Journal of Credit Risk

In this issue of The Journal of Credit Risk we present three research papers and one technical report.

The first paper in this issue is "Collateral and credit issues in derivatives pricing" by John Hull and Alan White. The main contribution of this paper is to describe the connection between recent work in the derivatives pricing literature that focuses on collateral and credit issues, and traditional risk-neutral pricing. The paper uses equilibrium and no-arbitrage arguments to motivate these results. Valuation partial differential equations are derived for derivatives with counterparty risk and collateral (both the capital asset pricing model version and the hedging version). The authors also argue that funding value adjustment should not be included in the valuation and they conclude by considering the situation in which the issuer requires an excess return on the derivative.

Our second paper is "Risk analysis probability of default: a stochastic simulation model" by Giuseppe Montesi and Giovanni Papiro. The authors evaluate a proposed simulation process for estimating a firm's probability of default. They then compare the probability of default results with two well-known alternative techniques. The authors' risk analysis probability of default method is shown to compare favorably with Altman's Z-score model and the Moody's KMV approach.

In the issue's third research paper, "Efficient Monte Carlo counterparty credit risk pricing and measurement" by Samim Ghamami and Bo Zhang, an efficient Monte Carlo counterparty credit risk (CCR) estimation framework is developed. They focus on widely used CCR measures like credit value adjustment, expected positive exposure and effective expected positive exposure. The efficient Monte Carlo estimators proposed by the authors outperform existing crude estimators of the CCR measures.

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 and provide a lot of value to practitioners.

This issue's technical report is "The large homogeneous portfolio approximation with two-factor Gaussian copula and random recovery rate" by Geon Ho Choe and Soon Won Kwon. The authors propose a large homogeneous portfolio approximation that builds on an idea of Vasicek (2002) that was extended to cover stochastic recovery by Andersen and Sidenius (2004). The portfolio dependence is a two-factor Gaussian factor model. The recovery rate is stochastic and depends on one of the Gaussian factors. The results are applied to a case study on the pricing of collateralized debt obligations.

REFERENCES
Andersen, L., and Sidenius, J. (2004). Extensions to the Gaussian copula: random recovery and random factor loadings. The Journal of Credit Risk 1(1), 29-70.

Burtschell, X., Gregory, J., and Laurent, J.-P. (2007). Beyond the Gaussian copula: stochastic and local correlation. The Journal of Credit Risk 3(1), 31-62.

Vasicek, O. A. (2002). Loan portfolio value. Risk 15(12), 160-162.

Ashish Dev
JPMorgan Chase, New York