This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that...

This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that...

This paper develops a novel importance sampling algorithm for estimating the probability of large portfolio losses in the conditional independence framework. In a sense we modify the algorithm of Glasserman and Li (2005) so that it can be applied in a wider variety of models, including the t...

Li and Zhou (2006) established that an investor, following an unconstrained mean-variance strategy, will achieve its discounted targeted wealth with a probability greater than 80%. Surprisingly, we will show that under short-selling restrictions (i.e without the possibility of borrowing stocks)...