Long Memory in Electricity Price Time Series

Abstract

The goal of this paper is to analyze a long memory in electricity price time series. Electricity price is different from other commodities by its features like mean-reversion, high volatility rate and frequent occurrence of jumps. These differences are mainly caused by non-storability of the electricity, which need to balance supply and demand in real time. We calculate the Hurst exponent by using the Rescaled Range analysis. The Hurst exponent is a measure that has been widely used to evaluate the self-similarity and correlation properties of fractional Brownian noise, the time-series produced by a fractional (fractal) Gaussian process. The Hurst exponent is used to evaluate the presence or absence of long-range dependence and its degree in a time-series. The Hurst exponent is a numerical estimate of the predictability of a time series. In this paper we investigate the use of the Hurst exponent to classify series of the biggest European energy markets EEX (Central European Energy Exchange). The values of the Hurst exponent vary between 0 and 1, with higher values indicating a smoother trend, less volatility, and less roughness. Random walk has a Hurst exponent of 0,5. When the values of the Hurst exponent lie close to 1.0, the system has long-memory dependence. The larger the H value is, the stronger the trend. Our results show exactly between the stochastic and deterministic process. We think that this value is a sufficient value for credible prediction.