The predictive accuracy of credit ratings: measurement and statistical inference

Credit ratings are ordinal predictions for the default risk of an obligor. To evaluate the accuracy of such predictions commonly used measures are the Accuracy Ratio or, equivalently, the Area under the ROC curve. The disadvantage of these measures is that they treat default as a binary variable thereby neglecting the timing of the default events and also not using the full information from censored observations. We present an alternative measure that is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods that use more sample information and lead to more powerful tests than alternatives that filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S&P Long Term Credit Ratings. --ratings,predictive accuracy,Accuracy Ratio,Harrell's C,overlapping lifetimes

Multi-period credit default prediction with time-varying covariates

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy. --credit default,multi-period predictions,hazard models,panel data,out-of-sample tests

Global Excess Liquidity and House Prices - A VAR Analysis for OECD Countries

The belief that house prices are driven by specific regional and institutional variables and not at all by monetary conditions is so entrenched with some market participants and some commentators that the search for empirical support would seem to be a trivial task. However, this is not the case. This paper investigates the relationship between global excess liquidity and asset prices on a global scale:How important is global liquidity? How are asset (especially house) prices and other important macro variables like consumer prices affected by global monetary conditions? This paper analyses the international transmission of monetary shocks with a special focus on the effects of a global monetary aggregate ("global liquidity") on consumer prices and different asset prices.We estimate a variety of VAR models for the global economy using aggregated data that represent the major OECD countries. The impulse responses show that a positive shock to global liquidity leads to permanent increases in the global GDP deflator and in the global house price index, while the latter reaction is even more distinctive. Moreover, we find that there are subsequent spillovers to consumer prices. In contrast, we are not able to find empirical evidence in favour of the hypothesis that the MSCIWorld index as a measure of stock prices significantly reacts to changes in global liquidity.Global liquidity, inflation control, international spillovers, asset prices, VAR analysis

Multi-Period Credit Default Prediction - A Survival Analysis Approach

The book deals with the problem to estimate credit default probabilities under a flexible multi-period prediction horizon. Multi-period predictions are naturally desirable because the maturity of loans usually spans several periods. However, single-period models largely prevail in the literature so far due to their simplicity. Predicting over multiple periods indeed entails certain challenges that do not arise within a single-period view. Among the main contributions of this work to the literature is to show that there are relatively simple solutions to these challenges available. From a methodological point of view, a survival analysis approach is used. In a survival analysis context, the time until default (or lifetime) is the central variable under investigation as opposed to the traditional approach of reducing the information to a binary variable representing the default event. Modeling the time until default has the advantage that both the timing of default events and censored data are utilized. Since both issues gain importance as the prediction horizon grows it is no coincidence that a survival analysis approach is selected for the multi-period prediction problem. The main results of the work are the following. First, a new index for measuring the predictive accuracy of default predictions is proposed and its advantages over commonly used indices are shown both theoretically and by an empirical analysis. This is part of the second chapter which further includes new methods of statistical inference for the new index. In the third chapter, default prediction models for the case of panel datasets with time-varying covariates are dealt with. A new approach is developed that is simpler than the models available in the literature so far. In an empirical study concerning North American public firms, we provide evidence that the proposed approach delivers more accurate predictions than its competitors as well. In the final chapter, the problem of assigning default probability estimates to given rating grades is examined. If default events are rare, standard approaches have certain drawbacks. As an alternative, an empirical Bayes approach is presented that mitigates the effects of data sparseness. The new estimator is applied to a comprehensive sample of sovereign bonds. Among the main findings of the empirical part is that capital requirements for sovereign bonds are likely to be underestimated by using standard approaches but not when using the empirical Bayes estimator

Liquidity and the dynamic pattern of price adjustment: a global view

Global liquidity expansion has been very dynamic since 2001. Contrary to conventional wisdom, high money growth rates have not coincided with a concurrent rise in goods prices. At the same time, however, asset prices have increased sharply, significantly outpacing the subdued development in consumer prices. This paper examines the interactions between money, goods and asset prices at the global level. Using aggregated data for major OECD countries, our VAR results support the view that different price elasticities on asset and goods markets explain the recently observed relative price change between asset classes and consumer goods. --Global liquidity,inflation control,monetary policy transmission,asset prices

Multi-period credit default prediction with time-varying covariates.

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy

Multi-period credit default prediction with time-varying covariates.

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy

Multi-period credit default prediction with time-varying covariates.

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy

The predictive accuracy of credit ratings: Measurement and statistical inference

Credit ratings are ordinal predictions for the default risk of an obligor. To evaluate the accuracy of such predictions commonly used measures are the Accuracy Ratio or, equivalently, the Area under the ROC curve. The disadvantage of these measures is that they treat default as a binary variable thereby neglecting the timing of the default events and also not using the full information from censored observations. We present an alternative measure that is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods that use more sample information and lead to more powerful tests than alternatives that filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S\&P Long Term Credit Ratings