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Periodic Trawl Processes: Simulation, Statistical Inference and Applications in Energy Markets

In: Quantitative Energy Finance

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  • Almut E. D. Veraart

    (Imperial College London, Department of Mathematics)

Abstract

This article introduces the class of periodic trawl processes, which are continuous-time, infinitely divisible, stationary stochastic processes, that allow for periodicity and flexible forms of their serial correlation, including both short- and long-memory settings. We derive some of the key probabilistic properties of periodic trawl processes and present relevant examples. Moreover, we show how such processes can be simulated and establish the asymptotic theory for their sample mean and sample autocovariances. Consequently, we prove the asymptotic normality of a (generalised) method-of-moments estimator for the model parameters. We illustrate the new model and estimation methodology in an application to electricity prices.

Suggested Citation

  • Almut E. D. Veraart, 2024. "Periodic Trawl Processes: Simulation, Statistical Inference and Applications in Energy Markets," Springer Books, in: Fred Espen Benth & Almut E. D. Veraart (ed.), Quantitative Energy Finance, pages 73-132, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-50597-3_3
    DOI: 10.1007/978-3-031-50597-3_3
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