[1] 陈荣达. 两种外汇期权市场风险非线性VaR计算方法[J]. 系统工程学报,2005,20(1):94-97. [2] 陈荣达. 基于Delta-Gamma-Theta模型的外汇期权风险度量[J]. 系统工程理论与实践,2005,25(7):55-60. [3] Glasserman P,Kang W,Shahabuddin P. Fast simulation of multifactor portfolio credit risk[J]. Operational Research,2008,56(5):1200-1217. [4] Bassamboo A,Juneja S,Zeevi A. Portfolio credit risk with extremal dependence:Asymptotic analysis and efficient simulation[J]. Operations Research,2008,56(3):593-606. [5] Sak H,Hörmann W,Leydold J. Efficient risk simulations for linear asset portfolios in the t-copula model[J]. European Journal of Operational Research,2010,202(3):802-809. [6] Chan J,Kroese D. Efficient estimation of large portfolio loss probabilities in t-copula models[J]. European Journal Operational Research,2010,205(2):361-367. [7] 陈荣达,吕轶. 期权组合市场风险度量的快速卷积方法[J]. 数量经济技术经济研究,2010,27(7):132-141. [8] 尹力博,韩立岩. 人民币外汇期权套保策略:基于随机规划模型[J]. 管理科学学报,2012,15(11):31-44. [9] Grundke P,Polle S. Crisis and risk dependencies[J]. European Journal of Operational Research,2013,223(1):518-528. [10] Claußen A,Löhr S,Rösch D. An analytical approach for systematic risk sensitivity of structured finance products[J]. Review of Derivatives Research,2014,17(1):1-37. [11] 秦学志,胡友群,肖汉. 基于ES-TV的贷款承诺极端风险测度模型[J]. 系统工程理论与实践,2014,34(3):656-662. [12] Dionne G,Pacurar M,Zhou Xiaozhou. Liquidity-adjusted intraday value at risk modeling and risk management:An application to data from Deutsche Börse[J]. Journal of Banking and Finance,2015,59(10):202-219. [13] 顾婧,程翔,周勇. 区间型股票挂钩类结构性产品定价模型与偏差检验[J]. 系统工程,2017,35(6):18-25. [14] Nickerson J,Griffin J. Debt correlations in the wake of the financial crisis:What are appropriate default correlations for structured products?[J]. Journal of Financial Economics,2017,125(3):454-474. [15] Vrins F,Petitjean M. Extreme events and the cumulative distribution of net gains in gambling and structured products[J]. Applied Economics,2018,50(58):6285-6300. [16] Pelster M,Schertler A. Pricing and issuance dependencies in structured financial product portfolios[J]. Journal of Futures Markets,2019,39(3):342-365. [17] Fink H,Geissel S,Sass J. Implied risk aversion:An alternative rating system for retail structured products[J]. Review of Derivatives Research,2019,22(3):357-387. [18] Choi Y,Doshi H,Jacobs K,et al. Pricing structured products with economic covariates[J]. Journal of Financial Economics,2019,DOI:10.1016/j.jfineco.2019.08.002. [19] Hartmann P. Interaction of market and credit risk[J]. Journal of Banking and Finance,2010,34(4):697-702. [20] Breuer T,Jandacka M,Rheinberger K,et al. Does adding up of economic capital for market and credit risk amount to conservative risk assessment?[J]. Journal of Banking and Finance,2010,34(4):703-712. [21] Holton G. Value-at-Risk:Theory and practice[M]. San Diego:Academic Press,second edition,2014. [22] Kima R. Portfolio market and credit risks aggregation using economic scenarios generators and student t-copula[D]. Sweden:Umea University,2014 [23] Castellacci G,Siclari J. The practice of Delta-Gamma VaR:Implementing the quadratic portfolio model. European Journal of Operational Research,2003,150(3):529-545. [24] Albanese C,Jackson K,Wiberg P. A new Fourier Transform algorithm for Value-at-Risk[J]. Quantitative Finance,2004,4(3):328-338. [25] Kamdem S. Δ-VaR and Δ-TVaR for portfolios with mixture of elliptic distributions risk factors and DCC[J]. Insurance:Mathematics and Economics,2009,44(3):325-336. [26] Yueh M,Wong M. Analytical VaR and expected shortfall for quadratic portfolios[J]. Journal of Derivatives,2010,17(3):33-44. [27] Cui Xueting,Zhu Shangshang,Sun Xiaoling,et al. Nonlinear portfolio selection using approximate parametric Value-at-Risk[J]. Journal of Banking and Finance,2013,37(10):2124-2139. [28] Jewell S,Li Yang,Pirvu T. Non-linear equity portfolio variance reduction under a mean-variance framework-A delta-gamma approach[J]. Operations