Nonlinear portfolio views: an efficient extension to the Black-Litterman approach

Two efficient approximate methods for optimal (Bayesian) blending of views on portfolios of assets with nonlinear payoff profiles are introduced. The idea is based on the observation that the application of the Black-Litterman model, with respect to market views, is equivalent to the measurement update in the linear filtering problem in engineering and statistics. The approaches suggested here are motivated by results from nonlinear filtering theory. In particular it is shown that the simple Gaussian framework can be approximately maintained, despite of nonlinear relations with the respective risk factors, by using the extended Kalman filter update or the unscented transform. Both methods are well suited for high dimensional problems from a computational point of view, and can thus be applied to large portfolios of derivatives. © 2015, Springer-Verlag Berlin Heidelberg.