We propose an iterative gradient-based algorithm to efficiently solve the portfolio selection problem with multiple spectral risk constraints. Since the conditional value at risk (CVaR) is a special case of the spectral risk measure, our algorithm solves portfolio selection problems with...

In the standard approach to fund valuation, it is often assumed that markets are perfectly liquid and hence assets have unique prices. In practice, however, as has been widely documented, this is not the case. Asset values are impacted by deterioration of market liquidity (market depth)....

In the existing literature, the value-at-risk (VaR) is one of the most representative downside risk measures due to its wide spectra of applications in practice. In this paper, we investigate the dynamic mean-VaR portfolio selection formulation, while the state-of-the-art has only witnessed...

We study portfolio selection in a one-period financial market with an Expected Shortfall (ES) constraint. Unlike in classical mean-variance portfolio selection, it can happen that no efficient portfolios exist. We call this situation regulatory arbitrage and show that the presence or absence of...

Recently, a lot of attention has been focused on developing portfolio allocation models that take into account the asymmetric nature of asset return distributions. In this paper, we extend Krokhmal, Palmquist, and Uryasev's approach by using CVaR-like constraints in the traditional portfolio...

We propose a method for optimal portfolio selection built on the Black and Litterman model and with two major contributions. We introduce in the investors' objective function a risk measure named expected tail loss, which is useful in portfolio selection context as it supports the benefi ts of...

The recent crisis made it evident that replicating the performance of a benchmark is not a sufficient goal to meet the expectations of usually risk-averse investors. The manager should also consider that the investor are seeking for a downside protection when the benchmark performs poorly and...

We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates based on limited historical return data are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty...