Despite much work on hedging in incomplete markets, the literature still lacks tractable dynamic hedges in plausible environments. In this article, we provide a simple solution to this problem in a general incomplete-market economy in which a hedger, guided by the traditional minimum-variance...

This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial...

We apply perturbation theory to solve the optimal control problem of an investor with time-additive power utility over intermediate consumption and final wealth. Under general conditions we show existence of a power series representation for the prevailing optimal consumption and investment...

This paper extends Merton’s continuous time (instantaneous) mean-varianceanalysis and the mutual fund separation theory. Given the existence of a Marko-vian state price density process, the optimal portfolios from concave utility max-imization are instantaneously mean-variance efficient...

We study in a general perspective the partial equilibrium incentives and the general equilibrium asset pricing implications of Value-at-Risk (VaR) regulation in continuous time economies with intermediate consumption, stochastic opportunity set, and heterogenous attitudes to risk. Our findings...

We apply perturbation methods to solve in closed form a class of robust control problems, implied by Anderson, Hansen and Sargent setting of a preference for robustness. In the constant investment opportunity set case, we obtain closed form power series solutions for the arising robust Bellman...

We present a geometric approach to discrete time multiperiod mean variance portfolio opti-mization that largely simplifies the mathematical analysis and the economic interpretation of such model settings. We show that multiperiod mean variance optimal policies can be decom-posed in an orthogonal...

We use an expected utility framework to integrate the hedge funds survival uncertainty into an asset allocation optimizartion model. The addition of investment constraints complicates the resolution of the optimal allocation problem. It is solved using a genetic algorithm that mimics the...

We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is...

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the...