Common Failings: How Corporate Defaults are Correlated

We develop, and apply to data on U.S. corporations from 1979-2004, tests of the standard doubly-stochastic assumption under which firms'default times are correlated only as implied by the correlation of factors determining their default intensities. This assumption is violated in the presence of contagion or "frailty" (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the time-series properties of default intensities. The data do not support the joint hypothesis of well specified default intensities and the doubly-stochastic assumption. There is also some evidence of default clustering in excess of that implied by the doubly-stochastic model with the given intensities.

Validation of Default Probabilities

Well-performing default predictions show good discrimination and calibration. Discrimination is the ability to separate defaulters from nondefaulters. Calibration is the ability to make unbiased forecasts. I derive novel discrimination and calibration statistics to verify forecasts expressed in terms of probability under dependent observations. The test statistics' asymptotic distributions can be derived in analytic form. Not accounting for cross correlation can result in the rejection of actually well-performing predictions, as shown in an empirical application. I demonstrate that forecasting errors must be serially uncorrelated. As a consequence, my multiperiod tests are statistically consisten

Performance of default-risk measures: the sample matters

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 within-sample 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.This paper has been possible thanks to the SANFI Research Grant for Young Researchers Edition 2015, the financial support from the Spanish Ministry of Economy, Industry and Competitiveness (ECO2016-77631-R (AEI/FEDER, UE)) and the Spanish Ministry of Science and Innovation (PID2019-104304GB-I00/AEI/10.13039/501100011033). Ana González Urteaga particularly acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through grant PGC2018-095072-B-I00

Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression

Developing a good prepayment model is a central task in the valuation of mortgages and mortgage-backed securities but conventional parametric models often have bad out-of-sample predictive ability. A likely explanation is the highly non-linear nature of the prepayment function. Non-parametric techniques are much better at detecting non-linearity and multivariate interaction. This article discusses how non-parametric kernel regression may be applied to loan level event histories to produce a better parametric model. By utilizing a parsimonious specification, a model can be produced that practitioners can use in valuation routines based on Monte Carlo interest rate simulation.

Modelling credit risk for innovative firms: the role of innovation measures

Financial constraints are particularly severe for R&D projects of SMEs, which cannot generally rely on equity markets and, in the EU, on a sufficiently developed VC industry. If innovative SMEs have to depend on banks to finance their R&D projects, it is particularly important to develop models able to estimate their probability of default (PD) in consideration of their peculiar features. Based on the signaling value of some innovative assets, the purpose of this paper is to show the importance to include them into models which have proved to be successful for SMEs. To this end, we take a logit model and test it on a unique dataset of innovative SMEs (based on PATSTAT database, EPO BULLETIN and AMADEUS) to estimate a two-year PD with default years 2006-2008. In the regression analysis the innovation-related variables are two in order to account for R&D productivity at the level of the firm and to consider the value of the inventive output. Our analyses first address measurement issues concerning innovation-related variable and then show that, while the accounting variables and the patent value are always significant with the expected sign, the patent number per se reduces the PD only in the presence of an appropriate equity level.innovative SMEs; default probability; patent value