The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from...

Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail-correlation matrices based on Value-at-Risk (VaR) estimates. We demonstrate how to obtain more efficient...

In spite of their importance, third or higher moments of portfolio returns are often neglected in portfolio construction problems due to the computational difficulties associated with them. In this paper, we propose a new robust mean–variance approach that can control portfolio skewness and...

In this note we show the following result of Dybvig (1995) is valid for a general von Neumann–Morgenstern utility function: for an agent who does not tolerate a decline in consumption, the optimal investment out of discretionary wealth (in excess of the perpetuity value of current consumption)...

Most socially responsible investment funds combine a sustainability objective with a tracking error constraint. We characterize the impact of a sustainability constraint on the mean-tracking error efficient frontier and illustrate this on a universe of US stocks for the period 2003–2010.

Generating a high positive excess return in a prospective period does not necessarily increase the empirical Sharpe ratio of an investment fund. Therefore, we derive a critical range in which prospective excess returns must lie in order to increase its empirical Sharpe ratio. We also give a...

This paper proposes a new dividend-based S&P 500 Index return predictor, the implied dividend yield term structure (IDYTS). We show that the IDYTS is a “cleaner” predictor than its conventional counterpart, the dividend price ratio (DP), in that the expected return is a linear combination...