Journal of Risk

Risk.net

Compound scenarios: an efficient framework for integrated market–credit risk

Ben De Prisco, Ian Iscoe, Yijun Jiang, Helmut Mausser

ABSTRACT

This paper describes an efficient three-tiered scenario generation framework for assessing joint market–credit risk with Monte Carlo simulation. The framework employs a set of so-called compound scenarios, having a tree-like structure comprising of three levels or tiers. The scenarios underlie correlated market and systemic credit factors and independent, idiosyncratic, credit risk factors. We obtain confidence intervals for the mean and quantiles of a portfolio loss distribution in the non-independent and identically distributed setting of compound scenarios. The confidence intervals derive from the asymptotic normality of the sample mean and sample quantile and a variance decomposition formula that expresses the asymptotic variance in terms of the number of samples from each of the three tiers. The resultant formula directly measures the impact of various sample sizes on the variance of the estimate, which is more efficient than the trial-and-error approach used by practitioners today. Moreover, the formula allows one to optimize the choice of sample sizes and to minimize the estimator’s variance subject to constraints on available time or computer resources. The results are illustrated with some numerical and empirical examples.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

If you already have an account, please sign in here.

You need to sign in to use this feature. If you don’t have a Risk.net 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: