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The optimal harvesting problem under price uncertainty: the risk averse case

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  • Bernardo K. Pagnoncelli

    (Escuela de Negocios, Universidad Adolfo Ibáñez)

  • Adriana Piazza

    (Universidad Técnica Federico Santa María)

Abstract

We study the exploitation of a one species, multiple stand forest plantation when timber price is governed by a stochastic process. Our model is a stochastic dynamic program with a weighted mean-risk objective function, and our main risk measure is the Conditional Value-at-Risk. We consider two stochastic processes, geometric Brownian motion and Ornstein–Uhlenbeck: in the first case, we completely characterize the optimal policy for all possible choices of the parameters while in the second, we provide sufficient conditions assuring that harvesting everything available is optimal. In both cases we solve the problem theoretically for every initial condition. We compare our results with the risk neutral framework and generalize our findings to any coherent risk measure that is affine on the current price.

Suggested Citation

  • Bernardo K. Pagnoncelli & Adriana Piazza, 2017. "The optimal harvesting problem under price uncertainty: the risk averse case," Annals of Operations Research, Springer, vol. 258(2), pages 479-502, November.
    • Handle: RePEc:spr:annopr:v:258:y:2017:i:2:d:10.1007_s10479-015-1963-9
    DOI: 10.1007/s10479-015-1963-9
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    Cited by:

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    2. Yu, Zhihan & Ning, Zhuo & Chang, Wei-Yew & Chang, Sun Joseph & Yang, Hongqiang, 2023. "Optimal harvest decisions for the management of carbon sequestration forests under price uncertainty and risk preferences," Forest Policy and Economics, Elsevier, vol. 151(C).
    3. Bushaj, Sabah & Büyüktahtakın, İ. Esra & Haight, Robert G., 2022. "Risk-averse multi-stage stochastic optimization for surveillance and operations planning of a forest insect infestation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1094-1110.
    4. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

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