Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Abstract

The ability to predict the price of stock exchange assets has attracted the attention of economists and physicists around the world, as physical models are useful to predict volatility behaviors. Knowing that volatility is crucial for energy sector planning, the research aim was to investigate whether the Heston pricing model is useful to predict energy generation, trough the steps established by the systematic review protocol. In a corpus of 25 documents, it was possible to identify: lots of financial studies, energy and demography researches; a low level of interaction among universities; the largest number of publications from Australia and China; the most important journal; and the advantages of applying Econophysics models to solve volatility problems. In conclusion, the Heston model can be applied to predict energy generation, since it is a closed-form model and capable of modeling the stochastic volatility, reversing it to the predicted value of average energy generation.

A Comprehensive Review of Research Approaches in the Energy Sector: A Management Sciences Perspective

“…The energy sector operates as a platform for LCSA usage and methodological progress, as studies utilizing this methodology in energy-related systems have increased significantly over the years [26]. Realizing that volatility is vital for energy sector planning, some review studies examine whether the Heston pricing model is beneficial for foreseeing energy production [27]. We also found review papers exploring the latest approaches in combining Artificial Intelligence and Analytics (AIA) in energy smart grid projects [28][29][30].…”

Quantifying Uncertainty: Potential Medical Applications of the Heston Model of Financial Stochastic Volatility

“…Applications of the Heston Model may go beyond financial markets, but so far have remained focused on economic forces. For example, in energy economics, it has been utilized to forecast the market volatility of energy generation [14]. This includes adjusting the model to account for seasonal patterns, spikes, or long-term trends in energy generation or consumption.…”

“…This includes adjusting the model to account for seasonal patterns, spikes, or long-term trends in energy generation or consumption. By doing so, the model has shown potential for predicting financial, operational, and strategic risks in various industries (Reichert and Souza, 2022). However, the model's adaptability could allow it to be applied even more widely, outside of finance and economics, to other fields where stochastic volatility is a factor.…”