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OR Spectrum

, Volume 40, Issue 3, pp 711–750 | Cite as

Optimizing conditional value-at-risk in dynamic pricing

  • Jochen Gönsch
  • Michael Hassler
  • Rouven Schur
Regular Article

Abstract

Many industries use dynamic pricing on an operational level to maximize revenue from selling a fixed capacity over a finite horizon. Classical risk-neutral approaches do not accommodate the risk aversion often encountered in practice. We add to the scarce literature on risk aversion by considering the risk measure conditional value-at-risk (CVaR), which recently became popular in areas like finance, energy, or supply chain management. A key aspect of this paper is selling a single unit of capacity, which is highly relevant in, for example, the real estate market. We analytically derive the optimal policy and obtain structural results. The most important managerial implication is that the risk-averse optimal price is constant over large parts of the selling horizon, whereas the price continuously declines in the standard setting of risk-neutral dynamic pricing. This offers a completely new explanation for the price-setting behavior often observed in practice. For arbitrary capacity, we develop two algorithms to efficiently compute the value function and evaluate them in a numerical study. Our results show that applying a risk-averse policy, even a static one, often yields a higher CVaR than applying a dynamic, but risk-neutral, policy.

Keywords

Revenue management Dynamic pricing Dynamic programming Risk management Service operations 

Notes

Acknowledgements

We thank Nikolaus Schweizer (Tilburg University), who suggested how to generalize the condition on the distribution of the willingness-to-pay to its present form.

Supplementary material

291_2018_513_MOESM1_ESM.pdf (618 kb)
Supplementary material 1 (PDF 618 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Mercator School of ManagementUniversity of Duisburg-EssenDuisburgGermany
  2. 2.Department of Analytics and OptimizationUniversity of AugsburgAugsburgGermany

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