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Convolution Bounds on Quantile Aggregation

Jose Blanchet¹; Henry Lam²; Yang Liu³; Ruodu Wang⁴

¹ Department of Management Science and Engineering, Stanford University, Stanford, California 94305 · ² Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027; · ³ School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong 518172, China · ⁴ Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

Advancing Risk Assessment: New Ways To Compute Quantile Aggregation This issue features a pivotal study on quantile aggregation amid dependence uncertainty, an area critical to finance, risk management, and statistics. The authors introduce “convolution bounds,” derived from a recent inf-convolution formula of quantiles and related risk measures. The obtained analytical tools unify existing results and enhance the understanding of quantile methods by providing general, sharp, and computationally efficient solutions. The results offer insights into the extremal dependence structures, with several implications in risk management and economic analysis applications. For more detailed insights, read the full paper, “Convolution Bounds on Quantile Aggregation” (reference: [insert reference]).

DOI

10.1287/opre.2021.0765

Volume

73 (5)

Pages

2761-2781

Language

en

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