Welcome to the June issue of The Journal of Credit Risk.
The first paper in this issue, ‘Portfolio credit risk model with extremal dependence of defaults and random recovery’ by Jong-June Jeon, Sunggon Kim and Yonghee Lee, deals with the topic of portfolio credit risk modelling, taking into account the extremal dependence of defaults and random recoveries. The authors propose to model a credit portfolio using a factor copula model with random recovery rates and develop an efficient importance sampling conditional Monte Carlo in order to compute the loss probability. The authors find the proposed Monte-Carlo simulation algorithm is relatively efficient compared to the plain Monte-Carlo simulation.
‘Primary-Firm-Driven Portfolio Loss’ by Stuart Turnbull deals with the problem of how the default of a primary firm can trigger the default of other (dependent) secondary firms. The author suggests a simple Gaussian latent factor model to quantify the impact the default of a primary firm has on corresponding secondary firms. The paper shows that failure to account for such dependence can result in the value-at-risk and the expected shortfall being greatly under estimated.
In the issue’s final paper, ‘Adapting the Basel II advanced internal-ratings-based models for International Financial Reporting Standard 9’, Peter Miu and Bogie Ozdemir examine how we may use A-IRB models in the estimation of expected credit losses for IFRS 9 purposes. The authors highlight the necessary model adaptations required to satisfy the new accounting standard of impairment measurement. By leveraging the A-IRB models, banks can lessen their modeling efforts in fulfilling IFRS 9 and capture the synergy among different modelling endeavours within the institutions.
This paper proposes a portfolio credit risk model with random recovery rates.
This paper describes a simple model that can be used for risk management.
Adapting the Basel II advanced internal-ratings-based models for International Financial Reporting Standard 9
This paper examines how we may use A-IRB models in the estimation of expected credit losses for IFRS 9 purposes.