We present a robust Black-Litterman (BL) model that takes into account the possibility of model misspecification. In place of a single prior distribution, we utilize multiple priors around the estimated expected excess returns and covariance matrix. The model has two primary advantages over the...

Multifactor models and the construction of factor-matching portfolios are by now pervasive in investment management. In textbook discussions, the construction of factor-tracking portfolios is presented as a simple exercise solved using well-known optimization methods such as linear programming....

The Duffie--Lando (DL) credit risk model is based on the structural model developed by Leland and Toft, but modifies it by adding a noise term to the observation of firm value. This is done to reflect the fact that the asset process cannot be observed directly and thus that default times are...

We describe an extension to a Liquidity-Adjusted Value-at-Risk (LVaR) model originally developed by Bangia et al. (1999) that incorporates liquidity risk into the traditional VaR model using random bid-ask spreads. By applying the Hellinger distance measure, we show how the bid-ask spread can be...

We present a methodology for constructing robust credit default estimates using Bayesian mixture models. Robust models explicitly take parameter uncertainty into account by allowing the modeller to formally express his degree of confidence in the model he is using and thus generate new model...

We present a methodology for obtaining a valid correlation matrix from an invalid one for financial applications. In contrast to other approaches, the methodology described only requires the use of elementary matrix algebra and a simple randomization procedure.

The majority of pension plans today are underfunded, uncertain about the market, the economic and regulatory environment and searching for a more stable approach to meeting their pension benefit obligations. For plan sponsors who want to adopt a more structured approach to asset--liability...