不完全信息下的期权定价

本文讨论了不完全信息下的期权定价问题,采用风险中性估值原理对期权进行定价,将不完全信息引入到模型中去,构造投资者不完全信息集,利用马尔科夫性质、条件期望性质、Fubini定理等结论,给出了在不完全信息集下的欧式期权定价公式。     

银行间市场中期票据信用利差的影响因素研究

基于Longstaff和Schwartz的公司债定价模型,从信用风险度量的角度对我国中期票据信用利差的影响因素进行实证分析,回归结果表明：股票市场波动率、无风险利率、利率期限结构的斜率等结构化模型变量对中期票据信用利差产生显著的影响,但基于低频数据的流动性衡量指标在模型中不显著。引入宏观、发行主体和债券构成要素等因子后的回归结果表明：产出指标、债券评级与信用利差负相关;债券新增供给量、久期及发行主体的权益乘数与中期票据的信用利差正相关。     

违约互换定价与商业银行信用风险管理

在假设市场风险和信用风险两者线性相关的前提下建立一个基于考克斯过程的信用违约互换的定价模型,为信用违约互换定价分析提供理论依据。     

适用于中国的信用风险缓释工具定价模型

本文提出了一个全新的基于中国市场构造的信用风险缓释工具（Credit Risk Mitigation, CRM）定价模型。与传统信用风险定价模型相比,此模型在以下两个方面做出了重要改进：将CRM购买方的信用违约风险考虑在内;在回复比率历史数据缺失的情况下,利用标的债券的交易数据及标的主体的财务信息,通过Monte Carlo模拟,估计得到标的债务的隐含回复比率。基于此模型,我们进行了情景分析和参数敏感性检验,验证了该模型在理论和实证上的可靠性。     

基于Stackelberg博弈的供应链信用风险分析

讨论了由一个供应商和一个销售商组成的简单供应链,在销售商还款不确定的前提下,基于供应商和销售商订货过程是Stackelberg博弈,给出了他们的最优定价和订货策略,并说明了通过合理定价,供应商可以有效地防范信用风险;然后分析了销售商违约对供销双方及供应链的影响,并通过算例进行了说明。     

不完全市场下的期权定价理论综述

由于B-S定价公式是在完全市场条件假设下推导出来的,这与现实存在很大的出路,因此后来的学者就针对市场条件状况,研究了不同市场条件下的期权定价,其中以不完全市场条件下的期权定价为主,这显然与事实更加吻合。不完全市场主要可以分为带交易费用的期权市场、存有违约风险的期权市场以及信息不完全的期权市场。文章在此基础上,分析总结了在这个市场假设条件下的研究现状,并给出了未来值得深入研究的方向:主要是进一步放松B-S定价模型的假设条件,引入更多的现实因素,深入研究不同市场状况下的期权定价问题。     

基于动态博弈的易逝性产品收入管理问题

本文通过易逝性产品及其市场特性的分析,不仅指出它符合使用收入管理方法的条件,还将动态博弈思想引入其定价销售过程.文章运用最优控制理论和动态博弈理论,探讨易逝品销售商在收入管理过程中根据对消费者效用函数的判断来进行动态定价的方法,并用逆推归纳法分析了完全信息动态博弈情况下的子博弈精炼纳什均衡.最后,对于不完全信息动态博弈情况下的诱导策略也进行了分类建模,得出了有益的结论.     

VaR方法在商业银行信用风险管理的应用

国外基于量化管理模型的信用风险管理模式是以成熟的金融市场和完善的信用制度为基础的,由于我国金融市场还不完善、股价信息和债券市场也还不能完全作为商业银行信用风险量化管理的依据,加上全社会制度和信用文化还不完善,主观因素和外部环境对公司信用资产的损失也有较大的影响,因此,结合我国当前实际情况,构建VaR方法在我国商业银行信用风险管理中运用的条件,     

KMV模型在农业上市公司信用风险度量中的应用

我国商业银行面临的主要风险是信用风险,信用风险管理成为银行关注的重中之重。本文以农业类上市公司为样本,运用Matlab技术,通过实证来检验模型识别上市公司信用风险的能力,研究表明现阶段基于期权定价理论的KMV模型能很好地度量我国农业类上市公司的信用风险。     

RAROC和VAR在贷款定价中的综合应用

建立科学的贷款定价机制,对于商业银行的经营具有重大意义。本文是基于VAR技术的RAROC模型为贷款定价。在RAROC模型框架下,用基于Gaussian模型的VAR来测算贷款的经济资本,并应用于RAROC模型。用两模型的相结合,有效综合度量信用风险与市场风险,来为银行贷款合理定价。     

我国汽车金融信用风险博弈分析

在汽车金融市场中,汽车金融公司和客户之间的交易行动可以看成是一种博弈,通过运用博弈的分析理论,对我国汽车金融市场中的信用主体即汽车金融公司和客户的行为及其市场均衡进行了研究分析,个人信用意识淡薄、个人征信体系、信用评估体系和信用法治体系不完善、汽车金融公司自身的信用风险管理体系落后,是我国汽车金融信用风险加剧的根本原因。如何降低汽车金融的信用风险,已经成为我国汽车金融公司尚待解决的问题。从动态博弈的分析入手,并针对性采取一系列对策建议,才能使我国汽车金融业顺利发展。     

Credit risk and asymmetric information: A simplified approach

We present a simple model for risky, corporate debt. Debtholders and equityholders have incomplete information about the financial state of the debt issuing company. Information is incomplete because it is delayed for all agents, and it is asymmetrically distributed between debtholders and equityholders. We solve for the equityholders' optimal default policy and for the credit spreads required by debtholders. Delayed information accelerates the equityholders' optimal decision to default. Interestingly, this effect is small, implying only a small impact on credit spreads. Asymmetric information, however, has a major impact on credit spreads. Our model predicts high credit spreads for short-term debt, as observed empirically in credit markets.     

