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Pricing Asset Scheduling Flexibility using Optimal Switching


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  • Rene Carmona
  • Michael Ludkovski
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    We study the financial engineering aspects of operational flexibility of energy assets. The current practice relies on a representation that uses strips of European spark-spread options, ignoring the operational constraints. Instead, we propose a new approach based on a stochastic impulse control framework. The model reduces to a cascade of optimal stopping problems and directly demonstrates that the optimal dispatch policies can be described with the aid of 'switching boundaries', similar to the free boundaries of standard American options. Our main contribution is a new method of numerical solution relying on Monte Carlo regressions. The scheme uses dynamic programming to efficiently approximate the optimal dispatch policy along the simulated paths. Convergence analysis is carried out and results are illustrated with a variety of concrete computational examples. We benchmark and compare our scheme with alternative numerical methods.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Mathematical Finance.

    Volume (Year): 15 (2008)
    Issue (Month): 5-6 ()
    Pages: 405-447

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    Handle: RePEc:taf:apmtfi:v:15:y:2008:i:5-6:p:405-447

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    Keywords: Optimal switching; Monte Carlo; operational flexibility; impulse control; Snell envelope;


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    Cited by:
    1. Chaton, Corinne & Durand-Viel, Laure, 2013. "Real Asset Valuation under Imperfect Competition: Can We Forget About Market Fundamentals?," Economics Papers from University Paris Dauphine 123456789/11439, Paris Dauphine University.
    2. El Asri, Brahim, 2013. "Stochastic optimal multi-modes switching with a viscosity solution approach," Stochastic Processes and their Applications, Elsevier, vol. 123(2), pages 579-602.
    3. Magnus Perninge & Lennart Söder, 2014. "Irreversible investments with delayed reaction: an application to generation re-dispatch in power system operation," Mathematical Methods of Operations Research, Springer, vol. 79(2), pages 195-224, April.
    4. Gassiat, Paul & Kharroubi, Idris & Pham, Huyên, 2012. "Time discretization and quantization methods for optimal multiple switching problem," Stochastic Processes and their Applications, Elsevier, vol. 122(5), pages 2019-2052.
    5. Michael Ludkovski, 2010. "Stochastic Switching Games and Duopolistic Competition in Emissions Markets," Papers 1001.3455,, revised Aug 2010.


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