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Risk tomography
Journal article   Peer reviewed

Risk tomography

András Prékopa and Jinwook Lee
European journal of operational research, v 265(1)
16 Feb 2018

Abstract

Conditional Value-at-Risk Convexity Multivariate risk measures p-Efficient points Value-at-Risk
•This paper proposes to represent a random vector by a vector of random polar coordinates.•A new class of multivariate risk measures (Directional Conditional Value-at-Risk).•Properties and calculation of the proposed risk measures.•Applications to agricultural industry and portfolio optimization problem.•Comparison with its univariate counterpart, Conditional Value-at-Risk (CVaR). New multivariate risk measures are introduced, suitable for optimal management of multidimensional assets. Risk is measured along lines through a given reference point in a multidimensional Euclidean space, and then maximum (minimum in financial planning) or mixture is taken with respect to lines lying in cones. We use VaR and CVaR as univariate risk measures but the construction allows for the use any of them. In some case numéraire is used to value the assets. Some of the new measures enjoy the coherence property for sums and also for composition, where assets are put together to form higher dimensional vectors. Numerical calculations of them are tractable as shown for certain multivariate distributions. Applications are presented for the agricultural industry using USDA database, as well as a financial portfolio problem using recent US stock market data.

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6 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Management
Operations Research & Management Science
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