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This study introduces the generalised normal distribution hidden Markov model (GND-HMM) with constrained and unconstrained parameters, where a first-order Markov chain governs the draws of the states from the mixture components. The proposed model is applied to the daily electricity price returns of the electricity market in northern Italy to detect the turmoil periods that occurred during the years 2020–2023. The turmoil periods detected by the GND-HMM model highlight important aggregate events such as the Covid-19 pandemic and the Russia-Ukranian conflict. Furthermore, our study aims to examine the relationship between the identified turmoil periods and the time series of CO \(_2\) emissions in the northern Italian electricity market.

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High Volatility, High Emissions? A Hidden-Markov Model Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Pierdomenico Duttilo,
  • Marina Bertolini,
  • Alfred Kume,
  • Stefano Antonio Gattone

摘要

This study introduces the generalised normal distribution hidden Markov model (GND-HMM) with constrained and unconstrained parameters, where a first-order Markov chain governs the draws of the states from the mixture components. The proposed model is applied to the daily electricity price returns of the electricity market in northern Italy to detect the turmoil periods that occurred during the years 2020–2023. The turmoil periods detected by the GND-HMM model highlight important aggregate events such as the Covid-19 pandemic and the Russia-Ukranian conflict. Furthermore, our study aims to examine the relationship between the identified turmoil periods and the time series of CO \(_2\) emissions in the northern Italian electricity market.