Research Letters,2013,41(6):694-700. [29] Wang Xiaoyu,Xie Dejun,Jiang Jingjing,et al. Value-at-Risk estimation with stochastic interest rate models for option-bond portfolios[J]. Finance Research Letters,2017,21:10-20. [30] Duffie D,Pan Jun. Analytical value-at-risk with jumps and credit risk[J]. Finance and Stochastics,2001,5(2):155-180. [31] Elliott R,Chan L,Siu T. Risk measures for derivatives with Markov-modulated pure jump processes[J]. Asia-Pacific Financial Markets,2006,13(2):129-149. [32] Grundke P. Importance sampling for integrated market and credit portfolio models[J]. European Journal of Operational Research,2009,194(1):206-226. [33] Frey R,Backhaus J. Dynamic hedging of synthetic CDO tranches with spread risk and default contagion[J]. Journal of Economic Dynamics & Control,2010,34(4):710-724. [34] Schröter A,Heider P. Numerical methods to quantify the model risk of basket default swaps[J]. Journal of Computational and Applied Mathematics,2013,251:117-132. [35] Beyot A,Frugier J,Benoit G. Risk management in exotic derivatives trading:Lessons from the recent past[R]. Working Paper,Chappuis Halder & Company on Risk Library,2015. [36] Skoglund J,Chen Wei. Financial risk management:Applications in market, credit, asset and liability management, and firmwide risk[M]. Hoboken:John Wiley Sons Inc,2015. [37] Glasserman P. Monte Carlo methods in financial engineering[M]. New York:Springer,2004. [38] Bassamboo A,Jain S. Efficient importance sampling for reduced form models in credit risk[C]//Proceedings of the 2006 Winter Simulation Conference, Monterey, CA, USA, December 3-6,2006:741-748. [39] Juneja S,Shahabuddin P. Rare-event simulation techniques:An introduction and recent advances[J]. Handbooks in Operations Research and Management Science,2006,13:291-350. [40] Blanchet J,Lam H. State-dependent importance sampling for rare-event simulation:An overview and recent advances[J]. Surveys in Operations Research and Management Science,2012,17(1):38-59. [41] Chen Rongda,Yu Huanhuan. Risk measurement for portfolio credit risk based on a mixed Poisson model[J]. Discrete Dynamics in Nature and Society,2014:1-9. [42] Scott A,Metzlerb A. A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models[J]. Insurance:Mathematics and Economics,2015,64(9):279-293. [43] JP Morgan. RiskMetrics-Technical Document[R]. Discussion Paper,http//www. RiskMetrics.com,1996. [44] Britten-Jones M,Schaefer S. Non-linear Value-at-Risk[J]. European Finance Review,1999,2:161-187. [45] Glasserman P,Heidelberger P,Shahabuddin P. Variance reduction techniques for estimating Value-at-Risk[J]. Management Science,2000,46(10):1349-1364. [46] 田新时,刘汉中,李耀. 基于DeltaGamma正态模型的VaR计算[J]. 系统工程,2002,20(5):92-96. [47] Nyström K. Harmonic analysis,quadratic forms and asymptotic expansions of risk measures[J]. Applied Mathematical Sciences,2008,21(2):1023-1052. [48] Mina J,Ulmer A. Delta-Gamma four ways[R]. Working Paper,RiskMetrics Group,1999. [49] Hardle W,Kleinow T,Stahl G. Applied quantitative finance[M]. New York:Springer-Verlag Berlin,2002. [50] Cardenas J,Fruchard E,Picron J,et al. Monte carlo within a day[J]. Risk,1999,12(2):55-59. [51] Glasserman P,Heidelberger P,Shahabuddin P. Efficient monte carlo methods for value-at-risk[J]. Mastering Risk,2001,2. [52] Hosking J,Bonti G,Siegel D. Beyond the lognormal[J]. Risk,2000,13(5):59-62. [53] Heyde C.C.,and Kou S. On the controversy over tail weight of distributions[J]. Operations Research Letters,2004,32(5):399-408. [54] 史敏. 证券投资基金绩效评价与风险管理研究[D]. 北京:中国科学院研究生院,2005. [55] Lin S,Wang R,Fuh C. Risk management for linear and non-linear assets:A bootstrap method with importance resampling to evaluate value-at-risk[J]. Asia-Pacific Financial Markets,2006,13(3):261-295. [56] Aas K,Haff I. The generalized hyperbolic skew Student's t-distribution[J]. Journal of Financial Econometrics,2006,4(2):275-309. [57] Behr A,Pötter U. Alternatives to the normal model of stock returns:Gaussian mixture,generalised logF and generalised hyperbolic models[J]. Annals of Finance,2009,5(1):49-68. [58] 周孝华,张保帅,董耀武. 