Long-term swings and seasonality in energy markets

Abstract

This paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with the Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing. AbstractThis paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with the Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing.

Energy Security and Portfolio Diversification: Conventional and Novel Perspectives

“…It does not include systemic risks (such as the COVID-19 pandemic) that can destabilize an entire industry, market, or the entire global economy. Regarding the issue of seasonality, the existing literature suggests that it exists in the futures market for gas [84] but is arguably negligible for oil [85]. Therefore, it is not considered in this study.…”

A unified European hydrogen infrastructure planning to support the rapid scale-up of hydrogen production

“…Although, traditionally, European natural gas market prices exhibit a seasonal and over the years fluctuating behavior [32], in recent years (2021-2023), analysts have expressed concern about their future evolution, which can impact the deployment of blue hydrogen. We find that a surge of up to 60 % in the 2030 natural gas price (from 13.2 e/MWh to 21.08 e/MWh), results in higher penetration of green hydrogen up to 63 % (Extended Data Fig.…”

Idiosyncratic volatility in commodity futures markets: measurement and puzzle

“…Previous studies on commodity futures markets primarily focus on building and calibrating structural models that describe commodity price movements (e.g. Schwartz, 1997;Schwartz and Smith, 2000;Sørensen, 2002;Mirantes et al, 2012;Dempster et al, 2018;Cortazar et al, 2019;Moreno et al, 2019;Guo, 2020) or examining MF 49,10 the relationship between expected return and total volatility (Erb and Harvey, 2006;Gorton and Geert Rouwenhorst, 2006;Bhardwaj et al, 2015). In contrast, this paper investigates the commodity futures markets from the perspective of idiosyncratic volatility, which has important implications for asset diversification and hedging (Fernandez-Perez et al, 2016;Yang et al, 2018;Makkonen et al, 2021).…”

“…, 2018; Cortazar et al. , 2019; Moreno et al. , 2019; Guo, 2020) or examining the relationship between expected return and total volatility (Erb and Harvey, 2006; Gorton and Geert Rouwenhorst, 2006; Bhardwaj et al.…”