信用社担保贷款中的风险防范

着重探讨信用社风险必须从源头上警惕和防堵的问题。     

Quality control for structural credit risk models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential — hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon–Nikodym derivative for a change of measure.     

The pricing of credit risk derivatives

This paper considers the pricing of derivatives that protect holders of corporate bonds from a reduction in their value because of a deterioration in their credit quality. These derivatives are structured as either puts on the bond price or calls on the bond spread (above the risk free rate) in the context of models developed by Merton (1974) and Black and Cox (1976). The pricing properties of these options are derived using both analytical and numerical methods.     

Sequential optimization three-way decision model with information gain for credit default risk evaluation

To categorize credit applications into defaulters or non-defaulters, most credit evaluation models have employed binary classification methods based on default probabilities. However, while some loan applications can be directly accepted or rejected, there are others on which immediate accurate credit status decisions cannot be made using existing information. To resolve these issues, this study developed an optimized sequential three-way decision model. First, an information gain objective function was built for the three-way decision, after which a genetic algorithm (GA) was applied to determine the optimal decision thresholds. Then, appropriate accept or reject decisions for some applicants were made using basic credit information, with the remaining applicants, whose credit status was difficult to determine, being divided into a boundary region (BND). Supplementary information was then added to reevaluate the credit applicants in the BND, and a sequential optimization process was employed to ensure more accurate predictions. Therefore, the model’s predictive abilities were improved and the information acquisition costs controlled. The empirical results demonstrated that the proposed model was able to outperform other benchmarking credit models based on performance indicators.     

Interest rate risk propagation: Evidence from the credit crunch

During the 2007–2009 financial crisis, US subprime mortgage risk exposures led to severe liquidity problems in several other foreign markets. Such risk contagion was caused by enormous changes in interest rates. Although risk contagion has been investigated by several literatures, the magnitude of propagated interest rate risk around global financial markets remains unexplored. Therefore, this study quantifies the degree to which the increased credit risk within the US financial system propagated to the European markets’ liquidity risks. Specifically, using a conditional value-at-risk (CoVaR) model, we quantitatively measure interest rate risk of a European country, by looking at the upside risk in distribution of changes in interest rate. And such propagation risk measure considers additional value-at-risk conditional on the interest rate movements in the US. The results show significantly positive differences between European country's value-at-risk conditional on the US financial markets being in a normal or distressed state. This propagating effect increased from 2007, and was particularly pronounced in the 2008–2009. In addition, the interest rate risk contagion is especially severe for some countries in the Euro regions with greater sovereign debt problems. Hence our result foretells the deterioration of the European sovereign debt crisis which started to unfold in 2010. Our work supplements the literature by successfully quantifying the magnitude of additional interest rate risk conditional on risk exposure from external sectors.     

A coupled Markov chain approach to credit risk modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.     

High-frequency credit spread information and macroeconomic forecast revision

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.     

Write-down bonds,credit risk and imperfect information

A structural model of pricing Write-Down (hereafter WD) bonds under imperfect information has been developed to investigate the effect of WD bonds issuance on credit risk. Information is not only delayed but also asymmetrically distributed between managers and outside investors. We derive analytical solutions for corporate securities prices and find the issuance of WD bonds could significantly improve firm value via reducing bankruptcy cost. Our numerical results further demonstrate that the WD bonds issuance increases corporate risk tolerance and reduces the risk of bankruptcy and credit spreads under imperfect information.     

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.     

Improved hybrid resampling and ensemble model for imbalance learning and credit evaluation

A clustering-based undersampling (CUS) and distance-based near-miss method are widely used in current imbalanced learning algorithms, but this method has certain drawbacks. In particular, the CUS does not consider the influence of the distance factor on the majority of instances, and the near-miss method omits the inter-class(es) within the majority of samples. To overcome these drawbacks, this study proposes an undersampling method combining distance measurement and majority class clustering. Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model (CDEILM). This algorithm combines distance-based undersampling, feature selection, and ensemble learning. In addition, a cluster size-based resampling (CSBR) method is proposed for preserving the original distribution of the majority class, and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods. The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework. The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods, and that the hybrid model provides the best results under most circumstances. Therefore, the proposed model can be used as an alternative imbalanced learning method under specific circumstances, e.g., for providing a solution to credit evaluation problems in financial applications.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.     

基于因子分析和聚类分析的创业板上市公司综合评价

本文运用多元统计的方法,对中国创业板上市公司的37组有效样本数据进行经营业绩的分类研究和综合考评。首先进行因子分析,从12个指标中提取出反映四方面能力的四个公共因子;然后对样本公司进行分层聚类分析,利用SPSS分析软件聚合成为5类,分析各类公司在各个因子上的平均得分,找到其优势因子和劣势因子,在此基础上进行综合评价,并从公司管理层和投资者的角度分别给出政策建议。     

Estimating the structural credit risk model when equity prices are contaminated by trading noises

The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan [Duan, J.-C., 1994. Maximum likelihood estimation using price data of the derivative contract. Mathematical Finance 4, 155–167] is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devise a particle filtering algorithm that is practical for conducting the MLE estimation of the structural credit risk model of Merton [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470]. We implement the method on the Dow Jones 30 firms and on 100 randomly selected firms, and find that ignoring trading noises can lead to significantly over-estimating the firm’s asset volatility. The estimated magnitude of trading noise is in line with the direction that a firm’s liquidity will predict based on three common liquidity proxies. A simulation study is then conducted to ascertain the performance of the estimation method.     

