2008金融危机的爆发令各国的金融监管机构意识到金融体系中系统性风险的重要性，也使得系统性风险方面的研究成为学者们关注的热点。要有效加强对系统性风险的监管，必须以能够有效识别并科学测度系统性风险为前提。边际期望损失(MES，MarginalExpectedShortfall)是由Acharyaetal.(2011)提出的一种金融系统性风险测度的新方法。MES是指在市场收益率出现大幅下跌时，某单个金融机构的系统性风险贡献，该指标可用于确定公司在金融危机中将面临的资本损失。相应的长期指标为长期边际期望损失(LRMES)，即未来一段较长时间内，市场股票价格指数下跌大于某一阈值时单个金融机构的系统性风...The outbreak of the financial crisis makes the financial regulators of many countries aware of the importance of systemic risk in the financial system, which also makes researchers interested in systemic risk. In order to effectively strengthen the regulation of systemic risk, we must be able to effectively identify and scientifically measure systemic risk as a prerequisite.Marginal Expected Short...学位：理学硕士院系专业：经济学院_统计学学号：1542014115200

刘莹

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Xiamen University Institutional Repository

学校编码：10384 学号：15420141152001         硕  士  学  位  论  文         基于序贯蒙特卡罗方法的边际期望损失测度         Marginal Expected Shortfall Measurementbased on Sequential Monte Carlo Method         刘莹         指导教师：林明             专业名称：统计学                答辩日期：2017年4月   厦门大学博硕士论文摘要库厦门大学学位论文原创性声明                  本人呈交的学位论文是本人在导师指导下,独立完成的研究成果。本人在论文写作中参考其他个人或集体已经发表的研究成果，均在文中以适当方式明确标明，并符合法律规范和《厦门大学研究生学术活动规范(试行)》。                  另外，该学位论文为(                  )课题(组)的研究成果，获得(                    )课题(组)经费或实验室的资助，在(          )实验室完成。(请在以上括号内填写课题或课题组负责人或实验室名称，未有此项声明内容的，可以不作特别声明。)                           声明人(签名)：                            年   月   日厦门大学博硕士论文摘要库厦门大学学位论文著作权使用声明                  本人同意厦门大学根据《中华人民共和国学位条例暂行实施办法》等规定保留和使用此学位论文，并向主管部门或其指定机构送交学位论文(包括纸质版和电子版)，允许学位论文进入厦门大学图书馆及其数据库被查阅、借阅。本人同意厦门大学将学位论文加入全国博士、硕士学位论文共建单位数据库进行检索，将学位论文的标题和摘要汇编出版，采用影印、缩印或者其它方式合理复制学位论文。                  本学位论文属于：                  (     )1.经厦门大学保密委员会审查核定的保密学位论文，于年 月 日解密，解密后适用上述授权。                  (     )2.不保密，适用上述授权。                  (请在以上相应括号内打“√”或填上相应内容。保密学位论文应是已经厦门大学保密委员会审定过的学位论文，未经厦门大学保密委员会审定的学位论文均为公开学位论文。此声明栏不填写的，默认为公开学位论文，均适用上述授权。)                           声明人(签名)：                            年   月   日厦门大学博硕士论文摘要库摘  要                  2008金融危机的爆发令各国的金融监管机构意识到金融体系中系统性风险的重要性，也使得系统性风险方面的研究成为学者们关注的热点。要有效加强对系统性风险的监管，必须以能够有效识别并科学测度系统性风险为前提。边际期望损失(MES，Marginal Expected Shortfall)是由Acharya et al. (2011)提出的一种金融系统性风险测度的新方法。MES是指在市场收益率出现大幅下跌时，某单个金融机构的系统性风险贡献，该指标可用于确定公司在金融危机中将面临的资本损失。相应的长期指标为长期边际期望损失(LRMES)，即未来一段较长时间内，市场股票价格指数下跌大于某一阈值时单个金融机构的系统性风险贡献。由于LRMES的计算涉及高维积分，难以得到精确的解析解，Brownlees andEngle(2012)通过拒绝抽样的方法计算LRMES的值。然而，当下跌阈值较大时，事件发生的概率较小，该方法的抽样效率会非常低。因此，考虑采用序贯重要性抽样方法计算LRMES。具体而言，本文采用序贯重要性抽样方法以提高样本的接受概率，解决上述有效样本比率过低的问题。然而在序贯重要性抽样过程中，某些样本分量会发生严重偏斜，导致其对最终样本估计量(LRMES)的贡献微乎其微，实际有效样本量减少。因此，本文考虑采用带重抽样的序贯重要性抽样方法来提高实际有效样本数量。同时，由于重抽样系数的计算涉及高维积分，本文采用模拟导频样本的方法算得该重抽样系数。模拟结果显示，序贯重要性抽样可以有效提高样本接受概率；基于导频的重抽样方法可以增加有效样本数量，并提高样本估计量的准确性。         关键词：长期边际期望损失；序贯重要性抽样；重抽样；导频厦门大学博硕士论文摘要库Abstract                  The outbreak of the financial crisis makes the financial regulators of manycountries aware of the importance of systemic risk in the financial system, whichalso makes researchers interested in systemic risk. In order to effectivelystrengthen the regulation of systemic risk, we must be able to effectively identifyand scientifically measure systemic risk as a prerequisite.Marginal ExpectedShortfall is a new method to measure the financial systemic risk proposed byAcharya et al. (2011). After the financial crisis in 2008, this measure has beenwidely