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Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market

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  • Carlo Mari

    (University of Chieti-Pescara)

  • Emiliano Mari

    (Sydus)

Abstract

In the spirit of Occam’s razor, we propose a parsimoniuos regime-switching model for describing the complex dynamics of electricity and natural gas prices observed in real markets. The model was built using a machine learning-based methodology, namely a cluster analysis to investigate the properties of the stable dynamics and a deep neural network appropriately trained on market data to drive transitions between different regimes. The main purposes of this study are twofold: (1) to build the simplest model capable of incorporating the main stylized facts of electricity and natural gas prices, including dynamic correlation; (2) to define an appropriate calibration procedure on market data. We applied this methodology to the Italian energy market. The results obtained show remarkable agreement with the empirical data, satisfactorily reproducing the first four moments of the empirical distributions of log-returns.

Suggested Citation

  • Carlo Mari & Emiliano Mari, 2024. "Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(2), pages 1093-1111, April.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:2:d:10.1007_s11135-023-01681-0
    DOI: 10.1007/s11135-023-01681-0
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    1. Carlo Mari & Emiliano Mari, 2021. "Gaussian clustering and jump-diffusion models of electricity prices: a deep learning analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1039-1062, December.
    2. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    3. Matthew Wang & Yi-Hong Lin & Ilya Mikhelson, 2020. "Regime-Switching Factor Investing with Hidden Markov Models," JRFM, MDPI, vol. 13(12), pages 1-15, December.
    4. Apostolos Serletis & Libo Xu, 2019. "Markov Switching Oil Price Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 1045-1064, October.
    5. Jinan Liu & Apostolos Serletis, 2023. "Volatility and dependence in energy markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(1), pages 15-37, March.
    6. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    7. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    8. Naeem, Muhammad Abubakr & Peng, Zhe & Suleman, Mouhammed Tahir & Nepal, Rabindra & Shahzad, Syed Jawad Hussain, 2020. "Time and frequency connectedness among oil shocks, electricity and clean energy markets," Energy Economics, Elsevier, vol. 91(C).
    9. Mason, Charles F. & A. Wilmot, Neil, 2014. "Jump processes in natural gas markets," Energy Economics, Elsevier, vol. 46(S1), pages 69-79.
    10. Elliott Robert J. & Siu Tak Kuen & Lau John W., 2018. "A hidden Markov regime-switching smooth transition model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-21, September.
    11. Duffie, Darrell & Singleton, Kenneth J, 1993. "Simulated Moments Estimation of Markov Models of Asset Prices," Econometrica, Econometric Society, vol. 61(4), pages 929-952, July.
    12. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    13. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    14. Bucher, Axel & Segers, Johan, 2018. "Inference for heavy tailed stationary time series based on sliding blocks," LIDAM Reprints ISBA 2018007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Ruble, Isabella & Powell, John, 2021. "The Brent-WTI spread revisited: A novel approach," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    16. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    17. Křehlík, Tomáš & Baruník, Jozef, 2017. "Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets," Energy Economics, Elsevier, vol. 65(C), pages 208-218.
    18. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    19. Mari, Carlo, 2006. "Regime-switching characterization of electricity prices dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 552-564.
    20. Borovkova, Svetlana & Schmeck, Maren Diane, 2017. "Electricity price modeling with stochastic time change," Energy Economics, Elsevier, vol. 63(C), pages 51-65.
    21. repec:dau:papers:123456789/1433 is not listed on IDEAS
    22. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    23. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    24. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
    25. Daniel Leonhardt & Antony Ware & Rudi Zagst, 2017. "A Cointegrated Regime-Switching Model Approach with Jumps Applied to Natural Gas Futures Prices," Risks, MDPI, vol. 5(3), pages 1-19, September.
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