基于非齐次Poisson过程的违约互换的一个定价模型

在一个亚滤波概率空间中,基于HJM模型的研究框架,违约时间服从非奇次Poisson过程,文章给出了可违约债券和信用违约互换期权的价格。     

在约化模型下具有随机波动率的可违约债券定价

在约化模型下,假设债券的违约强度服从扩散过程且其波动率变量服从CIR模型,文章在利率分别为常数和随机情形下得到了可违约债券价格的显示表达式.然后通过数值分析,讨论模型中的参数对信用利差的影响.     

随机波动率下信用风险定价模型的比较分析

文章分析了在Morten模型、跳扩散模型和首次时间通过模型中的随机波动率下可违约债券的定价问题。探讨了随机波动率下可违约债券定价机制,利用特征函数及其逆变换的方法得到了随机波动率下可违约债券的定价公式,并通过数值模拟分析了违约概率和信用价差的期限结构。     

公司债违约风险补偿研究

文章从公司债利差入手,探讨了公司违约的风险补偿问题.研究认为:(1)公司债利差中的信用溢价部分是由不可预测的跳跃性违约及市场违约传染所致,它具有系统性风险特征,无法分散;(2)用即期利率来测度和估算公司债利差,可以有效地避免用到期收益率计算利差的三个缺陷;(3)在没有公司累计违约率历史数据的条件下,可以用卡尔曼滤波下的拟最大似然法(QML)来估算跳跃性违约风险补偿.     

基于混频数据的实体经济与金融市场时变溢出效应研究

本文基于混频VAR模型分析了中国实体经济与股票市场、债券市场之间的时变溢出效应。结果显示,实体经济与股票市场、债券市场之间收益率与波动率的溢出效应呈现显著的时变特征,溢出效应在金融危机期间呈现快速上升趋势,而后呈现下降态势,且易受到极端事件的影响;在大部分考察时期内存在股票市场向实体经济的波动率溢出效应,而债券市场相对于实体经济则从考察初期波动率溢出效应的净输入方转化为中后期的波动率溢出效应的净输出方。进一步分析收益率与波动率总溢出指数的影响因素,结果发现,极端事件、宏观经济代理变量和期限利差对于溢出效应具有正向影响,泰德利差对波动率总溢出指数具有负向影响,而投资者情绪指数对收益率总溢出指数具有负向影响。     

货币政策冲击视角下债券利差对经济增长的影响研究

债券是资本市场支持实体经济的重要工具，债券收益率曲线也隐含了宏观经济运行信息和货币政策意图。为了准确识别债券利差通过货币政策传导机制对经济增长的影响效果，以国债日交易数据刻画债券期限利差、以企业债券日交易数据构建债券信用利差，并选取货币政策、信贷规模、债券利差和经济增长共4类经济变量构建TVP-VAR模型，研究货币政策冲击下债券信用利差、期限利差与经济增长的动态时变关系。结果表明：信用利差和期限利差对于货币政策都存在潜在的传导机制，在短期均可作为研判经济波动的领先指标；但是由于利率作为传导中介的时滞性，信用利差对于通货膨胀的跟踪程度较弱。因此，提出相关政策建议：一是充分挖掘信用利差信息，加强货币政策对经济的预调微调；二是进一步完善债券市场自身建设，提升市场信息映射经济预期的准确性；三是加快信用体系基础设施建设，为经济发展和市场建设提供基础支撑。     

交易对手违约风险的信用违约互换定价

本文在约化模型的框架内考虑了含交易对于违约风险的信用违约互换的定价。在参照资产违约强度与利率相关、信用保护买卖双方违约强度受到参照资产违约影响的情形下,给出了信用违约互换的定价公式。     

基于马尔可夫转换模型的违约风险溢价预测研究

针对违约风险溢价变化依赖于经济波动状态以及市场、宏观经济变量依赖于经济周期时变因素的阶段,基于马尔可夫转换阶段的具体特征,构建马尔可夫违约风险溢价预测转换模型,并以香港恒生指数信用违约互换波动为例,测算因时变系数波动的指数息差、宏观经济变量等概率,通过实证算例剖析股市、宏观经济变量与违约风险溢价之间的内在联动关系和信用违约风险溢价变化的转换机制,以期实现对违约风险溢价能够进行有效预测,实证仿真结果说明了模型的有效性。     

信息挖掘还是噪声交易:债券特质风险如何影响信用利差?

特质风险向债券市场传递风险的方式,直接关系到债券定价逻辑和系统性金融风险防范。本文选取交易所公司债数据,从投资者信息挖掘行为和非理性交易行为出发,研究债券特质风险对信用利差的传导效应与传导机制,并从违约视角探索特质风险产生传导效应的原因,同时分析投资者对不同类型债券所做反应的异质性。研究结论表明:特质风险会通过信息挖掘机制和噪声交易机制影响 信用利差,且以噪声交易机制为主;违约事件引致了更多噪声交易,是特质风险产生传导效应的重要环境因素;发行人的股票上市或国企背景降低了投资者面临的信息不对称程度,并有效抑制了噪声交易机 制的作用,而债券的低评级或短期限特征则会引发投资者的抛售行为,进而放大了噪声交易机制的作用。     

