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.