基于Copula-SV-GPD模型的投资组合风险度量[J]. 管理科学学报,2012,15(12):70-78. [59] Jules S. VaR and ES for linear portfolios with mixture of generalized Laplace distributions risk factors[J]. Annals of Finance,2012,8(1):123-150. [60] 王鹏. 基于时变高阶矩波动模型的VaR与ES度量[J]. 管理科学学报, 2013,16(2):33-45. [61] 魏宇,赖晓东,余江. 沪深300股指期货日内避险模型及效率研究[J]. 管理科学学报,2013,16(3):29-40. [62] Schneider J,Schweizer N. Robust measurement of (heavy-tailed) risks:Theory and implementation[J]. Journal of Economic Dynamics & Control,2015,61(12):183-203. [63] 方立兵,曾勇. 股市收益率高阶矩风险的产生机制检验[J]. 中国管理科学,2016,24(04):27-36. [64] 王鹏,吴金宴. 基于协高阶矩视角的沪港股市风险传染分析[J]. 管理科学学报,2018,21(6):29-42. [65] Glasserman P,Heidelberger P,Shahabuddin P. Portfolio value-at-risk with heavy-tailed risk factors[J]. Mathematical Finance,2002,12(3):239-269. [66] Albanese C,Jackson K,Wiberg P. A new Fourier Transform algorithm for Value-at-Risk[J]. Quantitative Finance,2004,4(3):328-338. [67] Albanese C,Campolieti G. Advanced derivatives pricing and risk management[M]. Diego California:Elsevier Academic Press,2006. [68] Date P,Bustreo R. Measuring the risk of a non-linear portfolio with fat-tailed risk factors through a probability conserving transformation[J]. IMA Journal of Management Mathematics,2014,8:1-25. [69] Johannes V,Jeffrey T,Anna,S. Value-at-Risk computation by Fourier inversion with explicit error bonds[J]. Finance Research Letters,2009,6(2):95-105. [70] 陈荣达,马庆国,孙元. 基于汇率回报厚尾性的外汇期权组合非线性VaR模型[J]. 管理工程学报,2009,23(3):115-119. [71] Fuh C,Hu I,Hsu Y. Efficient simulation of value at risk with heavy-tailed risk factors[J]. Operations Research,2011,59(6):1395-1406. [72] Chen Rongda,Yu Lean. A novel nonlinear value-at-risk method for modeling risk of option portfolio with multivariate mixture of normal distributions[J]. Economic Modelling,2013,35(9):796-804. [73] Kamdem J,Genz A. Approximation of multiple integrals over hyperboloids with application to a quadratic portfolio with options[J]. Computational Statistics and Data Analysis,2008,52(7):3389-3407. [74] Brummelhuis R,Kamdem J. VaR for quadratic portfolio's with generalized Laplace distributed returns[R]. Working Paper,University of Reims,2009. [75] Grundke P. Computational aspects of integrated market and credit portfolio models[J]. OR Spectrum,2007,29(2):259-294. [76] Glasserman P,Li Jingyi. Importance sampling for portfolio credit risk. Management science, 2015,51(11):1643-1656. [77] Rossignolo A,Fethi M,Shaban M. Market crises and Basel capital requirements:Could Basel III have been different? Evidence from Portugal,Ireland,Greece and Spain (PIGS)[J]. Journal of Banking and Finance,2013,37(5):1323-1339. [78] King M. The Basel III net stable funding ratio and bank net interest margins[J]. Journal of Banking and Finance,2013,37(11):4144-4156. [79] Dietrich A,Hess K,Wanzenried G. The good and bad news about the new liquidity rules of Basel III in Western European countries[J]. Journal of Banking and Finance,2014,44:13-25. [80] Schmaltz C,Pokutta S,Heidorn T,et al. How to make regulators and shareholders happy under Basel III[J]. Journal of Banking and Finance,2014,46:311-325. [81] 李少华,程远杰. 基于结构化模型的金融衍生品流动性分析[J]. 同济大学学报(自然科学版),2014,42(11):1765-1769. [82] Bai J,Krishnamurthy A,Weymuller C. Measuring liquidity mismatch in the banking sector[J]. The Journal of Finance,2018,73(1):51-93. [83] 高强,邹恒甫. 企业债与公司债二级市场定价比较研究[J]. 金融研究,2015,1:84-100. [84] Wu Ying. Asset pricing with extreme liquidity risk[J]. Journal of Empirical Finance,2019,54:143-165. [85] Yu Lean,Wang Shouyang,Lai K. Neural network-based mean-variance-skewness model for portfolio selection[J]. Computers & Operations Research, 2008, 35(1):34-46. [86] 孙柏,谢赤. 金融危机背景下的人民币汇率预测[J]. 