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Construction of an SDE Model from Intraday Copper Futures Prices

Author

Listed:
  • Loretta Mastroeni

    (Department of Economics, University of Roma Tre, 00145 Roma, Italy
    These authors contributed equally to this work.)

  • Pierluigi Vellucci

    (Department of Economics, University of Roma Tre, 00145 Roma, Italy
    These authors contributed equally to this work.)

Abstract

This paper introduces a model for intraday copper futures prices based on a stochastic differential equation (SDE). In particular, we derive an SDE that fits the model to the data and that is based on the whitening filter approach, a method characterizing linear time-variant systems. This method is applied to construct a model able to simulate the trajectories of copper futures prices, statistically described by means of an empirical autocorrelation approach. We show that the predictability of copper futures prices is rather weak. In fact, the developed model produces trajectories close to the actual data only in the short term. Consequently, the investment risk for copper futures is high. We also show that the performance of the model improves significantly if the time series satisfy particular conditions, e.g., those with a determinism measure.

Suggested Citation

  • Loretta Mastroeni & Pierluigi Vellucci, 2022. "Construction of an SDE Model from Intraday Copper Futures Prices," Risks, MDPI, vol. 10(11), pages 1-21, November.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:11:p:218-:d:975967
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    References listed on IDEAS

    as
    1. Tilton, John E. & Lagos, Gustavo, 2007. "Assessing the long-run availability of copper," Resources Policy, Elsevier, vol. 32(1-2), pages 19-23.
    2. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2022. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Resources Policy, Elsevier, vol. 77(C).
    3. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    4. of England, Bank, 2016. "Markets and operations," Bank of England Quarterly Bulletin, Bank of England, vol. 56(4), pages 212-221.
    5. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    6. Yu, Hui & Ding, Yinghui & Sun, Qingru & Gao, Xiangyun & Jia, Xiaoliang & Wang, Xinya & Guo, Sui, 2021. "Multi-scale comovement of the dynamic correlations between copper futures and spot prices," Resources Policy, Elsevier, vol. 70(C).
    7. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    8. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2018. "Co-existence of stochastic and chaotic behaviour in the copper price time series," Resources Policy, Elsevier, vol. 58(C), pages 295-302.
    9. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2019. "A reappraisal of the chaotic paradigm for energy commodity prices," Energy Economics, Elsevier, vol. 82(C), pages 167-178.
    Full references (including those not matched with items on IDEAS)

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