【作者】 叶康；

【导师】 缪柏其； 王怀清；

【作者基本信息】 中国科学技术大学 ， 金融工程， 2009， 博士

【摘要】 次贷危机造成了当前所面临的以全球信用市场和银行系统的流动性紧缺为特征的经济问题。由于房地产市场的违约事件和抵押资产的缩水,很多银行、房地产信托投资公司和对冲基金已经遭受了巨大的损失。当前的次贷危机是金融机构系统性风险(systemic risk)的典型案例。所谓系统性风险,是指由某个个体或部分的损失而带来大多数个体乃至整体的损失的风险。在现代金融市场上,金融创新等诸多因素使得当前金融机构的系统性风险管理变得愈来愈复杂。所以正如诺贝尔经济学获得者Michael Spence所说,当前的一个巨大挑战就是如何更好地去认识金融机构系统性风险管理的动态性和复杂性,从而为金融不稳定环境下的预警系统提供分析的基础。这也就是此论文的研究出发点。在最近的二三十年里,基于知识的技术已经被广泛运用于解决各种复杂的问题。为了加深我们对于金融机构系统性风险管理的理解和交流,本论文提出了一个系统性风险管理的知识层面模型(Knowledge Level Model)。所谓知识层面模型,是由Alan Newell首先提出,其核心是在不过多关注底层具体实现的基础上去获取知识和表达知识。本文所提出的知识层面模型包括两个部分:本体和问题解决模型。其中本体部分包括顶层本体和领域本体,用于表达知识层面模型的知识基础,而问题解决模型用于表达在此知识基础上推理过程。而且为了着重研究系统性风险的传染效应,本文还对领域本体进行了进一步的研究。即通过开发静态本体、动态本体和社会本体这三种不同的本体对这个问题领域的不同角度进行了研究。本文还讨论了知识层面模型的具体实现,也就是其在符号层面上的映射。具体地,OWL,SWRL和JESS被用于该模型的具体实现。最后,本文还通过对2008年的雷曼债券事件和1998年的长期资本管理公司事件的研究,讨论了所提出的模型在实践中的运用。本文所提出的知识层面的模型对于金融机构的系统性风险管理有如下三个方面的意义:(1),可以促进单个金融机构内部的信息共享和知识共享:(2),可以辅助金融机构系统性风险管理系统的知识库设计;(3),可以提供此领域的标准语言从而促进不同金融机构间的合作。最后从信息系统研究中设计科学的角度,整个研究框架也对其他金融信息系统领域的建模过程有一定的借鉴意义。更多还原

【Abstract】 The subprime mortgage crisis is an ongoing economic problem characterized by contracted liquidity in the global credit markets and banking system.Many banks,real estate investment trusts(REIT),and hedge funds have suffered significant losses as a result of mortgage payment defaults or mortgage asset devaluation.The current subprime mortgage crisis is a typical case for systemic risk in financial institutions.Systemic risk refers to the risk or probability of breakdown(losses) in individual parts of components and is evidenced by co-movements(correlation) among most or all parts.Many factors contribute to the complexity of the systemic risk management (SRM) in financial institutions at modern society,e.g.financial innovations.So as mentioned by Nobel laureate,Dr.A.Michael Spence,an important challenge in going forward is to better understand these dynamics and complexities of SRM in financial institutions as the analytical underpinning of an early warning system with respect to financial instability.The objective of this thesis is to fill this gap.Knowledge based technologies have been used considerably to solve complex problems during the last few decades.In order to further our understanding and communication about SRM in financial institutions,this thesis proposes a knowledge level model(KLM) for systemic risk management in financial institutions.As first proposed by Newell,the main focus of KLM is to capture and represent knowledge without paying more attention to implementation.There are two parts considered in the proposed KLM are:ontologies and problem solving method(PSM).Ontologies are adopted to represent a knowledge base of KLM,which integrates top level ontology and domain level ontologies.And then the problem solving method is given to show the reasoning process of this knowledge.Further,the details of domain level ontologies for SRM,the focus of which is the contagion effect of systemic risk,are also discussed in this thesis.Three categories of ontologoes,inchding static ontology,dynamic ontology,and social ontology aredeveloped to deal with different perspectives in this domain.The symbol level of KLM is also discussed which integrates OWL,SWRL and JESS. Further,two financial cases are provided to illustrate how proposed KLM is used in financial institutions,including the LTCM case in 1998 and Lehman Brother’s minibonds case in 2008. With these,first,they will enhance the interchange of information and knowledge sharing for SRM within a financial institution.Second,they will assist knowledge base development for SRM design,for which a prototype of financial systemic risk management decision support system is given in this study.Third,they will support coordination among different institutions by using standardized vocabularies.And finally,from the design science perspective,the whole proposed framework could be meaningful to models in other domains.更多还原