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PDSim: A Shiny App for Polynomial Diffusion Model Simulation and Estimation

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  • Peilun He
  • Nino Kordzakhia
  • Gareth W. Peters
  • Pavel V. Shevchenko

Abstract

PDSim is an R package that enables users to simulate commodity futures prices using the polynomial diffusion model introduced in Filipovic and Larsson (2016) through both a Shiny web application and R scripts. It also provides state variables and contract estimations via the Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). With its user-friendly interface, PDSim makes the features of simulations and estimations accessible to all users. To date, it is the only package specifically designed for the simulation and estimation of the polynomial diffusion model. Additionally, the package integrates the Schwartz and Smith two-factor model (Schwartz & Smith, 2000) as an alternative approach. PDSim offers versatile deployment options, including running locally, via the Shiny server, or through Docker.

Suggested Citation

  • Peilun He & Nino Kordzakhia & Gareth W. Peters & Pavel V. Shevchenko, 2024. "PDSim: A Shiny App for Polynomial Diffusion Model Simulation and Estimation," Papers 2409.19385, arXiv.org.
    • Handle: RePEc:arx:papers:2409.19385
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    References listed on IDEAS

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    1. Xi Kleisinger-Yu & Vlatka Komaric & Martin Larsson & Markus Regez, 2019. "A multi-factor polynomial framework for long-term electricity forwards with delivery period," Papers 1908.08954, arXiv.org, revised Jun 2020.
    2. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
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    4. Ames, Matthew & Bagnarosa, Guillaume & Matsui, Tomoko & Peters, Gareth W. & Shevchenko, Pavel V., 2020. "Which risk factors drive oil futures price curves?," Energy Economics, Elsevier, vol. 87(C).
    5. Peilun He & Nino Kordzakhia & Gareth W. Peters & Pavel V. Shevchenko, 2024. "Multi-Factor Polynomial Diffusion Models and Inter-Temporal Futures Dynamics," Papers 2409.19386, arXiv.org.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    7. Mihaela Manoliu & Stathis Tompaidis, 2002. "Energy futures prices: term structure models with Kalman filter estimation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 21-43.
    8. Gibson, Rajna & Schwartz, Eduardo S, 1990. "Stochastic Convenience Yield and the Pricing of Oil Contingent Claims," Journal of Finance, American Finance Association, vol. 45(3), pages 959-976, July.
    9. Benjamin Favetto & Adeline Samson, 2010. "Parameter Estimation for a Bidimensional Partially Observed Ornstein–Uhlenbeck Process with Biological Application," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 200-220, June.
    10. Matthew Ames & Guillaume Bagnarosa & Tomoko Matsui & Gareth W. Peters & Pavel V. Shevchenko, 2020. "Which risk factors drive oil futures price curves?," Post-Print hal-02779870, HAL.
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