In this issue of The Journal of Credit Risk we present three research papers and one technical report.
Our first research paper is "Recovery rate risk and credit spreads in a hybrid credit risk model" by Mathieu Boudreault, Geneviève Gauthier and Tommy Thomassin.The paper proposes a reduced-form credit risk model in which both the default intensity and the recovery rate are driven by a Markov process. The innovative feature of the model is that it uses link functions for both the recovery and the default intensity that are inspired by structural credit risk models. The main insight of the authors is that the structural framework naturally gives rise to nonlinearity in the link functions and to correlation between default intensity and recovery rates. The authors then proceed to estimate the model using the Kalman filter and credit default swap spreads at various maturities.
The issue's second paper, "Markov chain Monte Carlo estimation of default and recovery: dependent via the latent systematic factor" by Xiaolin Luo and Pavel V. Shevchenko, addresses the problem of estimating economic capital when a systematic dependence between defaults and recoveries is present. The authors present three models that describe systematic risk in recoveries: Frye (2000), Pykhtin (2003) and Düllmann and Trapp (2004). The authors choose the first of these three models - the Frye model - and attempt to quantify the effect of model parameter uncertainty on the economic capital estimate produced by the model via Bayesian inference. The authors apply the Markov chain Monte Carlo algorithm to generate the posterior distributions of the model parameters.
The third research paper in this issue is "Sample selection bias in acquisition credit scoring models: an evaluation of the supplemental-data approach" by Irina Barakova, Dennis Glennon and Ajay Palvia. The paper uses a national sample of credit bureau data to examine sample selection bias in account acquisition scoring models and to evaluate the effectiveness of the industry practice of using proxy payment performance for rejected applicants. The results suggest that ignoring the rejected applicants significantly affects the forecast accuracy of credit scores but has little effect on their discriminatory power. The paper also documents the fact that validating scores on only accepted applicants can be misleading.
A technical report describes a particular practical technique and enumerates situations in which it works well and others in which it does not. Such reports provide extremely useful information to practitioners in terms of saved time and minimizing duplication of effort. The contents of technical reports complement rigorous conceptual and model developments presented in the research papers. A technical report can be a useful survey article as well.
This issue's technical report, "Pricing of contingent convertibles under smile conform models" by José Manuel Corcuera, Jan de Spiegeleer, Albert Ferreiro-Castilla, Andreas E. Kyprianou, Dilip B. Madan and Wim Schoutens, proposes a sophisticated approach to the pricing of contingent convertible (CoCo) bonds. In the aftermath of the financial crisis, CoCo bonds became highly relevant to bank financing. Once a prespecified negative event happens, eg, the bank no longer satisfies regulatory capital rules, the interest rate liability is converted into equity, which mitigates the severity of financial distress. In order to price this instrument, the authors propose a beta variance gamma process. In particular, they combine properties of beta processes and Monte Carlo techniques to accomplish effective numerical pricing of CoCo bonds. They calibrate their model against both their own beta variance gamma process and the often-used Black-Scholes framework in order to study price deviations. For the calibration, information about the stock price and the CoCo bond term sheets is augmented with information about credit default swaps in order to capture market prices for extreme risks.
Düllmann, K., and Trapp, M. (2004). Systematic risk in recovery rates: an empirical analysis of US corporate credit exposures. Deutsche Bundesbank Discussion Paper, Series 2, Banking and Financial Studies, pp. 1-44.
Frye, J. (2000). Collateral damage. Risk 13(4), 91-94.
Pykhtin, M. (2003). Unexpected recovery risk. Risk 16(8), 74-78.
JPMorgan Chase, New York
Markov chain Monte Carlo estimation of default and recovery: dependent via the latent systematic factor
Sample selection bias in acquisition credit scoring models: an evaluation of the supplemental-data approach