A model for dependent default with hyperbolic attenuation effect and valuation of credit default swap

A hyperbolic function is introduced to reflect the attenuation effect of one firm＇s default to its partner. If two firms are competitors （copartners）, the default intensity of one firm will decrease （increase） abruptly when the other firm defaults. As time goes on, the impact will decrease gradually until extinct. In this model, the joint distribution and marginal distributions of default times are derived by employing the change of measure, and the fair swap premium of a credit default swap （CDS） can be valued.

Valuing Credit Default Swap under a double exponential jump diffusion model

This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.

Risk-Neutral Dynamic of Forward-Looking Default Probabilities and Recovery Rates： Evidence From Credit Default Swap Prices of DOW30 Companies

In modem financial markets, the credit default swap （CDS） market has supplanted the bond market as the industry gauge for a borrower＇s credit quality. Therefore, it is very important to value CDS accurately by getting closer to more realistic pricing models. So far there have been no models for extracting forward-looking credit information to value CDS. In current practice, historical data is used in a credit default swap pricing model. One of the reasons was the difficulty when the market for credit derivatives was small, to extract forward-looking credit information such as recovery rates and default probabilities from traded securities. Since the CDS market has undergone rapid expansion in recent years, the possibilities of extracting forward-looking credit information have increased. Our work significantly extends Das and Hanouma （2009） where a flexible jump-to-default model was introduced to obtain implied recovery rates. We improve the flexible jump-to-default model where forecasted forward-looking hazard rates and recovery rates can be extracted using stock prices, stock volatilities and data from credit default markets to forecast CDS spreads. Instead of using exogenously assumed constant recovery rates and default probabilities from a credit rating agency, we use forward-looking hazard rates and recovery rates to price and forecast CDS spreads. We also compare out-of-sample market CDS spreads with our forecasted CDS spreads to check how well our model performs. Our model fit the market CDS spreads very well across all time to maturity CDS contracts except in some extreme cases when there is a big jump in CDS spreads.

Credit Default Swaps （CDSs） and Systemic Risks

The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.

Do consumer internet behaviours provide incremental information to predict credit default risk?

The peer-to-peer lending industry has experienced recent turmoil,posing risks to fintech companies and banks.Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to submitting loan applications to a wellknown fintech company,Du Xiaoman Financial(formerly Baidu Finance),this article evaluates the predictive power of borrowers’internet behaviours on credit default risk.After controlling for borrowers’basic characteristics that are widely used in academic research and enterprise practices,the coefficients of key factors selected from 3,100 variables are economically and statistically significant.The average Kolmogorov-Smirnov value of the prediction model calculated using the hold-out method is approximately 37.09%.The results remain robust in several additional analyses.This study indicates the importance of non-credit information,particularly borrowers’internet behaviours,in supplementing borrowers’credit records for both fintech companies and banks.

Pricing Credit Default Swap with Contagious Risk and Simulation

This paper mainly studies the pricing of credit default swap(CDS) with the loan as the reference asset,and gives a model based on the obtained conclusions. In the contract of CDS, we consider that the default of the protection's seller is correlated with the stochastic interest rate following Vasicek model and the default state of the reference firm. We give the pricing formula of CDS and analyze the effect of the contagious risk between the counterparties on the pricing of CDS.

Informed Trading, Heterogeneity Investment, Liquidity Shocks and the Valuation of Credit Default Swaps

This paper explores the effect of informed trading, heterogeneity investment and liquidity shocks on the valuation of credit default swaps(CDSs). Under the condition of asymmetric information, the informed trading plays an important role in the valuation of CDS. Instruction order flow has a significant influence on CDS price.And the scope of influence changes in accordance with different time interval, company status and the size of bid-ask spread. Heterogeneity of investors seriously affects the market liquidity and subsequently affects the CDS price. The bigger heterogeneity of the investment philosophy, investment habits, investment preference and so on is the bigger risk for market liquidity, and the higher price for CDS shall be. On the contrary, the conclusion is also consistent. The effectiveness of liquidity, whether it is before or after the financial crisis, dominates the fluctuation of CDS price. The premium of liquidity accounts for 36% to 50% of the CDS price.

Determining threshold default risk criterion for trade credit granting

To solve the problem of setting threshold default risk criterion to select retailer eligible for trade credit granting, a novel method of solving simultaneous equations is proposed. This method is based on the bilevel programming modeling of trade credit decisions as an interaction between supplier and retailer. First, the bilevel programming is set up where the supplier decides on credit terms at the top level considering a retailer's default risk, and the retailer determines the order quantity at the lower level in response to the credit terms offered. By solving this bilevel programming, the relationship between the optimal terms and the corresponding default risk can be derived. Second, set the extreme scenario where the threshold default risk is approached as the point causing a zero marginal profit to the supplier. Another equation describing this particular scenario can also be derived. Thus, a system of two equations with two unknown variables can be obtained where the exact threshold default risk criterion can be found by solving them. A numerical example is presented as an illustration of the method proposed. It shows that the threshold criterion can be uniquely determined when the financial costs, inventory costs, and the marketing parameters of supplier and buyer are specified.

