In this issue we present four research papers.
The first paper is "An analytical value-at-risk approach for a credit portfolio with liquidity horizon and portfolio rebalancing", by Haohan Huang, EugeneWang, Huaxiong Huang andYongWang. The authors provide a two-period analytical value-at-risk approach for credit portfolios with a liquidity horizon and a constant level of risk. Given any time horizon, a two-period credit portfolio loss model is derived and, at the end of the first period, the portfolio is rebalanced to ensure that it has a constant level of risk as measured by the credit rating. By testing with the Monte Carlo simulation model, it is shown that the accuracy of the analytic model is acceptable over a large range of parameter values.
The issue's second paper, "Loss distributions: computational efficiency in an extended framework" by Daniel H. Stahl, contributes to the credit modeling literature for mixture models by leveraging an efficient algorithm for computing the density function of the loss distribution and extending the model in two key areas: construction of the systemic variable from a continuous-time process and introduction to semiendogenous liquidity risk. This generalization allows for time-dependent portfolios, fully accounts for granularity and concentration within the credit portfolio, and does not rely on assumptions that large credit portfolios are asymptotic.
In the third paper, "Default risk of money-market fund portfolios", Matulya Bansal addresses the problem of quantifying the risk associated with money-market fund (MMF) portfolios. Ever since the Reserve Primary Fund "broke the buck" in 2008, credit risk in MMFs has become an issue of great interest. Different proposals have been proposed to prevent a run on MMFs: for example, the use of capital buffers, liquidity fees and floating net asset values. But very little work has been done on actually measuring the risk. By focusing on default risk - which is the most material driver of portfolio losses - and by using a constant level of risk assumption, the author shows how this problem is similar to that of pricing a collateralized debt obligation (CDO). The author develops a semi-analytical approach to measuring the default risk of MMF portfolios. On using this model to evaluate the portfolios of three of the largest prime MMFs, the author finds that they vary considerably in their default risk.
The fourth and final paper in this issue, "Are all collections equal? The case of medical debt" by Kenneth P. Brevoort and Michelle Kambara, examines the predictive value of medical collections in assessing consumer creditworthiness with credit scoring models. The authors find two main results. First, they find that medical collections are less informative about a consumer's likelihood of delinquency than are nonmedical collections. Second, they find that medical collections that have been paid in full are less predictive than medical collections that remain unpaid.
Federal Reserve Board
An analytical value-at-risk approach for a credit portfolio with liquidity horizon and portfolio rebalancing
The authors provide a two-period analytical value-at-risk approach for credit portfolios with a liquidity horizon and a constant level of risk.
This paper contributes to the literature for mixture models by leveraging an efficient algorithm for computing the density function of the loss distribution and extending the model in two key areas: constructing the systemic variable from a continuous…
This paper proposes a semi-analytic approach to quantify the default risk associated with Money-Market Fund (MMF) portfolios.
This paper examines the predictive value of medical collections in assessing consumer creditworthiness with credit scoring models.