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An option pricing model based on a renewable energy price index

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  • Xue, Jian
  • Ding, Jing
  • Zhao, Laijun
  • Zhu, Di
  • Li, Lei

Abstract

As a result of rapid socioeconomic development, the world is facing the simultaneous challenges of environmental pollution and resource depletion. How to promote the development of renewable, non-polluting energy is therefore essential. This could be achieved by supporting the rapid growth of a renewable energy financial market that includes various derivatives. To support such an effort, we designed an option contract model based on a renewable energy price index using data from the current development status of energy financial markets. To construct a renewable energy price index, we chose the daily electricity price value from the Nord Pool exchange system from January 2017 to August 2019. Next, we used an auto-regressive integrated moving average (ARIMA) model to predict this price index and estimate the model's parameters. We then used the true value and the predicted value for Nord Pool to perform Monte Carlo simulation pricing for electricity price index options in September 2019, and calculated the relative error. We found that the option contract's design for the renewable energy price index was designed reasonably. The ARIMA model predicted the renewable electricity price index accurately, and the Monte Carlo simulation pricing was reasonable and practical. The option contract for renewable energy price index can effectively explore the energy financial markets varieties and promote the global development of renewable energy sources.

Suggested Citation

  • Xue, Jian & Ding, Jing & Zhao, Laijun & Zhu, Di & Li, Lei, 2022. "An option pricing model based on a renewable energy price index," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023653
    DOI: 10.1016/j.energy.2021.122117
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    4. Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.

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