Invited Session Tue.1.MA 144

Tuesday, 10:30 - 12:00 h, Room: MA 144

Cluster 22: Stochastic optimization [...]

Recent advances in risk representation


Chair: Erick Delage



Tuesday, 10:30 - 10:55 h, Room: MA 144, Talk 1

Erick Delage
Decision making under uncertainty when preference information is incomplete

Coauthor: Benjamin Armbruster


We consider the problem of optimal decision making under uncertainty but assume that the decision maker's utility function is not completely known. Instead, we consider all the utilities that meet some criteria, such as preferring certain lotteries over certain other lotteries and being risk averse, s-shaped, or prudent. This extends the notion of stochastic dominance. We then give tractable formulations for such decision making problems. We formulate them as robust utility maximization problems, as optimization problems with stochastic dominance constraints, and as robust certainty equivalent maximization problems. We use a portfolio allocation problem to illustrate our results.



Tuesday, 11:00 - 11:25 h, Room: MA 144, Talk 2

Dessislava A. Pachamanova
Skewness-aware asset allocation: A new theoretical framework and empirical evidence

Coauthors: Cheekiat Low, Melvyn Sim


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.



Tuesday, 11:30 - 11:55 h, Room: MA 144, Talk 3

Chen Chen
An axiomatic approach to systemic risk

Coauthors: Garud N. Iyengar, Ciamac C. Moallemi


Systemic risk is an issue of great concern in modern financial markets
as well as, more broadly, in the management of complex systems. We propose
an axiomatic framework for systemic risk. Our framework allows for an independent specification of (1) a functional of the cross-sectional profile of outcomes across agents in the system in a single scenario of nature, and (2) a functional of the profile of aggregated outcomes across scenarios of nature. This general class of systemic risk measures captures many specific measures of systemic risk that have recently been proposed as special cases, and highlights their implicit assumptions. Moreover, the
systemic risk measures that satisfy our conditions yield decentralized decompositions, i.e., the systemic risk can be decomposed into risk due to
individual agents. Furthermore, one can associate a shadow price for systemic risk to each agent that correctly accounts for the externalities of the agent's individual decision-making on the entire economy


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