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...

This paper presents an equilibrium model in a pure exchange economy when investors have three possible sources of heterogeneity. Investors may differ in their beliefs, in their level of risk aversion and in their time preference rate. We study the impact of investors heterogeneity on the...

We study survival, price impact and portfolio impact in heterogeneous economies. We show that, under the equilibrium risk-neutral measure, long-run price impact is in fact equivalent to survival, whereas longrun portfolio impact is equivalent to survival under an agent-specific, wealth-forward...

We introduce a new class of momentum strategies, the risk-adjusted time series momentum (RAMOM) strategies, which are based on averages of past futures returns, normalized by their volatility. We test these strategies on a universe of 64 liquid futures contracts and show that RAMOM strategies...

Despite the vast academic literature on modelling stochastic volatility, many finance practitioners still use the simple "RiskMetrics" approach of J. P. Morgan (1997), based on the exponentially weighted moving average (EWMA) volatility combined with the $\sqrt{h}$-rule for scaling volatility...

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 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...