998)and bootstrap methods.Then the spot trading of gold price is taken as samples,and the accuracy of the BHW method and other popular methods,such as traditional historical simulation methods,BRW methods,Hull and White(1998)methods and parametrical GARCH methods are backtested.Since Dumitrescu et al.(2012)pointed out that there's none backtesting method have absolute advantage on others,and suggest that take more backtesting methods as possible,six different methods are taken.The conclusions include:(1)Compared with the other 4 popular risk measurement methods,BHW methods shows relative better accuracy;(2)In the small sample case(125 days),the advantage of BHW are significantly better than other methods,when the sample size become larger(250 days),HW,BHW and GARCH models all show relative better accuracy,and the HW approach is slightly better than other methods;(3)the accuracy of different historical simualtion methods are influenced by rolling sample size differently.
Traditional Historical Simulation and Filtered Historical Simulation are the most used risk measurements among international commercial banks.Another filtered historical simulation approach——BHW is presented in this paper,which is constructed by Hull and White(1998)and bootstrap methods.Then the spot trading of gold price is taken as samples,and the accuracy of the BHW method and other popular methods,such as traditional historical simulation methods,BRW methods,Hull and White(1998)methods and parametrical GARCH methods are backtested.Since Dumitrescu et al.(2012)pointed out that there's none backtesting method have absolute advantage on others,and suggest that take more backtesting methods as possible,six different methods are taken.The conclusions include:(1)Compared with the other 4 popular risk measurement methods,BHW methods shows relative better accuracy;(2)In the small sample case(125 days),the advantage of BHW are significantly better than other methods,when the sample size become larger(250 days),HW,BHW and GARCH models all show relative better accuracy,and the HW approach is slightly better than other methods;(3)the accuracy of different historical simualtion methods are influenced by rolling sample size differently.
引文
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[25]周茂华,刘骏民,许平祥.基于族模型的黄金市场的风险度量与预测研究[J].国际金融研究,2011,(5):87-96.
[26]Hansen P R,Lunde A.A forecasting comparison of volatility models:Does anything beat a GARCH(1,1)?[J].Journal of Applied Econometrics,2005,20(7):873-889.
[27]Kupiec P.Techniques for verifying the accuracy of risk measurement models[J].Journal of Derivatives,1995,3(2):173-184.
[28]Christoffersen P.Evaluation interval forecasts[J].International Economic Review,1998,39:841-862.
[29]Engle R F,Manganelli S.CAViaR:Conditional autoregressive value at risk by regression quantiles[J].Journal of Business and Economic Statistics,2004,22(4):367-381.
[2]Ardia D,Bluteau K,Boudt K,et al.Forecasting risk with Markov-switching GARCH models:A largescale performance study[J].International Journal of Forecasting.2018,34(4):733-747.
[3]Patton A J,Ziegel J F,Chen R.Dynamic semiparametric models for expected shortfall(and Value-at-Risk)[J].Journal of Econometrics,2019.DOI:10.1016/j.jeconom.2018.10.008.
[4]Sobreira N,Louro R.Evaluation of volatility models for forecasting Value-at-Risk and Expected Shortfall in the Portuguese stock market[J].Finance Research Letters.2019.DOI:10.1016/j.frl.2019.01.010.
[5]余白敏,吴卫星.基于“已实现”波动率ARMA模型和CAViaR模型的VaR预测比较研究[J].中国管理科学.2015,(2):50-58.
[6]刘攀,周若媚.AEPD、AST和ALD分布下金融资产收益率典型事实描述与VaR度量[J].中国管理科学.2015,(2):21-28.
[7]刘晓倩,周勇.加权复合分位数回归方法在动态VaR风险度量中的应用[J].中国管理科学.2015,(6):1-8.
[8]McNeil A,Frey R.Estimation of tail-related risk measures for heteroscedastic financial time series,an extreme value approach[J].Journal of Empirical Finance,2000,7(4):271-300.
[9]Gilli M.An application of extreme value theory for measuring financial risk[J].Computational Economics,2006,27(2-3):207-228.
[10]魏宇.股票市场的极值风险测度及后验分析研究[J].管理科学学报,2008,1(2):78-88.
[11]Boudoukh J,Richardson M,Whitelaw R.The best of both worlds[J].Risk,1998,11(5):64-67.
[12]Barone-Adesi G,Giannopoulos K,Vosper L.VaRwithout correlations for non-linear portfolios[J].Journal of Futures Markets,1999,(19):583-602.
[13]Barone-Adesi G,Giannopoulos K,Vosper L.Backtesting derivative portfolios with filtered historical simulation(FHS)[J].European Financial Management,2002,(8):31-58.
[14]Hull J,White A.Incorporating volatility updating into the historical simulation method for value-at-risk[J].Journal of Risk,1998,1(4):5-19.
[15]Dowd K.Measuring market risk(second edition)[M].John Wiley&Sons.Ltd.2005,84-88.
[16]Costello A,Asem E,Gardner E.Comparison of historically simulated VaR:Evidence from oil prices[J].Energy Economics,2008,30(5):2154-2166.
[17]Daníelsson J,Jorgensen B N,Samorodnitsky G,et al.Fat tails,VaR and subadditivity[J].Journal of Econometrics,2013,172(2):283-291.
[18]叶五一.VaR与CVaR的估计方法以及在风险管理中的应用[D].中国科学技术大学,2006,25-36。
[19]黄剑.历史模拟法诸模型的比较研究[J].金融研究,2012,365(11):180-188.
[20]李孝华,宋敏.基于AEPD分布和ALD分布的VaR模型[J].数量经济技术经济研究,2013,(1):135-149.
[21]Pritske M.The hidden dangers of historical simulation[J].Journal of Banking and Finance,2006,30(2):561-582.
[22]Dumitrescu E,Hurlin Ch,Pham V.Backtesting Value-at-Risk:From dynamic quantile to dynamic binary tests[J].Finance,2012,33(1),79-112.
[23]Efron B.Bootstrap methods:Another look at the jackknife[J].The annals of Statistics,1979,7(1):1-26.
[24]Wang Yudong,Wei Yu,Wu Chongfeng.Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis[J].Physica A:Statistical Mechanics and its Applications,2011,390(5):817-827.
[25]周茂华,刘骏民,许平祥.基于族模型的黄金市场的风险度量与预测研究[J].国际金融研究,2011,(5):87-96.
[26]Hansen P R,Lunde A.A forecasting comparison of volatility models:Does anything beat a GARCH(1,1)?[J].Journal of Applied Econometrics,2005,20(7):873-889.
[27]Kupiec P.Techniques for verifying the accuracy of risk measurement models[J].Journal of Derivatives,1995,3(2):173-184.
[28]Christoffersen P.Evaluation interval forecasts[J].International Economic Review,1998,39:841-862.
[29]Engle R F,Manganelli S.CAViaR:Conditional autoregressive value at risk by regression quantiles[J].Journal of Business and Economic Statistics,2004,22(4):367-381.