Customer Segmentation of Credit Card Default by Self Organizing Map

In this paper we applied the technique of Self Organizing Map (SOM) to segment individuals based on their credit information. SOM is an unsupervised machine learning method that reduces data complexity and dimensionality while keeping sits original topology, which is superior to other dimension reduction methods especially when features in data have unclear nonlinear relations. Through this method we provide more clear and intuitive segmentation that other traditional methods cannot achieve.

Analysis on Risk Prevention Mechanism for Farmers' Default in Small Amount Credit

Through analysis, it is believed that major reasons for default risks in operation of small amount credit include low management level and vacancy of normative system, vacancy of risk sharing mechanism, rating distortion due to imperfect credit investigation system, and uncertainty of borrower's credit. On the basis of these, static and dynamic models are established to analyze the prevention mechanism for default risk in small amount credit. It is concluded that we must establish a restriction mechanism during operation of small amount credit as long as three values increase, namely, N (potential loss of bad credit record due to farmers' default), Q (probability of successful recovery by small amount credit institution), and S (cost of small amount credit institution punishing farmers after successful recovery). Finally, following countermeasures and suggestions are put forward: perfect laws and regulations and credit reward and punishment mechanism for risk management of small amount credit; bring into play proper function of loan officer in small amount credit practice; widely promote rural "Group Credit Union" system.

银行信用风险度量Credit Metrics^(TM)模型与CPV模型比较研究

根据新巴塞尔资本协议的基本原则及巴塞尔银行监管委员会推荐的确定资本金的VaR方法,结合中国商业银行目前信用风险管理的实际情况,比较分析CreditMet-ricsTM模型和CPV模型的基本原理和参数选择的共性及差异,对两模型各自特点做出客观评价,结果发现运用CPV模型有利于提高信用风险度量的精确性,为商业银行信用风险管理提供了有益的借鉴.

The Role of Comprehensive Information Sharing in Credit Market： Theoretical Analysis and New Evidence from the European Union

This paper investigates the effect of the comprehensive information sharing on aggregate credit volume and the default ratio. Firstly, we utilize a three-stage game model developed by Dell＇Ariccia and Marquez （2006） to illustrate that the comprehensive information sharing would change the credit resource allocation and produce negative ＂composition effects＂ stated by Jappelli and Pagano （2005）. Then we use European Union （EU） data to test these theoretical implications. We find that when the information sharing system develops to a relatively high level, comprehensive information sharing improvements, for both the width and depth, are associated with the rise in macro credit access but also the aggregate default risk. We further find that the macro-consequences of variations in information sharing just differ in OECD and non-OECD group in EU countries. For OECD countries, the negative ＂composition effects＂ mainly arise from the increase in the width of information sharing while these effects are correlated with both the rise in depth and width indicators for non-OECD countries.

Empirical Study on Credit Risk of Our Listed Company Based on KMV Model

KMV model is one of the most important credit risk evaluation models in the world. It uses B-S option pricing and Morton formula based on the market value and volatility of the company’s equity, debt maturities, risk-free interest rates and the book value of liabilities to estimate the market value of the company’s assets and the volatility of the asset value. In this paper, based on the theory of KMV model, we can derive the listed company’s default rate, and assess credit risk. And the result is reasonable.

我国社会信用体系建设面临的主要挑战及对策建议

党的十八大以来,国家高度重视社会信用体系建设,在一些基础领域和关键环节取得了重要进展,总体社会信用状况得到明显改善。然而,社会信用体系建设是一项长期且极为复杂的系统工程,目前仍面临着理论研究和法治建设滞后,社会信用体系建设的目标定位和功能边界不够清晰,商务征信体系不健全、企业账款拖欠严重,以及其他发展不平衡不充分等问题。建议在系统总结前期经验教训的基础上,加强理论研究,厘清认识和实践误区,进一步明确社会信用体系建设的目标定位和功能边界,加快推进信用立法,健全商务信用体系,破解企业账款拖欠难题,大力发展信用经济,并做好面向公众的宣传引导工作。

