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, the authors argue that credit spreads on corporate bonds tend to be many times wider than what would be implied by expected default losses alone, and they argue that spreads are so wide because they are pricing undiversified credit risk.
Abstract: Spreads on corporate bonds tend to be many times wider than what would be implied by expected default losses alone. These spreads are the difference between yields on corporate debt subject to default risk and government bonds free of such risk.2 While credit spreads are often generally understood as the compensation for credit risk, it has been difficult to explain the precise relationship between spreads and such risk. In 1997–2003, for example, the average spread on BBB-rated corporate bonds with three to five years to maturity was about 170 basis points at annual rates. Yet, during the same period, the average yearly loss from default amounted to only 20 basis points.In this case, the spread was more than eight times the expected loss from default. The wide gap between spreads and expected default losses is what we call the credit spread puzzle. In this article we argue that the answer to the credit spread puzzle might lie in the difficulty of diversifying default risk. Most studies to date have implicitly assumed that investors can diversify away the unexpected losses in a corporate bond portfolio. However, the nature of default risk is such that the distribution of returns on corporate bonds is highly negatively skewed. Such skewness would require an extraordinarily large portfolio to achieve full diversification. Evidence from the market for collateralised debt obligations (CDOs) indicates that in practice such large portfolios are unattainable, and thus unexpected losses are unavoidable. Hence, we argue that spreads are so wide because they are pricing undiversified credit risk. We first review the existing evidence on the determinants of credit spreads, including the role of taxes, risk premia and liquidity premia. We then discuss the role of unexpected losses and the difficulties involved in diversifying credit portfolios, drawing on evidence from the CDO market.
214 citations
TL;DR: The E-Cig FDS provides visual differentiation of the type of fluid that is being smoked, or vaped, and a user can selectively choose if the fluid is safe, non-nicotine or nicotine-infused.
Abstract: While theory predicts different effects of household credit and enterprise credit on the economy, the empirical literature has mainly used aggregate measures of overall bank lending to the private sector. We construct a new dataset from 45 developed and developing countries, decomposing bank lending into lending to enterprises and lending to households and assess the different effects of these two components on real sector outcomes. We find that: 1) enterprise credit raises economic growth whereas household credit has no effect; 2) enterprise credit reduces income inequality whereas household credit has no effect; and 3) household credit is negatively associated with excess consumption sensitivity, while there is no relationship between enterprise credit and excess consumption sensitivity.
213 citations
TL;DR: A credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm to decide whether to approve or reject a credit application is described.
Abstract: This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications.
213 citations
TL;DR: This paper used principal components analysis to identify common factors in the movement of banks' credit default swap spreads and found that the importance of common factors rose steadily to exceptional levels from the outbreak of the Subprime crisis to past the rescue of Bear Stearns, reflecting a diffuse sense that funding and credit risk was increasing.
213 citations
TL;DR: This article used time series data on specific banks' daily offering rates during the period May 1982 through July 1988, and found that CD rates paid by large money center banks include significant default risk premia.
213 citations