Parsimonious exposure-at-default modeling for unfunded loan commitments

Purpose–The purpose of this paper is to build an easy to implement, pragmatic and parsimonious yet accurate model to determine an exposure at default (EAD) distribution for CCL (contingent credit lines) portfolios. Design/methodology/approach–Using an algorithm similar to the basic CreditRisk+ and Fourier Transforms, the authors arrive at a portfolio level probability distribution of usage. Findings–The authors perform a simulation experiment which illustrates the convolution of two portfolio segments to derive an EAD distribution, chosen randomly from Moody's Default Risk Service (DRS) database of CCLs rated as of 12/31/2008, to derive an EAD distribution. The standard deviation of the usage distribution is found to decrease as we increase the number of puts used, but the mean value remains relatively stable, as the extreme points converge towards the mean to produce a shrinkage in the spread of the distribution. The authors also observe, for the sample portfolio, that an increase in the additional usage rate level also increases the volatility of the associated exposure distribution. Practical implications–This model, in conjunction with internal bank financial institution research, can be used for banks' EAD estimation as mandated by Basel II for bank CCL portfolios, or implemented as part of a Solvency II process for insurers exposed to credit sensitive unfunded commitments. Apart from regulatory requirements, distributions of stochastic exposure generated can be inputs for different economic capital models and stress testing procedures used to capture an accurate risk profile of the portfolio, as well as providing better insights into the problem of managing liquidity risk for a portfolio of CCLs and similar exposures. Originality/value–In-spite of the large volume of CCLs in portfolios of financial institutions all (for commercial banks holding these as well as for insurance companies having analogous exposures), paucity of EAD models, unsuitability of external data and inconsistent internal data with partial draw-downs have been a major challenge for risk managers as well as regulators in managing CCL portfolios.