系统工程理论与实践,2009,29(12):53-64. [87] Chen Yan,Mabu S,Hirasawa K. A model of portfolio optimization using time adapting genetic network programming[J]. Computers & Operations Research,2010,37(10):1697-1707. [88] Yu Lean,Yue Wuyi,Wang Shouyang,et al. Support vector machine based multiagent ensemble learning for credit risk evaluation[J]. Expert Systems with Applications,2010,37(2):1351-1360. [89] Tang Ling,Yu Lean,Wang Shuai,et al. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting[J]. Applied Energy,2012,93:432-443. [90] 姚潇,余乐安. 模糊近似支持向量机模型及其在信用风险评估中的应用[J]. 系统工程理论与实践,2012,32(3):549-554. [91] Yu Lean,and Yao X.iao A total least squares proximal support vector classifier for credit risk evaluation[J]. Soft Computing,2013,17(4):643-650. [92] 刘勇军,张卫国,徐维军. 考虑现实约束的模糊多准则投资组合优化模型[J]. 系统工程理论与实践,2013,33(10):2462-2470. [93] Chen Yan,Wang Xuancheng. A hybrid stock trading system using genetic network programming and mean conditional value-at-risk[J]. European Journal of Operational Research,2015,240(3):861-871. [94] Yu Lean,Dai Wei,Tang Ling,et al. A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting[J]. Neural Computing and Applications,2015:1-23. [95] Kim H,Shin K. A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets[J]. Applied Soft Computing,2007,7(2):569-576. [96] Andreou P,Charalambous C,aMartzoukos S. Pricing and trading european options by combining artificial neural networks and parametric models with implied parameters[J]. European Journal of Operational Research,2008,185(3):1415-1433. [97] Wang Y. Nonlinear neural network forecasting model for stock index option price:Hybrid GJR-GARCH approach[J]. Expert Systems with Applications,2009,36(1):564-570. [98] Chi B,Hsu C. A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model[J]. Expert Systems with Applications,2012,39(3):2650-2661. [99] Akkoc S. An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis:The case of Turkish credit card data[J]. European Journal of Operational Research,2012,222(1):168-178. [100] Kazema A,Sharifi E,Hussain F. Support vector regression with chaos-based firefly algorithm for stock market price forecasting[J]. Applied Soft Computing,2013,13(2):947-958. [101] Tkáč M,Verner R. Artificial neural networks in business:Two decades of research[J]. Applied Soft Computing,2016,38:788-804. [102] Huang Wei,Nakamori Y,Wang Shouyang. Forecasting stock market movement direction with support vector machine[J]. Computers & Operations Research,2005,32(10):2513-2522. [103] Huang S. Online option price forecasting by using unscented Kalman filters and support vector machines[J]. Expert Systems with Applications,2008,34(4):2819-2825. [104] Pires M,Marwala T. Option pricing using Bayesian neural networks[R]. Working Paper,arXiv.org,2007. [105] Gradojevic N,Gencay R,Kukolj D. Option pricing with modular neural networks[J]. IEEE Transactions on Neural Networks,2009,20(4):626-637. [106] 张鸿彦,林辉. 应用混合神经网络和遗传算法的期权价格预测模型[J]. 管理工程学报,2009,23(1):59-62. [107] 张鸿彦,林辉,姜彩楼. 用混合小波网络和遗传算法对期权定价的研究[J]. 系统工程学报,2010,25(1):43-49. [108] 王林,张蕾,刘连峰. 用模拟退火算法寻找Heston期权定价模型参数[J]. 数量经济技术经济研究,2011,9:131-139. [109] Chen Fei,Sutcliffe C. Pricing and hedging short Sterling options using neural networks[J]. International Journal of Intelligent Systems in Accounting and Finance,2012,19(2):128-149. [110] Hahn J. Option pricing using artificial neural networks:An Australian perspective[D]. Robina:Bond University Faculty of Business,2013. [111] Verma N,Srivastava N,Das S. Forecasting the price of call option using support vector regression[J]. IOSR Journal of Mathematics,2014,10(6):38-43. [112] Hsu C.M.,Fu Yingchi.,Liu Yuchun,et al. Forecasting the prices of TAIEX options by using genetic programming and support vector regression[C]. Proceedings of the International Multiconference of Engineers and Computer Scientists,March 18-20, Hong Kong,2015. [113] 李斌,何万里. 一种寻找Heston期权定价模型参数的新方法[J]. 数量经济技术经济研究,2015,3:129-146. |