Asymmetric determinants of corporate bond credit spreads in China: Evidence from a nonlinear ARDL model

This study utilizes the nonlinear ARDL (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014) to quantify the potentially asymmetric transmission of positive and negative changes in each of the possible determinants of industry-level corporate bond credit spreads in China. The determinants we consider include the corresponding industry stock price, China’s stock market volatility, the level and slope of the yield curve (i.e., the interest rate), the industrial production growth rate, and the inflation rate. The empirical results suggest substantial asymmetric effects of these determinants on credit spreads, with the positive changes in the determinants showing larger impacts than the negative changes for most industries we consider. Moreover, the corresponding industry stock prices, the interest rate, and the industrial production growth rate negatively drive the industry credit spreads for many industries. In turn, China’s stock market volatility and the inflation rate positively affect the credit spreads at each industry level. These findings may be helpful to investors, bond issuers and policymakers in understanding the dynamics of credit risks and corporate bond rates at the industry level.     

Supply chain finance: From traditional to supply chain credit rating

Traditional credit rating models, adopted by financial institutions to assess the credit risk of a company, adopt a purely financial perspective, and often fail to properly assess small and medium enterprises. On the other hand, buyers usually assess suppliers by means of comprehensive vendor ratings, considering a broad range of operational performance. This paper investigates whether financial and vendor ratings can be integrated into a supply chain credit rating model that jointly considers financial indicators of the supplier and its operational evaluation provided by buyers; the paper also investigates the benefits and the challenges of such a model for all the stakeholders involved (buyers, suppliers, financial institutions, and technology providers), adopting the lenses of the stakeholder theory. We adopted both multiple case studies and an iterative focus group, involving representatives from suppliers, buyers, financial institutions, and technology providers. The results confirm the potential value of such an integrated rating, mainly for strategic suppliers, showing the expected benefits for all stakeholders and highlighting the potential challenges to face.     

An empirical study on credit card loan delinquency

Following the Basel II convention, consumer credit default is commonly defined as delinquency beyond a period of 90 days. In this study, rather than considering default as a binary variable, we dissect delinquency states further to investigate default behavior in greater detail. As such, we define three states—no delinquency, delinquency and serious delinquency—and estimate the probabilities of the transitions between states using extensive panel data from Korea, covering a wide range of behavioral information. Our findings have several economic implications. First, the factors that affect delinquency risk can differ from those that affect the transition from delinquency to serious delinquency. Second, the recent increase in the number of seriously delinquent accounts can be attributed to changes in the borrower age distribution. Third, macroeconomic conditions, especially differences in gross domestic product and consumption growth, have led to the recent increase in delinquent accounts. Fourth, the debt-to-income (DTI) ratio has a profound effect on transitions between delinquency states and thus affects both recovery and delinquency. Furthermore, this result is robust to controls for demographic and macroeconomic factors.     

Default risk and private student loans: Implications for higher education policies

In recent years, the proportion of students facing a binding constraint on government student loans has grown. This has led to substantially increased use of private loans as a supplementary source of finance for households׳ higher education investment. A critical aspect of the private market for student loans is that loan terms must reflect students׳ risk of default. College investment will therefore differ from a world in which government student loans, whose terms are not sensitive to credit risk, are expanded to no longer bind. Moreover, beyond simply crowding out private lending, expansions of the government student loan program will feed back into default risk on private loans. The goal of this paper is to provide a quantitative assessment of the likely effects of the private market for student loans on college enrollment. We build a model of college investment that reflects uninsured idiosyncratic risk and a well-defined life-cycle that is consistent with observed borrowing and default behavior across family income and college preparedness. We find that higher government borrowing limits increase college investment but lead to more default in the private market for student loans, while tuition subsides increase college investment and reduce default rates in the private market. Consequently, higher limits on government student loans have small negative welfare effects, while tuition subsidies increase aggregate welfare.     

Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.     

Current account and credit growth: The role of household credit and financial depth

Understanding the impact of financial variables on the current account balance is one of the priorities of academic literature and policymakers. Evidence from a broad panel of advanced and emerging countries shows that an increase in credit growth is associated with a significant deterioration in the current account balance. When we examine the roles of the components of credit, we find that an increase in household credit causes a significant decline in the current account balance, whereas an increase in business loans has no significant effect. Therefore, our findings indicate that the significant negative impact of credit growth on the current account balance is driven by household credit. Furthermore, we show that total and household credit growth rates have a stronger negative effect on the current account balance for lower levels of financial depth. Our results suggest that targeted policy measures that curb household credit growth might be more effective to reduce external imbalances particularly at the early stages of financial deepening.