Using Hermite Expansions for Fast and Arbitrarily Accurate Computation of the Expected Loss of a Loan Portfolio Tranche in the Gaussian Factor Model

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 of the new algorithm is that it allows us to achieve higher accuracy in almost the same computational time. It is intended as an alternative to the much slower Fourier transform based methods \cite{MD}.Gaussian factor model, Gaussian copula model, loan portfolio, CDO, DJCDX, CDO tranche loss, portfolio tranche loss, expected loss

A Simple Approach to Combining Internal and External Operational Loss Data

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 alternative to the method of Baud et al. \cite{BA1}, when the latter is too computationally expensive due to the large quantity of data available.operational risk, basel accords, combining internal and external data, stratified sampling, weighted average

Fast Computation of the Economic Capital, the Value at Risk and the Greeks of a Loan Portfolio in the Gaussian Factor Model

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 assumes that all the loans in the portfolio have equal notionals and recovery rates. This is a very restrictive assumption which is unrealistic for many portfolios encountered in practice. Our algorithm makes no assumptions about the homogeneity of the portfolio. Additionally, it is easier to implement than the algorithm of Hull- White. We use the implicit function theorem to derive analytic expressions for the GreeksEconomic capital, gaussian factor model, value at risk, unexpected loss, fast algorithm

Predictability of Equity REIT Returns: Implications for Property Tactical Asset Allocation

This study presents further evidence of the predictability of excess equity REIT (real estate investment trust) returns. Recent evidence on forecasting excess returns using fundamental variables has resulted in diminishing returns from the 1990’s onward. Trading strategies based on these forecasts have not significantly outperformed the buy/hold strategy of the 1990’s. We have developed an alternative strategy that is based on the time variation of the risk premium of investors. Our results indicate that it is possible to outperform the buy/hold strategy by modeling the time variation of the risk premium. By modeling the dynamic behavior of the risk premium, we are able to implicitly capture economic risk premiums that are not captured by conventional multi beta asset pricing models.Equity REIT; Predictability; Risk premium