We provide a representation for the nonmyopic optimal portfolio of an agentconsuming only at the terminal horizon when the single state variable follows ageneral diusion process and the market consists of one risky asset and a risk-freeasset. The key term of our representation is a new object...

A random variable dominates another random variable with respect to the covariance order if the covariance of any two monotone increasing functions of this variable is smaller. We characterize completely the covariance order, give strong sufficient conditions for it, present a number of examples...

We provide a representation for the nonmyopic optimal portfolio of an agent consuming only at the terminal horizon when the single state variable follows a general di usion process and the market consists of one risky asset and a risk-free asset. The key term of our representation is a new...

We study the market price of risk, the stock volatility and the hedging behavior in equilibrium of heterogeneous agents with arbitrary utility functions, consuming only at the end of the time horizon, and with the state variable following an arbitrary homogeneous diffusion process. We introduce...

We develop an overlapping generations model in which investors differ in their investment horizons. In equilibrium, the intertemporal hedging demand of longer horizon investors leads to a two-factor capital asset pricing model (CAPM) in which risk premiums are determined by both the market...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We solve the problem of optimal risk management for an investor holding an illiquid, alpha generating fund and hedging his position with a liquid futures contract. When the investor is subject to a drawdown constraint, he is forced to reduce the total risk of his portfolio after a drawdown. In...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...