Journal of Risk

In this issue of The Journal of Risk we focus on new approaches to portfolio diversification, in both location and time; the handling of adverse price effects in illiquid markets; and the estimation of systemic risk measures.

In “Genetic algorithm-based portfolio optimization with higher moments in global stock markets”, the first paper in this issue, Saranya Kshatriya and Krishna Prasanna apply a technique initiated in biology to portfolio selection in order to account for moments higher than the typical mean and variance. This technique is particularly well suited to nonlinear optimization problems with multiple objectives. Through an illustration on investment strategies across a variety of regions, the authors show that skewness and kurtosis significantly affect portfolio selection, particularly when emerging markets are considered.

Our second paper, “International and temporal diversifications: the best of both worlds?” by Julien Fouquau, Cécile Kharoubi and Philippe Spieser, also deals with international asset allocations. Here, however, the focus is augmented by a temporal dimension, whereby investors are concerned with the length of time they should hold their assets. The authors then make use of a methodology that combines wavelets with nonparametric copulas, where the former are shown to be efficient for the proper accounting of time scales, while the latter are suitable for tail correlations across assets.

“Risk-averse dynamic arbitrage in illiquid markets” by Somayeh Moazeni, Ricardo A. Collado and Andy Zhang is the issue’s third paper. In it, the authors consider time-dependent price impacts of trades in the presence of time-consistent dynamic risk measures. In the particular context of illiquid markets, adverse price impact can be long lasting and thus may lead to dynamic arbitrage that is driven by price manipulation. In their paper, the authors derive trading conditions, based only on initial information, that prevent such dynamic arbitrage.

The fourth and final paper, “The CoCVaR approach: systemic risk contribution measurement”, is by Wei-Qiang Huang and Stan Uryasev, our own editor emeritus. They conduct a case study involving the implementation of an efficient regression-based estimation of a systemic risk measure that accounts extensively for severe losses. Through their results, the authors illustrate how a low-risk bank may have high systemic impact, while a high-risk bank may not necessarily be reflective of high systemic risk.

Farid AitSahlia
Warrington College of Business, University of Florida

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