Topic
Credit risk
About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.
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TL;DR: In this article, a model-free measure of euro-area market liquidity, and a measure of near-term interbank default risk are proposed to identify the contribution of these two effects on sovereign bond and interbank spreads, and the possibility that liquidity could be negatively correlated with marginal utility.
Abstract: Wide and volatile interest rate spreads in the 2007-2009 financial crisis could represent concerns over asset liquidity or issuer solvency. To precisely identify the contribution of these two effects on sovereign bond and interbank spreads, I propose a model-free measure of euro-area market liquidity, and a measure of near-term interbank default risk. I find that credit and liquidity are independently important. In interbank risk spreads, the role of liquidity dominates, while the importance in sovereign bond yield spreads varies substantially by country and maturity. To better understand the liquidity channel that is captured by the new liquidity measure, but is understated by extant measures, I test the pricing of liquidity risk; the possibility that liquidity could be negatively correlated with marginal utility. I exploit the variation in returns over countries, maturities and time, and find that liquidity euro-area sovereign bond risk premia are large and significant.
156 citations
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TL;DR: In this article, a Markov model for the term structure of credit risk spreads is proposed based on Jarrow and Turnbull (1995) with the bankruptcy process following a discrete state space Markov chain in credit ratings.
Abstract: This paper provides a Markov model for the term structure of credit risk spreads. The model is based on Jarrow and Turnbull (1995) with the bankruptcy process following a discrete state space Markov chain in credit ratings. The parameters of this process are easily estimated using observable data. This model is useful for pricing and hedging corporate debt with imbedded options, for pricing and hedging OTC derivatives with counterparty risk, for pricing and hedging (foreign) government bonds subject to default risk (e.g., municipal bonds), for pricing and hedging credit derivatives, and for risk management.
155 citations
TL;DR: The development of the methodology is surveyed, the current environment for consumer lending is described, and some of the modelling areas and issues that are actively being researched or should be are identified.
Abstract: Methods for assessing the credit risk when lending to consumers has been in operation for 50 years. Yet, there are probably now more opportunities and challenges for research into the development of this area than ever before. This paper surveys the development of the methodology, describes the current environment for consumer lending and seeks to identify some of the modelling areas and issues that are actively being researched or should be.
155 citations
TL;DR: In this article, the authors consider counterparty risk for credit default swaps (CDS) in presence of correlation between default of the counterparty and default of CDS reference credit, and propose a methodology for valuation of contingent CDS on CDS.
Abstract: We consider counterparty risk for Credit Default Swaps (CDS) in presence of correlation between default of the counterparty and default of the CDS reference credit. Our approach is innovative in that, besides default correlation, which was taken into account in earlier approaches, we also model credit spread volatility. Stochastic intensity models are adopted for the default events, and defaults are connected through a copula function. We find that both default correlation and credit spread volatility have a relevant impact on the positive counterparty-risk credit valuation adjustment to be subtracted from the counterparty-risk free price. We analyze the pattern of such impacts as correlation and volatility change through some fundamental numerical examples, analyzing wrong-way risk in particular. Given the theoretical equivalence of the credit valuation adjustment with a contingent CDS, we are also proposing a methodology for valuation of contingent CDS on CDS.
154 citations
TL;DR: The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy, and how such accuracy could be improved by using classifier ensembles.
Abstract: Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles. Benchmarking results on four credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.
154 citations