Forecasting probabilities of default and loss rates given default in the presence of selection (original) (raw)

Predicting and Pricing the Probability of Default

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.

Performance of default-risk measures: the sample matters

Journal of Banking & Finance, 2020

This paper examines the predictive power of the main default-risk measures used by both academics and practitioners, including accounting measures, market-price-based measures and the credit rating. Given that some measures are unavailable for some firm types, pair wise comparisons are made between the various measures, using same-size samples in every case. The results show the superiority of market-based measures, although their accuracy depends on the prediction horizon and the type of default events considered. Furthermore, examination shows that the effect of withinsample firm characteristics varies across measures. The overall finding is of poorer goodness of fit for accurate default prediction in samples characterised by high book-to-market ratios and/or high asset intangibility, both of which suggest pricing difficulty. In the case of large-firm samples, goodness of fit is in general negatively related to size, possibly because of the "too-big-to-fail" effect.

Measuring the Risk of Default: A Modern Approach

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 31.2trillion.Thedomesticfinancialsectoraccountedforhalfofthistotal,or31.2 trillion. The domestic financial sector accounted for half of this total, or 31.2trillion.Thedomesticfinancialsectoraccountedforhalfofthistotal,or15.8 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 performance of loss given default prediction models

The global financial crisis highlighted the fact that default and recovery rates of multiple borrowers generally deteriorate jointly during economic downturns. The vast majority of the literature, as well as many industry credit-portfolio risk models, ignore this and analyze default probabilities and recoveries in the event of default separately. As a result, the models project losses that are too low in economic downturns such as the recent financial crisis. Nevertheless, alternatives that incorporate the dependence between probabilities of default and recovery rates have been proposed. This paper is the first of its kind to assess the performance of these structurally different approaches. Four banks using different estimation procedures are compared. We use root mean square errors and relative absolute errors to measure the predictive accuracy of each procedure. The results show that models accounting for the correlation of default and recovery do indeed perform better than models ignoring it.

Estimating & Forecasting Default Risk: Evidence from Jamaica

2017

This paper employs the GMM estimation technique to evaluate the impact of macroeconomic factors on bank default risk for listed Jamaican banks and securities dealers (SDs) over the period December 2004 to June 2016. Default risk is captured by a distance-to-default measure which is computed using a Merton type, option-based model. This indicator accurately tracks the default experience of listed Jamaican banks and SDs over important dates throughout the sample period. The estimation results of the model revealed that GDP growth, inflation, the unemployment rate, growth in domestic private sector credit as well as the REER have a statistically significant impact on the performance of the distance to default measure. As such, the econometric findings validate the sensitivity of the fragility measure to the variability of key macroeconomic variables. The model was also utilized to forecast the distance to default measure six-quarters ahead, as this will aid in the formulation of policy...

The prediction of default for high yield bond issues

Review of Financial Economics, 1996

Bondholders and financial analysts have long sought models which will predict financial distress in corporations. Prior research has produced a number of usefiJI models to predict banknrpfcy in the short term. This paper looks at four models which predict defadt based upon public information at the time of issuance of high yield bonds. Multivariate results using logistic regression analysis indicate that high yield issues that default are characterized by having higher asset growth rates, lower operating profit margins, larger levels of collateralizable assets, and larger changes in net working capital. Models using Altman (1968) variables have lower likelihood ratio indexes than models cmploying alternative explanatory variables. Predictive ability tests of models excluding the traditional variables on a holdout sample are able to correctly predict 73.3 percent of the defaulted bonds and 68.6 percent of the nondefaulted bonds. Bondholders and financial analysts have long sought models which will predict financial distress in corporations. Prior research has produced a number of useful models to predict bankruptcy and bond rating. However, previous studies have not attempted to predict default on bonds. This paper investigates models which predict defuult based upon public information available at the time oJissuance of high yield bonds. The models investigated are useful to investors who wish to participate in high yield bond offerings and minimize transaction costs and monitoring costs. Research and interest in the prediction of financial distress has increased dramatically since the ground breaking research of Beaver (1966) and Altman (1968). However, these studies and subsequent studies predict bankruptcy by using financial data for a number of months or years prior to the bankruptcy event. This

Loss Given Default Estimations in Emerging Capital Markets

A. M. Karminsky et al. (eds.), Risk Assessment and Financial Regulation in Emerging Markets’ Banking, Advanced Studies in Emerging Markets Finance, 2021

This chapter proposes an approach to decompose the RR/LGD model development process with two stages, specifically, for the RR/LGD rating model, and to calibrate the model using a linear form that minimizes residual risk. The residual risk in the recovery of defaulted debts is determined by the high uncertainty of the recovery level according to its average expected level. Such residual risk should be considered in the capital requirements for unexpected losses in the loan portfolio. This paper considers a simple residual risk model defined by one parameter. By developing an optimal RR/LGD model, it is proposed to use a residual risk metric. This metric gives the final formula for calibrating the LGD model, which is proposed for the linear model. Residual risk parameters are calculated for RR/LGD models for several open data sources for developed and developing markets. An implied method for updating the RR/LGD model is constructed with a correction for incomplete recovery through the recovery curve, which is built on the training sets. Based on the recovery curve, a recovery indicator is proposed which is useful for monitoring and collecting payments. The given recommendations are important for validating the parameters of RR/LGD model.

Multi-period corporate default prediction with stochastic covariates

Journal of Financial Economics, 2007

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For U.S. Industrial firms, based on over 390,000 firm-months of data spanning 1979 to 2004, the level and shape of the estimated term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S& P 500 returns, and on U.S. interest rates, among other covariates. Variation in a firm's distance to default has a substantially greater effect on the term structure of future default hazard rates than does a comparatively significant change in any of the other covariates. Default intensities are estimated to be lower with higher short-term interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models.

Innovative Approach for Forecasting Corporate Default Risk

Journal of Global Economy, 2018

        Innovative Approach for Forecasting Corporate Default Risk       Submitted To: Journal of Global Economy                              By: Prashanta Kumar Behera, PhD                              Email: pkb_behera@yahoo.in . Ph : 91+8108932693: Present time corporate default risk parameters are dynamic in nature and understanding how these parameters change in time is a fundamental task for risk management. In this research paper I am trying to forecast for corporate default rates.  I work with historical credit migrations data to construct some time series of interest and to visualize default rates dynamics and also, I use some of the series constructed and some additional data to fit a forecasting model for corporate default rates and to shows some back testing and stress testing. A linear regression model for corporate default rates is presented but the tools and concepts described can be u...

A comparative study of the probability of default for global financial firms

Journal of Banking & Finance, 2012

This article presents a modification of ruin option pricing model to estimate the implied probability of default from stock and option market prices. To test the model, we analyze all global financial firms with traded options in the US and focus on the subprime mortgage crisis period. We compare the performance of the implied probability of default from our model to the expected default frequencies based on the Moody's KMV model and agency credit ratings by constructing cumulative accuracy profiles (CAP) and the receiver operating characteristic (ROC). We find that the probability of default estimates from our model are equal or superior to other credit risk measures studied based on CAP and ROC. In particular, during the subprime crisis our model surpassed credit ratings and matched or exceeded KMV in anticipating the magnitude of the crisis. We have also found some initial evidence that adding off-balance-sheet derivatives exposure improves the performance of the KMV model.