基于 𝐿𝑎𝑠𝑠𝑜 𝑉𝐴𝑅 的上市公司信用风险传染研究

[1] CROUHY M, GALAI D, MARK R. A comparative analysis of current credit risk models[J].Journal of Banking & Finance, 2000, 24(1-2): 59-117.
[2] NAGPAL K, BAHAR R. Measuring default correlation[J]. Risk, 2001, 14(3): 129-132.
[3] KANIOVSKI Y M, PFLUG G C. Risk assessment for credit portfolios: a coupled Markov chain model[J]. Journal of Banking & Finance, 2007, 31(8): 2303-2323.
[4] SKLAR M. Fonctions de repartition an dimensions et leurs marges[J]. Publ. inst. statist. univ.Paris, 1959, 8: 229-231.
[5] LI D X. On default correlation: A copula function approach[J]. The Journal of Fixed Income,2000, 9(4): 43-54.
[6] GAGLIARDINI P, GOURIÉROUX C. Migration correlation: Definition and efficient estimation[J]. Journal of Banking & Finance, 2005, 29(4): 865-894.
[7] AAS K, CZADO C, FRIGESSI A, et al. Pair-copula constructions of multiple dependence[J].Insurance: Mathematics and economics, 2009, 44(2): 182-198.
[8] JAYECH S. The contagion channels of July–August-2011 stock market crash: A DAG-copula based approach[J]. European Journal of Operational Research, 2016, 249(2): 631-646.
[9] LI Z, ZHAO Y, BOTTA N, et al. COPOD: copula-based outlier detection[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 1118-1123.
[10] PHO K H, LY S, LU R, et al. Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China[J]. International Review of Financial Analysis, 2021, 74: 101674.
[11] 戴志锋, 张宗益, 陈银忠. 基于期权定价理论的中国非上市公司信用风险度量研究[J]. 管理科学, 2005, 18(6): 72-77.
[12] 张根明, 陈晓红. 相依违约的违约风险度量研究及其在上市公司中的应用[J]. 系統工程,2008, 26(5): 61-67.
[13] 董乃全. 上市公司信用风险度量模型的实证比较研究[J]. 管理评论, 2010, 22(1): 22-28.
[14] 尹群耀, 陈庭强, 何建敏, 等. 基于滤子理论的信用风险传染模型[J]. 系统工程, 2012, 30(12): 19-25.
[15] 杨星, 胡国强. 交易对手信用违约事件与信用违约互换公允价值[J]. 系统工程理论与实践, 2013, 33(6): 1389-1394.
[16] 萧超武, 蔡文学, 黄晓宇, 等. 基于随机森林的个人信用评估模型研究及实证分析[J]. 管理现代化, 2014(6): 111-113.
[17] 李淑锦, 吕靖强. 基于 BP 神经网络的 P2P 网贷借款者的信用风险评估[J]. 生产力研究,2016(4): 45-49.
[18] 王辉, 梁俊豪. 基于动态因子 Copula 模型的我国银行系统性风险度量[J]. 金融研究, 2020(11): 18.
[19] LANG L H, STULZ R. Contagion and competitive intra-industry effects of bankruptcy announcements: An empirical analysis[J]. Journal of financial economics, 1992, 32(1): 45-60.
[20] KIYOTAKI N, MOORE J. Credit chains[J]. unpublished paper (London School of Economics),1998.
[21] BOSCHI M, GOENKA A. Relative risk aversion and the transmission of financial crises[J].Journal of Economic Dynamics and Control, 2012, 36(1): 85-99.
[22] FANELLI V, MADDALENA L. A nonlinear dynamic model for credit risk contagion[J]. Mathematics and Computers in Simulation, 2020, 174: 45-58.
[23] CERQUETI R, CLEMENTE G P, GRASSI R. Systemic risk assessment through high orderclustering coefficient[J]. Annals of Operations Research, 2021, 299(1): 1165-1187.
[24] JARROW R A, YU F. Counterparty risk and the pricing of defaultable securities[J]. the Journal of Finance, 2001, 56(5): 1765-1799.
[25] ALLEN F, CARLETTI E. Credit risk transfer and contagion[J]. Journal of Monetary Economics, 2006, 53(1): 89-111.
[26] SCHÖNBUCHER P. Information-driven default contagion[J]. Preprint ETH, 2003.
[27] GIESECKE K, WEBER S. Cyclical correlations, credit contagion, and portfolio losses[J]. Journal of Banking & Finance, 2004, 28(12): 3009-3036.
[28] YUF. Default correlation in reduced-form models[J]. Available at SSRN 709523, 2005.
[29] JORION P, ZHANG G. Good and bad credit contagion: Evidence from credit default swaps[J].Journal of Financial Economics, 2007, 84(3): 860-883.
[30] EGLOFF D, LEIPPOLD M, VANINI P. A simple model of credit contagion[J]. Journal ofBanking & Finance, 2007, 31(8): 2475-2492.
[31] GEFANG D, KOOP G, POTTER S M. Understanding liquidity and credit risks in the financialcrisis[J]. Journal of Empirical Finance, 2011, 18(5): 903-914.
[32] JIANG S, FAN H. Credit risk contagion coupling with sentiment contagion[J]. Physica A:Statistical Mechanics and Its Applications, 2018, 512: 186-202.
