The Role of Financial,Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction

We assess the use of bank loan information in predicting the timing to default. We use unique data on defaults in small and medium enterprises maintained by the Central Bank of Portugal which includes financial accounting and macroeconomic indicators, as well as non-financial information. The findings are indicative of the incremental predictive ability of non-financial information over and above macroeconomic and financial accounting information in the baseline, industry, and in- and out-of-sample models. Specifically, total credit secured by firms is, as expected, negatively and significantly related to default. Gross domestic product is negatively and significantly related to default, and benchmark market rate is positively and significantly associated with default. The findings also reveal that firms which are operated by partners, which have stronger financial support from partners, and which possess operational assets exhibit lower hazards of default. The study indicates that non-financial information and macroeconomic indicators assessed alongside financial accounting data can significantly improve the forecasting performance of default models.     

Forecasting and explaining aggregate consumer credit delinquency behaviour

We model aggregate delinquency behaviour for consumer credit (including credit card loans and other consumer loans) and for residential real estate loans using data up until 2008. We test for cointegrating relationships and then estimate short run error correction models. We find evidence to support the portfolio explanations of declines in credit quality for consumer and for real estate loans, but support for the reduced stigma explanation was restricted to real estate loans. Evidence supportive of household-level explanations of irrational borrowing and unexpected net income shocks was found for consumer and real estate loans, but evidence of strategic default was restricted to the volume of consumer loans and real estate loans, and not for credit cards. We also found that the error correction model gave forecasts of the volume of delinquent consumer debt which were of an accuracy comparable to that of an ARIMA model.     

Probability of default in collateralized credit operations

The goal of this paper is to identify the major determinants of the probability of default in a mortgage credit operation, which is backed by collateral. We use an exclusive data set with 268,036 loan contracts and apply logistic regression and Cox proportional hazards model in the estimation. The discriminatory power of the estimated models is analyzed by several accuracy indicators. The inclusion of time-dependent macroeconomic variables in addition to covariates representing characteristics of the contract and individuals improved the overall performance. Logistic regression showed a higher discriminatory power than Cox proportional hazards model according to all accuracy indicators. It is worth mentioning the negative relationship between the probability of default and the economy base interest rate. Decreases in the base interest rate lead banks to lose revenue from treasury operations and expand credit operations to compensate the loss. This strategy brings individuals with a higher probability of default to the financial market.     

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.     

Bank competition,financial development and macroeconomic stability: Empirical evidence from emerging economies

In this study, we investigate the potential contribution of bank competition to macroeconomic stability, and the interactive role of financial development. We classify macroeconomic stability into economic and financial stability. Economic stability is represented by the volatility of actual and unexpected output growth, whereas financial stability is assessed by the aggregate Z-score and volatility of the private credit-to-gross domestic product ratio. We employ two structural and two non-structural measures of bank competition in our analysis. Applying a two-step dynamic panel system (GMM) to macroeconomic data from 48 developing nations from 1999 to 2018, we find a bell-shaped relationship between bank competition and macroeconomic stability. The findings imply that a higher level of bank competition promotes macroeconomic stability by reducing output growth volatility, fluctuations in private credit, and the probability of bank default. There is an optimal level of bank competition beyond which it may foster economic and financial instability. Moreover, financial development enhances bank competition’s positive impact on macroeconomic stability.     

Market perception of sovereign credit risk in the euro area during the financial crisis

We study market perception of sovereign credit risk in the euro area during the financial crisis. In our analysis we use a parsimonious CDS pricing model to estimate the probability of default (PD) and the loss given default (LGD) as perceived by financial markets. In our empirical results the estimated LGDs perceived by financial markets stay comfortably below 40% in most of the samples. Global financial indicators are positively and strongly correlated with the market perception of sovereign credit risk; whilst macroeconomic and institutional developments were at best only weakly correlated with the market perception of sovereign credit risk.     

信息不完全的信用风险定价模型及其在我国的应用

基于信息不完全的信用风险定价模型与传统的结构化模型和约化模型的最大区别在于它将信息不完全这一前提引入了以信息完全为前提的结构化模型,同时它又考虑了约化模型中强度的优点,引入短期信用风险的度量,成为当前最切合现实的信用风险定价模型。本文认为,应用基于信息不完全的信用风险定价模型来测度信用风险,将具有十分重要的现实意义。     

Modeling frailty-correlated defaults using many macroeconomic covariates

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a downward bias in estimated default rate volatility and in estimated probabilities of extreme default losses on portfolios of U.S. debt. The latent factor does not substitute for a single omitted macroeconomic variable. We argue that it captures different omitted effects at different times. We also provide empirical evidence that default and business cycle conditions partly depend on different processes. In an out-of-sample forecasting study for point-in-time default probabilities, we obtain mean absolute error reductions of more than forty percent when compared to models with observed risk factors only. The forecasts are relatively more accurate when default conditions diverge from aggregate macroeconomic conditions.     

A mixed PDE-Monte Carlo approach for pricing credit default index swaptions

Abstract The problem of numerically pricing credit default index swaptions on a large number of names is considered. We place ourselves in a stochastic intensity framework, where Ornstein-Uhlenbeck-type correlated processes are used to model both firms’ distance to default and a macroeconomic state variable. Here the default of the firms’ follows the reduced-form approach and the (random) intensity of the default depends on the behavior of the diffusion processes. We propose here a numerical method based on both a Monte Carlo and a deterministic approach for solving PDEs by finite differences. Numerical tests demonstrate the efficiency and the robustness of the proposed procedure.     

