The impact of central clearing on the market for single-name credit default swaps

In this article, we revisit the impact of the voluntary central clearing scheme on the CDS market. In order to address the endogeneity problem, we use a robust methodology that relies on dynamic propensity-score matching combined with generalized difference-in-differences. Our empirical findings show that central clearing results in a small increase in CDS spreads (ranging from 14 to 19 bps), while there is no evidence of an associated improvement in CDS market liquidity and trading activity or of a deterioration in the default risk of the underlying bond. These results suggest that the increase in CDS spreads can be mainly attributed to a reduction in CDS counterparty risk.     

The effect of systematic risk factors on counterparty default and credit risk of interest rate swaps

This research investigates the effect of specific systematic risk factors on credit risk pricing and capital allocation of interest rate swaps. Because of the stochastic nature of uncertain future cash flows and interest rates, practitioners typically employ the Black-Scholes option pricing model in combination with a simulation analysis to establish capital requirements and estimate the shadow price of an interest rate swap. However, this practice of pricing swap risk excludes systematic risk factors that affect the risk shadow price, thereby underestimating the capital allocation required for financial institutions. This research demonstrates the effect of risk mispricing when simulation models ignore systematic risk factors such as model risk, convexity risk, and parameter risk on the pricing of interest rate swaps.     

Credit default swaps and sovereign debt markets

This study investigates the link between the price discovery dynamics in sovereign credit default swaps (CDS) and bond markets and the degree of financial integration of emerging markets. Using CDS and sovereign bond spreads, the price discovery mechanism was tested using a vector error correction model. Financial integration is measured using news-based methods. We find that sovereign CDS and bond markets are co-integrated. In five out of seven sovereigns (71%), the bond market leads in price discovery by adjusting to new information regarding credit risk before CDS. In 29% of times, CDS markets are the source of price discovery. We also find a positive correlation of 0.67 between the degree of financial integration and the bond market information share. The evidence suggests that changes in sovereign credit risk and bond yields are significantly influenced by common external (global) factors, while country-specific factors play an insignificant role.     

Pricing basket default swaps using quasi-analytic techniques

Decisions in Economics and Finance - This research work is based on the concept of the one-factor copula model together with the discrete Fourier transform, which is applied to reduce the...     

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.     

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.     

Endogenous information revelation in a competitive credit market and credit crunch

In this paper, we propose a new mechanism able to explain the occurrence of credit crunches. Considering a credit market with an asymmetry of information between borrowers and lenders, we assume that borrowers have to pay a cost to reveal information on the quality of their project. They decide to be transparent if it is necessary for getting a loan or for paying a lower interest rate. Two types of competitive equilibria may exist: an opaque equilibrium in which all projects receive funding without revealing information; a transparent one in which only the best projects reveal information and receive funding. It is also possible to get multiple equilibria. Incorporating this microeconomic mechanism in an OLG model, the economy may experience fluctuations due to the change of regime, and indeterminacy may occur.     

Endogenous debt constraints in collateralized economies with default penalties

The objective of the paper is to propose endogenous debt constraints that rule out Ponzi schemes and ensure the existence of equilibria in a model with limited commitment and (possible) default. We appropriately modify the definition of finitely effective debt constraints, introduced by Levine and Zame (1996) (see also Levine and Zame (2002)), to encompass models with limited commitment, default penalties and collateral. Along this line, we introduce in the setting of Araujo et al. (2002), Kubler and Schmedders (2003) and Páscoa and Seghir (2009) the concept of actions with finite equivalent payoffs. We show that, independent of the level of default penalties, restricting plans to have finite equivalent payoffs rules out Ponzi schemes and guarantees the existence of an equilibrium that is compatible with the minimal ability to borrow and lend that we expect in our model.     

Probability of default in collateralized credit operations for small business

The paper examines how the collateral affects the probability of default for small firms. We present a stylized theoretical model to derive the relationship between the level of collateral and subsequent loan default. We find that the probability of default is negatively correlated with the level of collateral, which is intuitive. Subsequently, we test this relationship by using a proprietary database of collateralized loans of small Brazilian enterprises.     