企业资源配置战略与债券信用评级

企业战略作为企业全局性、长期性资源配置的重要规划,对企业经营活动具有重要影响,也必然会影响企业的融资行为。本文从企业战略视角出发,以2008—2020年中国A股上市公司为研究对象,运用固定效应模型探究了企业资源配置战略对债券信用评级的影响。研究发现:相对于经营主导型企业,投资主导型企业的债券信用评级更低,并且该结论在处理内生性问题和进行一系列稳健性检验后依然成立。异质性分析发现,企业资源配置战略对债券信用评级的影响在非国有企业和分析师跟踪人数多的企业中更明显。中介效应检验结果表明,企业资源配置战略通过作用于企业违约风险而影响债券信用评级。本文的研究结论对发债企业、信用评级机构和债券市场投资者具有重要启示意义。

地区失信环境与企业现金持有——来自失信被执行人的经验证据

构建相互信任的营商环境是经济高质量发展的基础。基于最高人民法院公布的失信被执行法人数据,实证检验地区失信环境对企业现金持有水平的影响并揭示其主要作用机制。研究发现:企业所在地区的失信被执行人越多,其现金持有水平越高;机制检验发现,失信环境能够加剧企业融资约束程度、增加费用支出和降低经营绩效,内外部资金来源受限以及增加的费用支出迫使企业出于预防性动机持有更高水平的现金;进一步检验发现,当企业的会计信息质量较差、规模较小时,失信环境中的企业需持有更多的现金以应对潜在风险。这些结论从现金持有的角度丰富了对失信环境经济后果的研究,为持续优化营商环境和完善社会信用体系提供了借鉴思路。

The Macroeconomic Impact of Internet Finance

This paper investigates the macroeconomic impacts of Internet finance,highlighting its advantages and challenges.Internet finance,a fusion of Internet technology with traditional financial practices,introduces innovative models for global asset management,capital financing,payments,investments,and intermediary services.While it enhances the accessibility and efficiency of financial services,it also introduces new risks,such as higher credit default rates.This study explores how Internet finance contributes to financial inclusivity and macroeconomic growth yet poses potential threats to traditional financial stability.The dual aspects of Internet finance are analyzed:its application in existing processes and its capacity to generate novel business models.Furthermore,the paper proposes strategic responses to mitigate the negative impacts of Internet finance,mainly focusing on risk management and regulatory improvements to safeguard economic stability.

具有CDS的动态多层网络银行系统性风险研究

美国次贷危机表明CDS对银行系统性风险有很大影响,但CDS如何影响银行系统性风险的机理还未明。因此,构建了一个具有CDS交互作用的动态多层银行网络模型,研究两种经济环境下的CDS对银行系统的双重影响。研究结果表明:经济平稳时期,CDS有风险吸收作用,降低银行系统性风险;经济波动时期,银行因CDS释放的超额风险资产会转变为新的系统性风险;CDS的规模与银行系统性风险呈负相关关系,且规模存在临界值。

城商行参与、地方政府救助与城投债违约风险化解

地方政府的支持对于降低城投债违约风险具有重要意义。基于2015—2021年城投债地级市数据,采用OLS估计和工具变量法,分析城商行资源对城投债信用利差的影响。研究表明,地区金融资源和地方政府可支配金融资源能够显著降低当地城投债违约风险。异质性分析表明,城商行对城投债违约风险的降低效应仅在经济较发达和城商行资产富裕的地区存在。机制分析表明,第一例城投债的实质性违约强化了城商行资源对信用利差的降低效应,金融资源结构的“本地化”倾向对信用利差呈“倒U型”状态,城商行资源在降低城投风险方面具有财政金融二重属性。基于此,建议重视地区城商行建设,提升地方政府紧急救助能力,防止出现隐性债务“由点到面”的违约风险扩大化。

基于Transformer编码器和残差网络的信贷违约预测模型

针对传统信贷违约预测模型对高维稀疏类别特征缺乏有效处理,性能受到人工特征工程影响较大的问题,提出一种基于Transformer编码器和残差网络的信贷违约预测模型(TE-ResNet)。该模型首先利用嵌入层对类别特征进行处理,将它们转化为低维度的稠密向量;然后将连续特征和嵌入后的类别特征连接,输入到堆叠的Transformer编码器中进行特征提取,捕捉输入特征之间的关系,得到有用信息的高层特征表示;最后使用结合了通道注意力机制的一维残差网络模型进行违约预测。在训练过程中,模型采用加权交叉熵损失函数,以解决信贷数据不平衡的问题。实验结果表明,与8种主流基准模型的最佳表现相比,TE-ResNet在LendingClub数据集、天池贷款数据集上的各项指标均有提升:AUC指标分别提升了0.58%和2.85%,F1-Score指标分别提升了0.85%和11.92%,G-mean指标分别提升了2.94%和16.19%。TE-ResNet能够提高信贷违约预测的性能,减少人工特征工程,实现端到端的学习。因此,TE-ResNet模型具有实际应用的潜力,并可为信贷业务提供更加精确和可靠的风险评估服务。