The bounds for risk measures of a portfolio when its components have known marginal distributions but the dependence among the risks is unknown are often too wide to be useful in practice. Moreover, availability of additional dependence information, such as knowledge of some higher-order...

Assuming that agents' preferences satisfy first-order stochastic dominance, we show how the Expected Utility paradigm can rationalize all optimal investment choices: the optimal investment strategy in any behavioral law-invariant (state-independent) setting corresponds to the optimum for an...

We investigate multiperiod portfolio selection problems in a Black & Scholes type market where a basket of 1 riskfree and m risky securities are traded continuously. We look for the optimal allocation of wealth within the class of 'constant mix' portfolios.First, we consider the portfolio...

We show that maximizing distortion risk measures over the set of distribution functions with given mean is equivalent to maximizing their concave counterpart. In the case of Value-at-Risk and Tail Value-at-Risk the equivalence also holds when adding information on higher moments

Yaari's dual theory of choice is the natural counterpart of expected utility theory. While the optimal payoff choice for an expected utility maximizer is well studied in the literature, substantially less is known about the optimal payoff for a Yaari investor. In the first part of the paper, we...

We first study mean-variance efficient portfolios when there are no trading constraints and show that optimal strategies perform poorly in bear markets. We then assume investors use a stochastic benchmark (linked to the market) as a reference portfolio. We derive mean-variance efficient...

In standard portfolio theories such as Mean-Variance optimization, Expected Utility Theory, Rank Dependent Utility Theory, Yaari's Dual Theory and Cumulative Prospect Theory, the worst outcomes for optimal strategies occur when the market declines (e.g, during crises), which is at odds with the...