Endogenous credit standards and aggregate fluctuations

Widespread empirical evidence shows that credit standards fluctuate over the business cycle. We build a macroeconomic model in which countercyclical lending standards emerge as an equilibrium outcome. In the model, banks compete on lending rates as well as collateral requirements. The presence of lending relationships between firms and banks gives rise to endogenous fluctuations in interest rate margins and collateral requirements. We demonstrate that endogenous credit standards amplify business cycles, driving up output volatility by around 25% when compared to a model without lending relationships. Finally, we show that in order to combat the effects of endogenous credit standards on macroeconomic volatility, a countercyclical loan-to-value ratio is an effective macroprudential policy tool.     

Asymmetric convergence in US financial credit default swap sector index markets

This study examines the asymmetric adjustments to the long-run equilibrium for credit default swap (CDS) sector indexes of three financial sectors – banking, financial services and insurance – in the presence of a threshold effect. The results of the momentum-threshold autoregression (M-TAR) models demonstrate that asymmetric cointegration exists for all pairs comprised of those three CDS indexes. The speeds of adjustment in the long-run are much higher in the case of adjustments from below the threshold than from above for all the pairs. The estimates of The MTAR-VEC models suggest that the dual CDS index return in each sector pair participates in the adjustment to equilibrium in the short- and long-run taken together. But in the long-run alone, only one of the two spreads in each pair participates. Policy implications are also provided.     

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.     

Forecasting and stress testing credit card default using dynamic models

We present discrete time survival models of borrower default for credit cards that include behavioural data about credit card holders and macroeconomic conditions across the credit card lifetime. We find that dynamic models which include these behavioural and macroeconomic variables provide statistically significant improvements in model fit, which translate into better forecasts of default at both account and portfolio levels when applied to an out-of-sample data set. By simulating extreme economic conditions, we show how these models can be used to stress test credit card portfolios.     

The conditional dependence structure of insurance sector credit default swap indices

This study assesses the dependence structure of insurance sector credit default swap indices, using a copula-GARCH approach. We use daily data of the US, EU, and UK insurance sectors, covering the period from January 2004 to June 2013. We find substantial increases in dependence during the financial crisis periods. Prior to the crises, various copulas are found to best fit each pair; specifically, asymmetric tail dependence is found for the UK–US pair, suggesting the possibility of large simultaneous losses. However, during the crisis periods, the Frank copula fits best, with no significant tail dependence detected, implying low systemic risks.     

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.     

Endogenous fluctuations,markups, capacity and credit constraints

In the present paper endogenous fluctuations have been generated by referring to endogenous markups, variable capacity utilization, and credit constraints. As one can detect, they are the same ingredients as those used to generate the so called selffulfilling cycles. With respect to this strand of literature, three changes are introduced. First of all, credit constraints are conceived within the Minsky’s financial instability hypothesis. Secondly, markups may have different dynamic patterns and impacts. Finally, heterogeneous agents are assumed to form evolutionary expectations. The results of these interacting aggregate demand and supply aspects are endogenous fluctuations obtained by means of simulations. Robust limit cycles and interesting comovements between variables are achieved in this medium-run model.     

Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness

This study assesses systemic risk inherent in credit default swap (CDS) indices using empirical and statistical analyses. We define systemic risk in two perspectives: the possibilities of simultaneous and contagious defaults, and then quantify them separately across benchmark models. To do so, we employ a Marshall-Olkin copula model to measure simultaneous default risk, and an interacting intensity-based model to capture contagious default risk. For an empirical test, we collect daily data for the iTraxx Europe CDS index and its tranche prices in the period from 2005 to 2014, and calibrate model parameters varying across time. In addition, we select forecasting models that have minimal prediction errors for the calibrated time series. Finally, we identify significant changes in each dynamic of systemic risk indicator before and after default and downgrade-related episodes that have occurred in the global financial crisis and European sovereign debt crisis.     

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.     

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.     

Payment changes and default risk: The impact of refinancing on expected credit losses

This paper analyzes how access to credit and the path of mortgage rates can affect borrower credit risk. This is a crucial issue for evaluating refinance programs as a form of loss mitigation, and it became prominent in the debates around the Treasury Department's Home Affordable Refinance Program (HARP). These debates exposed gaps in the literature on the relationship between credit performance and changes in borrowers’ monthly mortgage payments. Since then, several new studies have attempted to fill these holes, each pursing a different methodology. In this paper, we review the relevant debates and look at what downward adjustments in prime adjustable-rate mortgages can tell us about modifications of prime fixed-rate mortgages. We argue that this method better addresses the various sampling biases that plague all attempts to predict HARP's impact. Our analysis indicates that typical monthly payment reductions under HARP would reduce credit losses by 56 basis points.