Credit risk

About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.

The relationship between liquidity risk and credit risk in banks.

Abstract: This paper investigates the relationship between the two major sources of bank default risk: liquidity risk and credit risk. We use a sample of virtually all US commercial banks during the period 1998–2010 to analyze the relationship between these two risk sources on the bank institutional-level and how this relationship influences banks’ probabilities of default (PD). Our results show that both risk categories do not have an economically meaningful reciprocal contemporaneous or time-lagged relationship. However, they do influence banks’ probability of default. This effect is twofold: whereas both risks separately increase the PD, the influence of their interaction depends on the overall level of bank risk and can either aggravate or mitigate default risk. These results provide new insights into the understanding of bank risk and serve as an underpinning for recent regulatory efforts aimed at strengthening banks (joint) risk management of liquidity and credit risks.

Determinants and impacts of sovereign credit ratings

Abstract: In this article, we present the first systematic analysis of the sovereign credit ratings of the two leading agencies, Moody's and Standard & Poor's (S&P). We find that the ordering of risks they imply is broadly consistent with macroeconomic fundamentals. While the agencies cite a large number of criteria in their assignment of sovereign ratings, a regression using only eight factors explains more than 90 percent of the cross-sectional variation in the ratings. In particular, a country's rating appears largely determined by its per capita income, external debt burden, inflation experience, default history and level of economic development. We do not, however, find any systematic relationship between ratings and either fiscal or current deficits, perhaps because of the endogeneity of fiscal policy and international capital flows. Sovereign ratings are also closely related to market-determined credit spreads, effectively summarizing and supplementing the information content of macroeconomic indicators in the pricing of sovereign risk. Cross-sectional results suggest that the rating agencies' opinions have independent effects on market spreads. Event study analysis broadly confirms this qualitative conclusion, for the reactions of bond yields to the announcements of changes in the agencies' sovereign risk opinions are statistically significant with respect to both their sign and magnitude.

Parameterizing credit risk models with rating data

Abstract: Estimates of average default probabilities for borrowers assigned to each of a financial institution’s internal credit risk rating grades are crucial inputs to portfolio credit risk models. Such models are increasingly used in setting financial institution capital structure, in internal control and compensation systems, in asset-backed security design, and are being considered for use in setting regulatory capital requirements for banks. This paper empirically examines properties of the major methods currently used to estimate average default probabilities by grade. Evidence of potential problems of bias, instability, and gaming is presented. With care, and perhaps judicious application of multiple methods, satisfactory estimates may be possible. In passing, evidence is presented about other properties of internal and rating-agency ratings. ” 2001 Elsevier Science B.V. All rights reserved.

Forecasting bank loans loss-given-default

Abstract: With the advent of the new Basel Capital Accord, banking organizations are invited to estimate credit risk capital requirements using an internal ratings based approach. In order to be compliant with this approach, institutions must estimate the loss-given-default, the fraction of the credit exposure that is lost if the borrower defaults. This study evaluates the ability of a parametric fractional response regression and a nonparametric regression tree model to forecast bank loan credit losses. The out-of-sample predictive ability of these models is evaluated at several recovery horizons after the default event. The out-of-time predictive ability is also estimated for a recovery horizon of 1 year. The performance of the models is benchmarked against recovery estimates given by historical averages. The results suggest that regression trees are an interesting alternative to parametric models in modeling and forecasting loss-given-default.

Merton's model, credit risk and volatility skews

Abstract: In 1974 Robert Merton proposed a model for assessing the credit risk of a company by characterizing the company’s equity as a call option on its assets. In this paper we propose a method for estimating the model’s parameters from the implied volatilities of options on the company’s equity. We use data from the credit default swap market to compare our implementation of Merton’s model with the traditional approach to implementation.