used. MES is the expected loss an equity investor in a financial firm wouldexperience if the overall market declined substantially, which can be used todetermine the capital loss that the company will face in a financial crisis. Thecorresponding long-term indicator is the Long Run Marginal Expected Shortfall(LRMES), the systemic risk contribution of a single financial institution when themarket stock price index falls more than a certain threshold over a long period oftime.Brownlees and Engle (2012) calculate LRMES through rejection method.However, when the threshold is large, the sampling efficiency of the method willbe very low. Therefore, this paper adopted Sequential Importance Samplingmethod to calculate LRMES.To be specific, this paper uses SequentialImportance Sampling method to improve the acceptance probability of the sampleand solve the problem that the effective sample ratio is too low. In the sequentialimportance sampling process, some partial samples are seriously skewed,resulting in minimal contribution to the sample estimation (LRMES), the number ofpractical effective samples decreases. Therefore, this paper considers theSequential Importance Sampling with Resampling method to improve the numberof practical effective samples. At the same time, since the calculation of theweight involves high dimensional integral, this paper calculates the weight by厦门大学博硕士论文摘要库generating pilots. The simulation results show that the Sequential ImportanceSampling method can effectively improve the probability of sample acceptance.Resampling method guided by pilots can increase the number of practicaleffective samples and improve the accuracy of sample estimation.         Keywords: LRMES; Sequential Improtance Sampling; Resampling; Pilots厦门大学博硕士论文摘要库参考资料         [1] 范小云, 王道平, 方意. 我国金融机构的系统性风险贡献测度与监管——基于边际风险     贡献与杠杆率的研究[J]. 南开经济研究, 2011(4):3-20. [2] 方意, 赵胜民, 王道平. 我国金融机构系统性风险测度——基于DGC-GARCH模型的研究[J]. 金融监管研究, 2012(11):26-42. [3] 高国华, 潘英丽. 银行系统性风险度量——基于动态CoVaR方法的分析[J]. 上海交通大学学报, 2011,45(12):1753-1759. [4] 李志辉, 樊莉. 中国商业银行系统性风险溢价实证研究[J]. 当代经济科学, 2011(6):13-20. [5] 彭建刚, 易昊, 童磊. 基于系统性风险防范的金融压力测试新理念[J]. 湖南大学学报(社会科学版),2012, 26(5):37-43. [6] 赵进文, 韦文彬. 基于MES测度我国银行业系统性风险[J]. 金融监管研究, 2012(8):28-40. [7] Acharya V V, Cooley T F, Richardson M, et al. 4. Measuring Systemic Risk[M]// Regulating Wall Street: TheDodd-Frank Act and the New Architecture of Global Finance. John Wiley & Sons, Inc. 2011:85-119. [8] Acharya V, Engle R, Richardson M. Capital Shortfall: A New Approach to Ranking and Regulating SystemicRisks[J]. American Economic Review, 2012, 102(3):59-64. [9] A. F. M. Smith, A. E. Gelfand. Bayesian Statistics without Tears: A Sampling–Resampling Perspective[J].American Statistician, 1992, 46(2):84-88. [10] Adrian,Tobias and Markus Brunnermeier[R]．CoVaR,Working Paper,Federal Reserve Bank of NewYork,2009. [11] Augustine Kong, Jun S. Liu, Wing Hung Wong. Sequential Imputations and Bayesian Missing DataProblems[J]. Journal of the American Statistical Association, 1994, 89(425):278-288. [12] Beadle E R, Djuric P M. A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation[J].IEEE Transactions on Aerospace & Electronic Systems, 1997, 33(1):338-343. [13] Brownlees C T, Engle R F. Volatility, Correlation and Tails for Systemic Risk Measurement[J]. SocialScience Electronic Publishing, 2012:16-18. [14] Chi F, Meng W, Qing-Bo J I. Analysis and Comparison of Resampling Algorithms in Particle Filter[J].Journal of System Simulation, 2009, 14(2):323-331. [15] Doucet A, Freitas N D, Gordon N. Sequential Monte-Carlo Methods in Practice[J]. Journal of theAmerican Statistical Association, 2001, 98(1):496-497. [16] Doucet A, Johansen A M. A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later[J]. InHandbook of Nonlinear Filtering, 2009, 12.