In this paper, saddle point techniques are used in the computation of risk measures for large mark-to-market credit portfolios with stochastic recovery and correlation between obligors depending on the state of the economy.The authors compare the efficiency of the saddle point approach with existing methods such as plain Monte Carlo simulation and large deviation theory. By measuring run time and accuracy of calculations of the value-at-risk and the conditional value at-risk for different significance levels they analyze the quality of these approximation approaches. Furthermore, the approximation quality over the whole portfolio loss distribution function is analyzed. The results show that the saddle point approximation performs not only very quickly but also very accurately over the whole loss distribution function. This result is not limited to large portfolios and can also be achieved for small portfolios.