Dessislava A. Pachamanova

Robust multiperiod portfolio management in the presence of transaction costs

We study the viability of different robust optimization approaches to multiperiod portfolio selection. Robust optimization models treat future asset returns as uncertain coefficients in an optimization problem, and map the level of risk aversion of the investor to the level of tolerance of the total error in asset return forecasts. We suggest robust optimization formulations of the multiperiod portfolio optimization problem that are linear and computationally efficient. The linearity of the optimization problems is an advantage when complex additional requirements need to be imposed on the portfolio structure, e.g., limitations on positions in certain assets or tax constraints. We compare the performance of our robust formulations to the performance of the traditional single period mean-variance formulation frequently employed in the financial industry.

Constructing Risk Measures from Uncertainty Sets

We illustrate the correspondence between uncertainty sets in robust optimization and some popular risk measures in finance and show how robust optimization can be used to generalize the concepts of these risk measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can construct coherent risk measures and address the issue of the computational tractability of the resulting formulations. Our results have implications for efficient portfolio optimization under different measures of risk.

Robust strategies for facility location under uncertainty

This paper considers a stochastic facility location problem in which multiple capacitated facilities serve customers with a single product, and a stockout probabilistic requirement is stated as a chance constraint. Customer demand is assumed to be uncertain and to follow either a normal or an ambiguous distribution. We study robust approximations to the problem in order to incorporate information about the random demand distribution in the best possible, computationally tractable way. We also discuss how a decision maker’s risk preferences can be incorporated in the problem through robust optimization. Finally, we present numerical experiments that illustrate the performance of the different robust formulations. Robust optimization strategies for facility location appear to have better worst-case performance than nonrobust strategies. They also outperform nonrobust strategies in terms of realized average total cost when the actual demand distributions have higher expected values than the expected values used as input to the optimization models.

Handling Parameter Uncertainty in Portfolio Risk Minimization

Portfolio allocation decisions are frequently made according to optimization algorithms that treat parameters such as means, variances, and covariances of returns as given. These parameters, however, are estimated through error-prone procedures like statistical modeling or subjective evaluation. Robust optimization has become a way to incorporate uncertainty directly into the formulation of optimization problems. The technique can be applied to modeling uncertainty in the expected returns in portfolio shortfall minimization. Tests using simulated and real market data suggest that portfolio allocation strategies resulting from robust optimization formulations outperform strategies obtained using classic optimization methods.

Robust investment decisions under supply disruption in petroleum markets

Energy-dependent economies and energy security strategies need to cope with oil and gas supply disruptions that are rare but persistent and can be financially catastrophic. This paper proposes a tractable approach for determining robust investment strategies in petroleum markets under the risk of supply disruption when asset prices follow geometric mean-reverting jump processes. The robust counterpart of the portfolio management problem under supply disruption is derived for several symmetric and asymmetric representations of the uncertainties in the problem. Computational experiments with real market data indicate that the robust optimization approach using uncertainty sets tailored to the characteristics of the data results in strategies with superior worst-case performance.

Skewness‐Aware Asset Allocation: A New Theoretical Framework and Empirical Evidence

This paper presents a new measure of skewness, skewness‐aware deviation, that can be linked to prospective satisficing risk measures and tail risk measures such as Value‐at‐Risk. We show that this measure of skewness arises naturally also when one thinks of maximizing the certainty equivalent for an investor with a negative exponential utility function, thus bringing together the mean‐risk, expected utility, and prospective satisficing measures frameworks for an important class of investor preferences. We generalize the idea of variance and covariance in the new skewness‐aware asset pricing and allocation framework. We show via computational experiments that the proposed approach results in improved and intuitively appealing asset allocation when returns follow real‐world or simulated skewed distributions. We also suggest a skewness‐aware equivalent of the classical Capital Asset Pricing Model beta, and study its consistency with the observed behavior of the stocks traded at the NYSE between 1963 and 2006.

Recent Trends in Equity PortfolioConstruction Analytics

Portfolio analytics is critical for identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring portfolio risk and performance. Analytics-based portfolio management lets investment managers filter information quickly, take advantage of statistical arbitrage opportunities, and deal with inefficiencies, such as transaction costs incurred during trading and tax consequences of investment decisions. This article reviews some widely used approaches to portfolio analytics, discussing new trends in metrics, modeling approaches, and portfolio analytics system design. Topics include stock screening, text analytics, traditional and new uses for factor models, investment methodologies of recent interest (such as smart beta), new visualization and analytics features available from vendors and open source software, and cloud-based solutions for data management and analysis.

An optimization-based connection admission control method for IEEE 802.16 wireless networks

The IEEE 802.16 standard specifies the air interface of broadband wireless access (BWA) systems supporting multimedia services. The applications are varied in their nature and demand different performance levels in order to maintain the appropriate quality of service (QoS). The network should guarantee the pre-defined QoS requirements through an adequate resource allocation and admission control schemes. In this paper we propose a connection admission control method that takes into consideration specific QoS parameters of the ongoing connections and the new requests. The method is based on an optimization approach where the maximum allowed mean delay and the mean data queue lengths corresponding to the connections are used as decision criteria for accepting the new requests for service. The solution of the defined optimization problem also gives the optimal resource allocation. Since solving the optimization problem for a large number of connections can be time consuming, we propose a simple and practical algorithm for obtaining the optimal solution.

A robust asset---liability management framework for investment products with guarantees

This paper suggests a robust asset–liability management framework for investment products with guarantees, such as guaranteed investment contracts and equity-linked notes. Stochastic programming and robust optimization approaches are introduced to deal with data uncertainty in asset returns and interest rates. The statistical properties of the probability distributions of uncertain parameters are incorporated in the model through appropriately selected symmetric and asymmetric uncertainty sets. Practical data-driven approaches for implementation of the robust models are also discussed. Numerical results using generated and real market data are presented to illustrate the performance of the robust asset–liability management strategies. The robust investment strategies show better performance in unfavorable market regimes than traditional stochastic programming approaches. The effectiveness of robust investment strategies can be improved by calibrating carefully the shape and the size of the uncertainty sets for asset returns.

Robust Portfolio Optimization

As the use of quantitative techniques has become more widespread in the investment industry, the issue of how to handle portfolio estimation and model risk has grown in importance. Robust optimization is a technique for incorporating estimation errors directly into the portfolio optimization process, and is typically applied in conjunction with robust statistical estimation methods. The robust optimization approach uses the distribution from the estimation process to find a portfolio allocation in one single optimization, while keeping the computational costs low. Robust portfolios tend to be less sensitive to estimation errors, offer some improved portfolio performance, and often have lower turnover ratios. Keywords: portfolio optimization; uncertainty sets; robust portfolio management; Bayesian methods; robust modeling; investment management; MSCI Barra Research Insights Report; Management Science