Bagging方法在基于日常交易数据解决中小企业的信用评价问题中的应用

关于信用风险评价问题至今已经做了很多研究,各种信用评价模型与方法也已被开发。但是这些模型与方法几乎都是基于财务数据、股票价格或风险调研机构发表的各种调查结果。因为几乎所有的中小企业的财务数据都是非公开的,至今开发的信用评价模型与方法都不免成为无米之炊。为此,本文提出了一个新的途径,只需要根据销售额、顾客付款额、拖欠款额等日常业务处理数据来评价顾客企业的信用度。本文提出一个应用Sagging方法评价顾客信用的系统,其目的在于解决由于异常顾客数比正常顾客要少很多而带来的问题,提高分辨异常顾客的能力。本文所提出的信用评价系统将应用到一个实际企业的信用评价问题中,借此来验证系统的性能和效果。     

Default-risky bond prices with jumps,liquidity risk and incomplete information

This paper provides a default-risky bond valuation model, which assumes that the issuer’s credit quality, modelled by the intensity of default, is driven by a continuous-time Markov chain. The model accounts for default and liquidity risk as well as incomplete information. A full-information semimartingale representation of a liquid defaultable bond price, which separates three different types of risks—default, interest-rate and credit-quality, is obtained. The illiquidity is modelled as exogenously specified stochastic reduction in the price of the bond, which adds more risks for the investors. A model of a market with partially informed investors, belonging to specific investor classes and having access to discrete information sets about credit quality, was specified. Valuations of defaultable bonds in this market were provided as well as price impacts of the new information releases.      

Homogeneous semi-Markov reliability models for credit risk management*

Abstract The credit risk problem is one of the most important issues of modern financial mathematics. Fundamentally it consists in computing the default probability of a company going into debt. The problem can be studied by means of Markov transition models. The generalization of the transition models by means of homogeneous semi-Markov models is presented in this paper. The idea is to consider the credit risk problem as a reliability problem. In a semi-Markov environment it is possible to consider transition probabilities that change as a function of waiting time inside a state. The paper also shows how to apply semi-Markov reliability models in a credit risk environment. In the last section an example of the model is provided. Mathematics Subject Classification (2000): 60K15, 60K20, 90B25, 91B28 Journal of Economic Literature Classification: G21, G33     

Trading constraints penalizing default: A recursive approach

This paper proves existence of an ergodic Markov equilibrium for a class of general equilibrium economies with infinite horizon, incomplete markets, and default. Agents may choose to deny their liabilities and face trading constraints that depend on the adjusted amount of past default on each asset. These constraints replace the usual utility penalties and explore intertemporal tie-ins that appear in dynamic economies. The equilibrium prices and solvency rates present stationary properties that are usually required in econometric models of credit risk.     

商业银行信用风险管理方法研究

本文通过分析目前国内商业银行在信用风险管理措施方面的误区和不足,给出了在信用风险全面识别基础上进行信用风险管理的基本框架,并对当今主流的信用风险模型进行了系统性的介绍,旨在为我国商业银行提高信用风险管理水平提供借鉴和研究思路。     

Spatial dependence in microfinance credit default

Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.     

Transition matrix models of consumer credit ratings

Although the corporate credit risk literature includes many studies modelling the change in the credit risk of corporate bonds over time, there has been far less analysis of the credit risk for portfolios of consumer loans. However, behavioural scores, which are calculated on a monthly basis by most consumer lenders, are the analogues of ratings in corporate credit risk. Motivated by studies of corporate credit risk, we develop a Markov chain model based on behavioural scores for establishing the credit risk of portfolios of consumer loans. Although such models have been used by lenders to develop models for the Basel Accord, nothing has been published in the literature on them. The model which we suggest differs in many respects from the corporate credit ones based on Markov chains — such as the need for a second order Markov chain, the inclusion of economic variables and the age of the loan. The model is applied using data on a credit card portfolio from a major UK bank.     

A nonparametric decision approach for entrepreneurship

Credit identification is one of core issues of financing process. Enterprise credit involves a lot of financial and non-financial measures, among which entrepreneurship is an important but rarely mentioned variable. Good entrepreneur credit often leads to good enterprise credit. A comprehensive analysis of enterprise credit identification is important to avoid losses, foster excellent enterprise and make the optimal allocation of resources. The existing literature mainly studied the impact of entrepreneurship on enterprise credit from the perspective of historical information, which is about average and tendency. Hence, those models were unable to explain the function of complex human nature and, consequently, linear models are unable to well describe the relationship between enterprise credit and entrepreneur credit. Given the deficiency of parametric models when discussing the impact of entrepreneur credit, a non parametric approach are proposed to individually describe the impact path of different individuals. This paper established a decision tree based on nonparametric approach to verify the practicability of the model in the evaluation of enterprise credit recognition. In the end of this paper, we demonstrate the validity of the non parametric model and the validation method of it.