基于省域视角的信用风险模型适应性分析

巴塞尔新资本协议在鼓励银行采用内部评级法评估信用风险以提取资本准备的同时也强化了各国监管机构对内部评级模型绩效检验与审查的要求.CreditMetrics和CreditRisk+是银行业信用风险评估的基准模型.从建模的数学方法看,CreditRisk+是基于违约的判断,而CreditMetrics则是根据等级变化评价.利用江苏省银监局的相关统计数据对信用风险评估模型进行参数特性审查与绩效检验,结果显示这两类常用模型都可以在江苏的商业银行经营实践中稳定地实现根据信贷组合的实际风险状况进行内部资本配置这一目标.     

What are the macroeconomic effects of high‐frequency uncertainty shocks?

This paper evaluates the effects of high‐frequency uncertainty shocks on a set of low‐frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single‐frequency and mixed‐frequency data models, suggesting that the temporal aggregation bias is not acute in this context.     

A vector-autoregression analysis of credit and liquidity factor dynamics in US LIBOR and Euribor swap markets

We use a vector autoregressive approach to investigate the determinants of US Dollar LIBOR and Euribor swap spread variation during the 2007–2009 crisis in global credit and money markets. Using market-quoted yield and spread data from the highly liquid credit default swap (CDS) and overnight index swap (OIS) markets, we provide compelling empirical evidence that liquidity risk factor shocks have been the dominant drivers of the variation in swap spreads over this period. Our findings provide an explanation for the temporal differences that liquidity shocks have on swap spreads and provide a contemporary perspective on the dynamical interplay between credit-default and liquidity risk-factors in these markets. As all our risk-factor proxies are traded in liquid derivatives markets, our findings have implications for proprietary hedge fund traders hedging an exposure to swap-spread risk, for bank treasurers managing their liquidity requirements and for central bankers seeking to better understand the response of markets to their macroeconomic policy implementation and liquidity management actions. Indeed our markets-based analysis suggests that the European Central Bank (ECB) has underperformed relative to the Federal Reserve in terms of the differing levels of market confidence placed in its macroeconomic policy actions and remedial liquidity interventions during the period.     

What drives credit risk in the Indian banking industry? An empirical investigation

Using a two-step system GMM approach on a unique bank-level dataset for the period 1998/99–2013/14, this paper tries to explore the key determinants of credit risk in the Indian banking industry. The main premise of this paper is that, along with regulatory and institutional factors, both macroeconomic and bank-specific variables influence the formation of credit risk in a banking system, and their influences vary across ownership groups. The empirical findings suggest that lower profitability, more diversification in the banking business, the large size of banks and a higher concentration of banks in lending increase the probability of defaults in India. We find a significant degree of persistence in credit risk, and the observed persistence is higher in the gross non-performing loans (NPLs) specification relative to what has been observed in the net NPLs specification. In the case of public sector banks, NPLs are more sensitive to internal bank-specific factors, while for private and foreign banks, macroeconomic and industry-related factors play a significant role in determining credit risk. Our results are robust for different panel data estimation models and sub-samples of ownership groups. The findings of this paper provide important insights into the formation of default risk in the banking system of an emerging market economy.     

Loss given default models incorporating macroeconomic variables for credit cards

Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.     

A mixture model for credit card exposure at default using the GAMLSS framework

The Basel II and III Accords propose estimating the credit conversion factor (CCF) to model exposure at default (EAD) for credit cards and other forms of revolving credit. Alternatively, recent work has suggested it may be beneficial to predict the EAD directly, i.e.modelling the balance as a function of a series of risk drivers. In this paper, we propose a novel approach combining two ideas proposed in the literature and test its effectiveness using a large dataset of credit card defaults not previously used in the EAD literature. We predict EAD by fitting a regression model using the generalised additive model for location, scale, and shape (GAMLSS) framework. We conjecture that the EAD level and risk drivers of its mean and dispersion parameters could substantially differ between the debtors who hit the credit limit (i.e.“maxed out” their cards) prior to default and those who did not, and thus implement a mixture model conditioning on these two respective scenarios. In addition to identifying the most significant explanatory variables for each model component, our analysis suggests that predictive accuracy is improved, both by using GAMLSS (and its ability to incorporate non-linear effects) as well as by introducing the mixture component.     

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.     

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD

Estimating the recovery rate and recovery amount has become important in consumer credit due to the new Basel Accord regulation and the increase in the number of defaulters as a result of the recession. We compare linear regression and survival analysis models for modelling recovery rates and recovery amounts, in order to predict the loss given default (LGD) for unsecured consumer loans or credit cards. We also look at the advantages and disadvantages of using single and mixture distribution models for estimating these quantities.     

Spillover effects on government bond yields in euro zone. Does full financial integration exist in European government bond markets?

This paper examines the time varying nature of European government bond market integration by employing multivariate GARCH models. We state that unlike other bond markets, in euro markets the default(credit) risk factor and other macroeconomic and fiscal indicators are not able to explain the sovereign bond yields after the beginning of monetary union. This fact might be counted as a signal for perfect financial integration. However, we also find that the global shocks affect Germany and the rest of euro bond markets in various levels, creating particular discrepancies in asset prices even we take into account the market specific factors. Different level responses of each euro market to the global shocks reveal that euro bond markets are not fully integrated with each other unlike the recent literature claimed. Besides, we explore that the global factors are effective for the volatility of yield differentials among euro government bonds.