Journal of Credit Risk

In this issue of The Journal of Credit Risk we present two full-length research papers and two technical reports.

The first paper, "Two models of stochastic loss given default", is by Simone Farinelli and Mykhaylo Shkolnikov. The paper proposes two structural models of stochastic loss given default (LGD) for modeling portfolio credit losses. The first model suggests random parameters for beta distribution with matching mean LGD, and the second model utilizes fixed parameters with a symmetric density support around mean LGD. A calibration methodology is also provided and applied to the historical data.

The second research paper is "Double exponential jump diffusion processes: a structural model of an endogenous default barrier with a rollover debt structure" by Binh Dao and Monique Jeanblanc. The authors present a new and useful structural model of debt that includes jumps and that therefore allows for nonnegligible credit spreads at short maturities, as opposed to what is predicted in the seminal papers of Merton, Longstaff and Schwartz, Leland and others. This model allows many stylized features of real-world markets to be recovered.

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.

The first technical report, "Impact of factor models on portfolio risk measures: a structural approach", is by Marcos Escobar, Tobias Frielingsdorf and Rudi Zagst. This is an interesting comparison paper in which several factor models are compared on their capability to capture heavy-tailed phenomena in the computation of risk measures like value-at-risk (VaR) and lower tail dependence. The authors compare linear and nonlinear factor models, like the Gaussian factor model, the double Student t model, the normal inverse Gaussian factor model, the stochastic correlation factor model and the simple Student t factor model. The models are unified, in the sense that the same covariance matrix is used for all models. Furthermore, they are calibrated, after which the VaR and lower tail dependence are computed for a portfolio consisting of three companies rated Aa, Baa and Ba.

The second technical report is "Modeling exposure at default and loss given default: empirical approaches and technical implementation" by Bill Huajian Yang and Mykola Tkachenko. The paper briefly surveys current modeling methodologies and then proposes some empirical approaches and provides technical insights into their implementation.

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here