Corporate credit risk modeling and the macroeconomy

International Review of Economics & Finance, 2012

We explore how general economic conditions impact defaults and major credit rating changes by fitting reduced-form Cox intensity models with a broad range of macroeconomic and firm-specific ratings-related variables. For all corporate issuers in the period 1981-2002 we find both types of factors strongly influenced the risk of a credit event. However, while the effects of ratings-related factors were consistent with expectations and very robust under different specifications, significance levels and even signs for the macro variable coefficients depended heavily on which other variables were included. This sheds light on the disparate results reported in earlier studies.

In this paper we study how corporate bond defaults can be predicted using financial ratios and how the forecasted probability of default relates to the cross-section of expected stock returns. Using several performance measures we find that the duration model outperforms existing models in correctly classifying both Default and Non-Default firms. Using the default probabilities predicted by our model, we analyze the relation between default risk and the Fama-French distress factors, HML and SMB. We find evidence that supports the interpretation on HML as a distress related factor. Both portfolio and individual stock factor loadings are related to the estimated default probabilities. We find a negative and significant contemporaneous correlation between HML and shocks to the level of aggregate financial distress. Torous for valuable suggestions and helpful comments. All remaing errors are the author's responsability.

SSRN Electronic Journal, 2007

2015

The credit risk is the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms. Using a large dataset of corporate balance sheets we develop a survival model to predict default. Unlike previous works, we consider forecasts of probability of default for small corporate, we take into account regional macroeconomic conditions as well as national macroeconomic indicators and we use a large sample of balance sheet. We define our model after a series of steps designed to select only the significant variables; we examine the scale of the continuous covariates in the preliminary main effect model and we apply, when required, appropriate transformations to respect the linearity in the log hazard. Last but not least, we check the proportionality hazard assumption.

Journal of Evolutionary Economics, 2011

This paper investigates the relevance of financial and economic variables as determinants of firm defaults. Our analysis is not limited to publicly traded companies but extends to a large sample of limited liability firms. We consider size, growth, profitability and productivity together with a standard set of financial indicators. Non parametric tests allow to assess to what extent defaulting firms differ from the non-defaulting group. Bootstrap probit regressions confirm that economic variables play both a long and short term effect. Our findings are robust with respect to the inclusion of Distance to Default and risk ratings among the regressors.

VIDYA - A JOURNAL OF GUJARAT UNIVERSITY

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides ...

2011

This monograph address the empirical estimation of corporate default risk. My audience is researchers from academia, regulatory authorities, and the financial services industry. A graduate-level background in probability theory is assumed for readers who plan to focus on the statistical methodology. In addition to data obtained from common public sources, I rely heavily on corporate default event data from Moody's, to whom I am grateful. I am especially grateful to Richard Cantor and Roger Stein of Moody's, for their longstanding research support to myself and other members of the Moody's Academic Advisory Research Committee. Edward Altman generously provided additional helpful data on corporate default events. I am especially thankful for organizational and editorial guidance from Andrew Schuller, and for the hospitality of Andrew Schuller, Jenni Craig, and Colin Mayer while at Oxford University in June, 2004, when presenting the Clarendon Lectures in Finance, on which much of this manuscript is based. The research described here is the product of various collaborations with Sanjiv

The recent financial crisis raised awareness of the need for a framework for conducting macroprudential policy. Identifying as early as possible and addressing the buildup of endogenous imbalances, exogenous shocks, and contagion from financial markets, market infrastructures, and financial institutions are key elements of a sound macroprudential framework. This paper contributes to this literature by estimating several models of default probability, two of which relax two key assumptions of the Merton model: the assumption of constant asset volatility and the assumption of a single debt maturity. The study uses market and banks' balance sheet data. It finds that systemic risk in Luxembourg banks, while mildly correlated with that of European banking groups, did not increase as dramatically as it did for the European banking groups during the heights of the financial crisis. In addition, it finds that systemic risk has declined during the second half of 2010, both for the banking groups as well as for the Luxembourg banks. Finally, this study illustrates how models of default probability can be used for event-study purposes, for simulation exercises, and for ranking default probabilities during a period of distress according to banks' business lines. As such, this study is a stepping stone toward developing an operational framework to produce quantitative judgments on systemic risk and financial stability in Luxembourg.

The UniTed STaTeS is a nation of debtors. By the end of 2007, total debt outstanding by households, businesses, state and local governments, and the federal government added up to $31.2 trillion. The domestic financial sector accounted for half of this total, or $15.8 trillion. The size of the debt market is quite large. Indeed, it exceeds both the U.S. GDP in 2007 ($13.8 trillion) and the equity market value of all domestic corporations ($15.5 trillion). 1 The primary risk of all this debt is credit risk, or the risk of default. The current credit crisis demonstrates how shifts in credit spreads and market liquidity can also significantly impact debt values. Although these alternative factors are important for understanding debt markets, we will focus here only on default risk. Investors measure default risk in many different ways, and there have been important recent innovations in this regard. The state of the art in assessing corporate credit risk is based on one of three approaches: 1) the Merton distance-to-default measure, 2) the reduced-form approach, and 3) credit ratings. We will compare and contrast these three approaches, showing that the reducedform approach is preferred because of its generality, flexibility, and superior forecasting ability. Merton's Distance-to-Default For more than three decades, a common approach used to measure a firm' s default probability has been the so-called distance-to-default. This measure is based on the pioneer

Empirical Economics

We present a new measure of extreme credit risk in the time domain, namely the conditional expected time to default (CETD). This measure has a clear interpretation and can be applied in a straightforward way to the analyses of loan performance in time. In contrast to the probability of default, CETD provides direct information on the timing of a potential loan default under some stress scenarios. We apply a novel method to compute CETD using Markov probability transition matrices, a popular approach in the survival analysis literature. We employ the new measure to the analysis of changing credit risk in a large portfolio of corporate loans. CETD changes through time in line with other measures of credit risk and is positively related to output growth.