We propose a simple approach to combining internal and external loss data in the case when internal and external data come from the same distribution. We assume that the internal data is uncensored but the external data includes only losses above a known threshold. This approach is an...

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model with arbitrary accuracy using Hermite expansions. No assumptions about homogeneity of the portfolio are made. The algorithm is a generalization of the algorithm proposed in \cite{PO}. The advantage...

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model. We test it on a 125 name portfolio with a single factor Gaussian model and show that the algorithm gives accurate results. We choose a 125 name portfolio for our tests because this is the size of...

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model. We test it on a 125 name portfolio with a single factor Gaussian model and show that the algorithm gives accurate results. We choose a 125 name portfolio for our tests because this is the size of...

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model. We test it on a 125 name portfolio with a single factor Gaussian model and show that the algorithm gives accurate results. We choose a 125 name portfolio for our tests because this is the size of...

We propose a fast algorithm for computing the economic capital, Value at Risk and Greeks in the Gaussian factor model. The algorithm proposed here is much faster than brute force Monte Carlo simulations or Fourier transform based methods. While the algorithm of Hull-White is comparably fast, it...

We propose a fast algorithm for computing the expected tranche loss in the Gaussian factor model with arbitrary accuracy using Hermite expansions. No assumptions about homogeneity of the portfolio are made. The algorithm is a generalization of the algorithm proposed in \cite{PO}. The advantage...

We propose a simple approach to combining internal and external loss data in the case when internal and external data come from the same distribution. We assume that the internal data is uncensored but the external data includes only losses above a known threshold. This approach is an...