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A three-factor stochastic model for forecasting production of energy materials

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  • Bufalo, Michele
  • Orlando, Giuseppe

Abstract

In this paper, we present a generalized stochastic three-factor model to forecast changes in the industrial production of energy materials. This approach is new as, by deriving a stochastic process correlated with its mean and volatility, we convert it into an uncorrelated auxiliary process through Lamperti transformations. We show that the proposed model can be used for forecasting the change in the equilibrium between demand and supply of energy materials and could be further developed for setting up a reference pricing model for the market.

Suggested Citation

  • Bufalo, Michele & Orlando, Giuseppe, 2023. "A three-factor stochastic model for forecasting production of energy materials," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005347
    DOI: 10.1016/j.frl.2022.103356
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    1. Gonzales Chavez, S & Xiberta Bernat, J & Llaneza Coalla, H, 1999. "Forecasting of energy production and consumption in Asturias (northern Spain)," Energy, Elsevier, vol. 24(3), pages 183-198.
    2. Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2020. "Forecasting interest rates through Vasicek and CIR models: A partitioning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 569-579, July.
    3. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    4. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    5. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    6. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo & Fernandez-Anaya, Guillermo, 2008. "Time-varying Hurst exponent for US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6159-6169.
    7. Thorsten Thadewald & Herbert Buning, 2007. "Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 87-105.
    8. Chen, Lin, 1996. "A bond pricing formula under a non-trivial, three-factor model of interest rates," Economics Letters, Elsevier, vol. 51(1), pages 95-99, April.
    9. Chaboud, Alain & Hjalmarsson, Erik & Zikes, Filip, 2021. "The evolution of price discovery in an electronic market," Journal of Banking & Finance, Elsevier, vol. 130(C).
    10. Sheldon, Tamara L., 2017. "Asymmetric effects of the business cycle on carbon dioxide emissions," Energy Economics, Elsevier, vol. 61(C), pages 289-297.
    11. Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2019. "Interest rates calibration with a CIR model," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 20(4), pages 370-387, September.
    12. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    13. Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.
    14. Chen, Yu-Lun & Tsai, Wei-Che, 2017. "Determinants of price discovery in the VIX futures market," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 59-73.
    15. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    16. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    17. Lili Wang & Lina Zhan & Rongrong Li, 2019. "Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    18. Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2019. "A new approach to forecast market interest rates through the CIR model," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 37(2), pages 267-292, September.
    19. Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    20. Orlando, Giuseppe & Bufalo, Michele, 2022. "Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model," Finance Research Letters, Elsevier, vol. 47(PA).
    21. Nicola Cufaro Petroni & Piergiacomo Sabino, 2020. "Gamma Related Ornstein-Uhlenbeck Processes and their Simulation," Papers 2003.08810, arXiv.org.
    22. repec:eme:sef000:sef-03-2019-0116 is not listed on IDEAS
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    More about this item

    Keywords

    Energy; Forecasting; Stochastic trifactorial model; ARIMA-GARCH; Lamperti transformations;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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