The asset allocation is a practical problem for most institutional and private investors, who routinely deal with a wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over time. Many studies find that the expected returns have...

We examine asset prices in a representative-agent model of general equilibrium. Assuming only that individuals are risk averse, we determine conditions on the changes in asset risk that are both necessary and sufficient for the asset price to fall. We show that these conditions neither imply,...

This paper examines real-time applications of quickest disorder detection techniques for timing stock markets. The focus is on the stochastic disorder model by Shiryaev, Zhitlukhin, and Ziemba (2014, 2015), Zhitlukhin and Ziemba (2016) and their optimal stopping rule. The model uses sequential...

This paper studies the pricing of volatility risk using the first-order conditions of a long-term equity investor who is content to hold the aggregate equity market rather than overweighting value stocks and other equity portfolios that are attractive to short-term investors. We show that a...

We develop and implement linear formulations of general N-th order Stochastic Dominance criteria for discrete probability distributions. Our approach is based on a piece-wise polynomial representation of utility and its derivatives and can be implemented by solving a relatively small system of...

In this paper, we offer a MD (Minimum Discrepancy) reformulation of the estimation and inference problem that arises in SD analysis, delivering a method that retains the desirable properties of optimal GMM while offering better higher order ones and, most importantly, without requiring the...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

Recent empirical work documents large liquidity risk premiums in stock markets. We calculate the liquidity risk premiums demanded by large investors by solving a dynamic portfolio choice problem with stochastic price impact of trading, CRRA utility and a time-varying investment opportunity set....