Multi-scaling of wholesale electricity prices

TL;DR

This paper analyzes the multi-scaling properties of volatile wholesale electricity prices in North America, demonstrating that the generalized Hurst exponent can predict price fluctuations when evaluated dynamically.

Contribution

It introduces the use of the generalized Hurst exponent for predicting volatile electricity prices and highlights the importance of cyclicality in forecasting accuracy.

Findings

GHE reveals persistent behavior in electricity price fluctuations.

Dynamic GHE evaluation improves prediction accuracy.

Cyclicality significantly affects the predictive power of Hurst exponents.

Abstract

We empirically analyze the most volatile component of the electricity price time series from two North-American wholesale electricity markets. We show that these time series exhibit fluctuations which are not described by a Brownian Motion, as they show multi-scaling, high Hurst exponents and sharp price movements. We use the generalized Hurst exponent (GHE, $H(q)$) to show that although these time-series have strong cyclical components, the fluctuations exhibit persistent behaviour, i.e., $H(q)>0.5$. We investigate the effectiveness of the GHE as a predictive tool in a simple linear forecasting model, and study the forecast error as a function of $H(q)$, with $q=1$ and $q=2$. Our results suggest that the GHE can be used as prediction tool for these time series when the Hurst exponent is dynamically evaluated on rolling time windows of size $\approx 50 - 100$ hours. These results are also compared to the case in which the cyclical components have been subtracted from the time series, showing the importance of cyclicality in the prediction power of the Hurst exponent.