Since Markowitz published his seminal work on mean-variance portfolio selection in 1952, almost all literature in the past half century adhere their investigation to a binding budget spending assumption on this classical investment issue. In the mean-variance world for a market of all risky...

Robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance when making well-informed risk management decisions. In this paper, we quantify for any given distortion risk measure its robustness to distributional uncertainty by...

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...

In this paper we study the asset-liability management of an insurance company selling “participating contracts”. Participating contracts are typical insurance policies sold worldwide.The payoff of a participating policy is linked to the portfolio or the surplus of the insurance company. We...

We derive the optimal portfolio for an expected utility maximizer whose utility does not only depend on terminal wealth but also on some random benchmark (state-dependent utility). We then apply this result to obtain the optimal portfolio of a loss-averse investor with a random reference point...

We develop in this paper a novel portfolio selection framework with a feature of dual robustness in both return distribution modeling and portfolio optimization. While predicting the return distributions of the future market always represents the most compelling challenge in investment, any...

Model-based decisions are highly sensitive to model risk that arises from the inadequacy of the adopted model. This paper reviews the existing literature on model risk assessment and shows how to use the theoretical results to develop a corresponding best practice. Specifically, we develop tools...

We construct an algorithm that makes it possible to numerically obtain an investor's optimal portfolio under general preferences. In particular, the objective function and risks constraints may be driven by benchmarks (reflecting state-dependent preferences). We apply the algorithm to various...

We study optimal investment strategies under the objective of maximizing the Omega ratio, proposed by Keating and Shadwick (2002) as an alternative to the Sharpe ratio for performance assessment of investment strategies. We show that in a standard set-up of the financial market the problem is...