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Regime Switching in Stochastic Models of Commodity Prices: An Application to an Optimal Tree Harvesting Problem

Author

Listed:
  • Shan chen

    (Department of Economics, University of Waterloo)

  • Margaret Insley

    (Department of Economics, University of Waterloo)

Abstract

This paper investigates whether a regime switching model of stochastic lumber prices is better for the analysis of optimal harvesting problems in forestry than a more traditional single regime model. Prices of lumber derivatives are used to calibrate a regime switching model, with each of two regimes characterized by a different mean reverting process. A single regime, mean reverting process is also calibrated. The value of a representative stand of trees and optimal harvesting prices are determined by specifying a Hamilton-Jacobi-Bellman Variational Inequality, which is solved for both pricing models using a fully implicit finite difference approach. The regime switching model is found to more closely match the behaviour of futures prices than the single regime model. In addition, analysis of a tree harvesting problem indicates significant differences in terms of land value and optimal harvest thresholds between the regime switching and single regime models.

Suggested Citation

  • Shan chen & Margaret Insley, 2010. "Regime Switching in Stochastic Models of Commodity Prices: An Application to an Optimal Tree Harvesting Problem," Working Papers 1016, University of Waterloo, Department of Economics, revised Jul 2010.
    • Handle: RePEc:wat:wpaper:1016
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    File URL: http://economics.uwaterloo.ca/documents/10-016MI.pdf
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    2. Cantegril, Pierre & Paradis, Gregory & LeBel, Luc & Raulier, Frédéric, 2019. "Bioenergy production to improve value-creation potential of strategic forest management plans in mixed-wood forests of Eastern Canada," Applied Energy, Elsevier, vol. 247(C), pages 171-181.
    3. Oscar V. De la Torre-Torres & José Álvarez-García & María de la Cruz del Río-Rama, 2024. "An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
    4. Margaret Insley, 2013. "On the timing of non-renewable resource extraction with regime switching prices: an optimal stochastic control approach," Working Papers 1302, University of Waterloo, Department of Economics, revised Aug 2013.
    5. Chevallier Julien & Goutte Stéphane, 2017. "On the estimation of regime-switching Lévy models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 3-29, February.
    6. Pless, Jacquelyn & Arent, Douglas J. & Logan, Jeffrey & Cochran, Jaquelin & Zinaman, Owen, 2016. "Quantifying the value of investing in distributed natural gas and renewable electricity systems as complements: Applications of discounted cash flow and real options analysis with stochastic inputs," Energy Policy, Elsevier, vol. 97(C), pages 378-390.
    7. Insley, Margaret, 2017. "Resource extraction with a carbon tax and regime switching prices: Exercising your options," Energy Economics, Elsevier, vol. 67(C), pages 1-16.
    8. Lee, Sangjun & Zhao, Jinhua, 2021. "Adaptation to climate change: Extreme events versus gradual changes," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    9. Margaret Insley & Yichun Huang, 2020. "The economics of water conservation regulations under uncertainty: An application to Alberta's Lower Athabasca River Region," Working Papers 2003, University of Waterloo, Department of Economics, revised Jul 2020.
    10. René Aïd & Luciano Campi & Liangchen Li & Mike Ludkovski, 2021. "An Impulse-Regime Switching Game Model of Vertical Competition," Dynamic Games and Applications, Springer, vol. 11(4), pages 631-669, December.
    11. Adriana Piazza & Bernardo Pagnoncelli, 2015. "The stochastic Mitra–Wan forestry model: risk neutral and risk averse cases," Journal of Economics, Springer, vol. 115(2), pages 175-194, June.
    12. Mehrdoust, Farshid & Noorani, Idin & Kanniainen, Juho, 2024. "Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 228-269.
    13. Martzoukos, Spiros H. & Zacharias, Eleftherios, 2013. "Real option games with R&D and learning spillovers," Omega, Elsevier, vol. 41(2), pages 236-249.
    14. Fan, Kun & Shen, Yang & Siu, Tak Kuen & Wang, Rongming, 2015. "Valuing commodity options and futures options with changing economic conditions," Economic Modelling, Elsevier, vol. 51(C), pages 524-533.
    15. Ballestra, Luca Vincenzo & Pacelli, Graziella, 2013. "Pricing European and American options with two stochastic factors: A highly efficient radial basis function approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(6), pages 1142-1167.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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