We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable.

In an environment with stocks and short-term debt, random changes in the risk-reward frontier produce hedging demands for equities, implying that portfolio policies supporting optimal life-cycle consumption are rarely mean-variance efficient. Pursuing optimal life-cycle portfolio policies is...

Traditional portfolio optimization approaches suffer from the drawback of often leading to highly concentrated portfolios. We propose a new kind of optimization focusing on a homogeneous distribution of risk among the portfolio constituents. We describe the underlying ideas of the approach and...

We give a functional description of the space of stochastic integrals with respect to a given family of martingales, based on the notion of direct integral of Hilbert spaces. We define the multiplicity function of a filtration and show, using our Hilbert space construction, that the multiplicity...

We propose a new modeling framework to study the asset pricing implications of learning under ambiguity aversion. In a continuous time partial information Lucas economy, we characterize analytically equilibrium equity returns and make the following observations. First, learning under ambiguity...

We solve analytically the Merton's problem of an investor with timeadditive power utility. For general state dynamics, we prove existence of two power series representations of the relevant optimal policies and value functions, which hold for all admissible risk aversion parameters. We...

We analyze the empirical predictions arising from settings of ambiguity aversion in intertemporal heterogenous agents economies. We study equilibria for two tractable wealth-homothetic settings of ambiguity aversion in continuous time. Such settings are motivated by a different robust control...