运用VaR值度量信用风险模型的比较研究

一、导言 目前在国际银行界流行的用VaR值度量信用风险的模型主要有:Credit-Metrics模型、KMV模型、麦肯锡的CreditPortfolioView模型和CreditRisk+模型.CreditMetrics模型的特点在于它完全基于信用转移分析,即在既定的时间内(一般为一年)一种信用变为另一种信用质量的概率,用它来度量将来比如一年以后贷款资产组合的价值分布,模型强调资产组合价值变化只与信用转移相关,利率以确定好的轨迹运动.KMV模型稍微有别于CreditMetrics模型,它基于个体的预期违约频率,而不是评定机构提供的每个信用级别历史平均的变化频率.以上两种模型都以莫顿的期权定价模型为基础,区别只在于处于操作便利考虑而设定了不同的假设条件.CreditPortfolioView模型仅仅是度量了违约风险.它通过构造离散的多期模型,把违约概率看作宏观变量的函数.Cred-itRisk+模型则是假设贷款违约服从泊松分布,通过随机违约概率将信用转移风险部分涵盖在内.这四种模型的主要区别就在于它们运用了不同的方法来测算信用违约概率,而对资产组合价值分布和资产损失分布大都采用了蒙特卡罗模拟法来计算.下面从如何测算信用违约风险的角度对四种模型加以描述和比较.     

Derivative Pricing With Wishart Multivariate Stochastic Volatility

This paper deals with the pricing of derivatives written on several underlying assets or factors satisfying a multivariate model with Wishart stochastic volatility matrix. This multivariate stochastic volatility model leads to a closed-form solution for the conditional Laplace transform, and quasi-explicit solutions for derivative prices written on more than one asset or underlying factor. Two examples are presented: (i) a multiasset extension of the stochastic volatility model introduced by Heston (1993), and (ii) a model for credit risk analysis that extends the model of Merton (1974) to a framework with stochastic firm liability, stochastic volatility, and several firms. A bivariate version of the stochastic volatility model is estimated using stock prices and moment conditions derived from the joint unconditional Laplace transform of the stock returns.     

基于贝叶斯方法的信用评级模型构建与违约概率估计

以贝叶斯方法为基础构建了信用评级和违约概率模型,指出金融机构利用已有评级信息提高债务人信用风险评估准确性的途径,并以单个债务人违约概率度量方法和Merton理论为基础,考虑异质性导致的宏观经济冲击对债务人的不同影响,度量资产组合违约风险。利用相关数据对贝叶斯模型应用给出例证,结果表明贝叶斯方法具有更为灵活的框架和较好的预测能力。     

Liquidity spreads in the corporate bondmarket: Estimation using a semi-parametric model

This study utilizes the liquidity risk associated with Treasury bonds to directly determine the degree to which liquidity spreads account for corporate bond spreads. This enhances understanding of their relative contributions to the yield spreads of corporate bonds. To capture time variation on instantaneous spreads and volatility and to reduce modeling bias, semi-parametric techniques are applied to estimate the time-varying intensity process. Empirical results indicate that our semi-parametric model is good at capturing the time variation in default and liquidity intensity processes. The credit spreads are due to default risk and reflect the relative liquidity of the corporate bond market, indicating that liquidity risk plays an important role in corporate bond valuation.     

Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT

Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.     

Credit risk modeling based on survival analysis with immunes

Statistical modeling of credit risk for retail clients is considered. Due to the lack of detailed updated information about the counterparty, traditional approaches such as Merton’s firm-value model, are not applicable. Moreover, the credit default data for retail clients typically exhibit a very small percentage of default rates. This motivates a statistical model based on survival analysis under extreme censoring for the time-to-default variable. The model incorporates the stochastic nature of default and is based on incomplete information. Consistency and asymptotic normality of maximum likelihood estimates of the parameters characterizing the time-to-default distribution are derived. A criterion for constructing confidence ellipsoids for the parameters is obtained from the asymptotic results. An extended model with explanatory variables is also discussed. The results are illustrated by a data example with 670 mortgages.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

Continuous Time Wishart Process for Stochastic Risk

Risks are usually represented and measured by volatility–covolatility matrices. Wishart processes are models for a dynamic analysis of multivariate risk and describe the evolution of stochastic volatility–covolatility matrices, constrained to be symmetric positive definite. The autoregressive Wishart process (WAR) is the multivariate extension of the Cox, Ingersoll, Ross (CIR) process introduced for scalar stochastic volatility. As a CIR process it allows for closed-form solutions for a number of financial problems, such as term structure of T-bonds and corporate bonds, derivative pricing in a multivariate stochastic volatility model, and the structural model for credit risk. Moreover, the Wishart dynamics are very flexible and are serious competitors for less structural multivariate ARCH models.     

市场分割条件下KMV模型的改进与实证分析

为克服传统KMV模型只能应用于单一市场的困难,将多市场的股权价值、股权价值波动的相关性、汇率等因素纳入考虑,建立市场分割条件下的KMV模型。选取了24家A＋H上市公司对所建模型进行了实证分析,结果表明,模型对不同公司的违约距离有较好的区分能力。     

Particle Learning for Fat-Tailed Distributions

It is well known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this article, we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a t_ν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/U.S. dollar daily exchange rate data and on data from the 2008–2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.