Models for Credit risk in Static Portfolios

Abstract

In this thesis we investigate models for credit risk in static portfolios. We study Vasicek's closed form approximation for large portfolios with the mixed binomial model using the beta distribution and a two-factor model inspired by Merton as mixing distributions. For the mixed binomial model we estimate Value-at-Risk using Monte-Carlo simulations and for the one-factor model inspired by Merton we analytically calculate Value-at-Risk, using Vasicek's large portfolio approximation. We find that the mixed binomial beta model and Vasicek's large portfolio approximation yields similar results. Furthermore, we find that Value-at-Risk is lower in the two-factor model than in the one-factor model, but when the loss given default depends on the factors the results are mixed. However, when the factors are positively correlated, Value-at-Risk is higher in the two-factor model than in Vasicek's large portfolio approximation.