We mentioned in Chapter 2 that the factor process X(t) in our models has two possible interpretations. Its components Xi(t) may represent observable data series, either financial data such as stock indices, bond yield spreads etc., or macroeconomic data such as GDP growth, employment data or...

In the preceding chapter we showed that in a model with Gaussian diffusion factors the asset allocation problem reduces, via the change of measure technique, to a controlled diffusion problem in the factor process, even though there are jumps in the asset price model. The problem can be handled...

An important feature of the diffusion-based models presented in Part I is that they can be solved analytically, and as such do not require additional work to get the optimal investment strategy and the value function (aside from solving a Riccati equation and a linear ODE)…

The following sections are included:IntroductionFinancial Market, Investment Portfolio and BenchmarkDynamic Programming and the Value FunctionExistence of a Classical (C1,2) Solution Under Affine Drift AssumptionsExistence of a Classical (C1,2) Solution Under Standard Control AssumptionsFund...