Measuring Anti-Correlations in the Nordic Electricity Spot Market by Wavelets

TL;DR

This paper analyzes the Nordic electricity spot market using wavelet techniques, revealing anti-persistent mean-reverting behavior with a specific Hurst exponent, and demonstrates the advantages of scale-decoupling methods over traditional approaches.

Contribution

It introduces a wavelet-based multi-scale analysis to accurately characterize market dynamics and identify cross-overs, outperforming classic methods like R/S and Fourier analysis.

Findings

Market exhibits anti-persistent, mean-reverting behavior with Hurst exponent ~0.41.

Wavelet analysis effectively decouples scales and identifies cross-overs.

Traditional methods struggle to define scaling regimes and cross-over points.

Abstract

We consider the Nordic electricity spot market from mid 1992 to the end of year 2000. This market is found to be well approximated by an anti-persistent self-affine (mean-reverting) walk. It is characterized by a Hurst exponent of $H\simeq 0.41$ over three orders of magnitude in time ranging from days to years. We argue that in order to see such a good scaling behavior, and to locate cross-overs, it is crucial that an analyzing technique is used that {\em decouples} scales. This is in our case achieved by utilizing a (multi-scale) wavelet approach. The shortcomings of methods that do not decouple scales are illustrated by applying, to the same dat a set, the classic $R/S$- and Fourier techniques, for which scaling regimes and/or positions of cross-overs are hard to define.