厦门大学博硕士论文摘要库 [17] Duan J C. Non-Gaussian Bridge Sampling with an Application[J]. Social Science Electronic Publishing,2015. [18] Engle R. Dynamic Conditional Correlation[J]. Journal of Business & Economic Statistics, 2002, 20(3):339-350. [19] Fearnhead P. Sequential Monte Carlo methods in filter theory /[J]. University of Oxford, 1998,93(443):209-250. [20] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian stateestimation[J]. Radar & Signal Processing Iee Proceedings F, 1993, 140(2):107-113. [21] Huang X, Zhou H, Zhu H. A framework for assessing the systemic risk of major financial institutions[J].Journal of Banking & Finance, 2009, 33(11):2036-2049. [22] Kitagawa G. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models[J]. Journalof Computational & Graphical Statistics, 1996, 1(1):1-25. [23] Lin M, Chen R, Mykland P. On generating Monte Carlo samples of continuous diffusion bridges[J].Journal of the American Statistical Association, 2010, 105(490): 820-838. [24] Liu J S, Chen R. Blind Deconvolution via Sequential Imputations[J]. Journal of the American StatisticalAssociation, 1995, 90(430):567-576. [25] Liu J S. Metropolized independent sampling with comparisons to rejection sampling and importancesampling[J]. 1996, 6(2):113-119. [26] Liu J. Monte Carlo Strategies in Scientific Computing[D]. Springer New York, 2008. [27] Liu, Jun S., and Rong Chen. “Sequential Monte Carlo Methods for Dynamic Systems.” Journal of theAmerican Statistical Association, vol. 93, no. 443, 1998, pp. 1032–1044. [28] Marshall A W. The use of multi-stage sampling schemes in Monte Carlo simulations[J]. Symposium onMonte Carlo Methods, 1954:123-140. [29] Michael K. Pitt, Neil Shephard. Filtering Via Simulation: Auxiliary Particle Filters[J]. Journal of theAmerican Statistical Association, 1999, 94(446):590-599. [30] Robert C P, Casella G. Monte Carlo Statistical Methods[M]// Monte Carlo statistical methods /. Springer,2005:xxx,645. [31] Von Neumann J. Various techniques used in connection with random digits[J].U.s.natl.bur.stand.appl.math.ser, 1951, 12:36-38. [32] Zaritskii V S, Svetnik V B, Shimelevich L I. Monte-Carlo technique in problems of optimal informationprocessing[J]. Automation & Remote Control, 1975, 1975(12):95–103.厦门大学博硕士论文摘要库Degree papers are in the “Xiamen University Electronic Theses and Dissertations Database”. Fulltexts are available in the following ways: 1. If your library is a CALIS member libraries, please log on http://etd.calis.edu.cn/ and submitrequests online, or consult the interlibrary loan department in your library. 2. For users of non-CALIS member libraries, please mail to etd@xmu.edu.cn for delivery details.厦门大学博硕士论文摘要库

Marginal Expected Shortfall Measurement based on Sequential Monte Carlo Method

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Marginal Expected Shortfall Measurement based on Sequential Monte Carlo Method

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