[33] WANG L, LI S, CHEN T. Investor behavior, information disclosure strategy and counterparty credit risk contagion[J]. Chaos, Solitons & Fractals, 2019, 119: 37-49.
[34] QIAN Q, FENG H, GU J. The influence of risk attitude on credit risk contagion—Perspective of information dissemination[J]. Physica A: Statistical Mechanics and its Applications, 2021,582: 126226.
[35] 陈林, 周宗放. 基于股权比重的企业集团内母子公司之间信用风险传递研究[J]. 管理工程学报, 2009(3): 80-84.
[36] 赵微, 刘玉涛, 周勇. 金融风险中违约传染效应的研究[J]. 数理统计与管理, 2014, 33(006):983-990.
[37] 王军, 赵成国, 胡磊, 等. 互联网金融平台间信用风险传染模型构建与实证研究[J]. 数学的实践与认识, 2021, 51(14): 11.
[38] 陈暮紫, 汤婧, 张小溪, 等. 信用和流动风险冲击下的中国银行业传染分析[J]. 系统工程理论与实践, 2021, 41(6): 16.
[39] 张甜迪, 余雪飞. 极端事件冲击下金融行业总是系统性风险的扩散器吗?——基于行业风险传染的贝叶斯网络研究[J]. 上海金融, 2021.
[40] ERDŐS P, RÉNYI A, et al. On the evolution of random graphs[J]. Publ. Math. Inst. Hung.Acad. Sci, 1960, 5(1): 17-60.
[41] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’networks[J]. Nature,1998, 393(6684): 440-442.
[42] 孙耀吾, 卫英平. 高技术企业联盟知识扩散研究——基于小世界网络的视角[J]. 管理科学学报, 2011, 14(12): 17-26.
[43] 宁连举, 刘茜, 张普宁. 基于社会偏好的网络社群中顾客契合演化研究[J]. 科研管理, 2017,38(9): 150-160.
[44] INAOKA H, TAKAYASU H, SHIMIZU T, et al. Self-similarity of banking network[J]. Physica A: Statistical Mechanics and its Applications, 2004, 339(3-4): 621-634.
[45] EDSON B, CONT R. The brasilian interbank network structure and systemic risk[J]. Banco Central Do Brasil Working Paper Series, 2010, 219.
[46] 欧阳红兵, 刘晓东. 中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J]. 中国管理科学, 2015, 23(10): 30-37.
[47] 吴念鲁, 徐丽丽. 我国银行同业间网络的拓扑结构特征分析及启示[J]. 当代财经, 2015(11): 42-52.
[48] 隋聪, 王宗尧. 银行间网络的无标度特征[J]. 管理科学学报, 2015, 18(12): 18-26.
[49] NIER E, YANG J, YORULMAZER T, et al. Network models and financial stability[J]. Journal of Economic Dynamics and Control, 2007, 31(6): 2033-2060.
[50] GAI P, KAPADIA S. Contagion in financial networks[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2010, 466(2120): 2401-2423.
[51] CHEN T, HE J, LI X. An evolving network model of credit risk contagion in the financial market [J]. Technological and Economic Development of Economy, 2017, 23(1): 22-37.
[52] BAI L, ZHANG X, LIU Y, et al. Economic risk contagion among major economies: Newevidence from EPU spillover analysis in time and frequency domains[J]. Physica A: Statistical Mechanics and its Applications, 2019, 535: 122431.
[53] XIE X, HU X, XU K, et al. Evaluation of associated credit risk in supply chain based on trade credit risk contagion[J]. Procedia Computer Science, 2022, 199: 946-953.
[54] 李守伟, 何建敏. 不同网络结构下银行间传染风险研究[J]. 管理工程学报, 2012, 26(4):71-76.
[55] 孙艳霞, 鲍勤, 汪寿阳. 房地产贷款损失与银行间市场风险传染——基于金融网络方法的研究[J]. 管理评论, 2015(3): 3-15.
[56] 李永奎, 周宗放. 基于无标度网络的关联信用风险传染延迟效应[J]. 系统工程学报, 2015,30(5): 575-583.
[57] 李永奎, 周宗放. 基于小世界网络的企业间关联信用风险传染延迟效应[J]. 系统工程,2015, 33(9): 74-79.
[58] 胡志浩, 李晓花. 复杂金融网络中的风险传染与救助策略——基于中国金融无标度网络上的 SIRS 模型[J]. 财贸经济, 2017, 38(4): 101-114.
[59] 刘超, 徐君慧, 周文文. 中国金融市场的风险溢出效应研究——基于溢出指数和复杂网络方法[J]. 系统工程理论与实践, 2017, 37(4): 831-842.
[60] 刘艳. 中国股市与 G7 国家股市间溢出效应研究[D]. 湖南大学, 2019.
[61] XIAO X, HUANG J. Dynamic connectedness of international crude oil prices: The Diebold–Yilmaz approach[J]. Sustainability, 2018, 10(9): 3298.
[62] TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58(1): 267-288.
[63] 陈婷婷. 基于分位数回归和 Lasso 回归模型的董事会特征对创业板上市公司财务绩效的影响及预测研究[D]. 上海师范大学, 2020.