Multifractal detrended fluctuation analysis for clustering structures of electricity price periods

Wang, Fang; Liao, Guiping; Li, Jianhui; Li, Xiaochun; Zhou, Tiejun

doi:10.1016/j.physa.2013.07.039

Cited by 59 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamical and statistical properties of price fluctuations are investigated using several methods. First, we evaluate correlations in price dynamics across different time scales using the method of scale-dependent fractal exponent [7] obtained from detrended fluctuation analysis (DFA) [32,33,34,35]. We also use the Fourier spectral analysis to identify cyclic components in the electricity price dynamics, as well as the Pareto probability distribution analysis for testing the stability of statistical moments of the studied data.…”

Section: Introductionmentioning

confidence: 99%

Predictability of price movements in deregulated electricity markets

Uritskaya

Uritsky

2015

Energy Economics

View full text Add to dashboard Cite

In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from one hour to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on-and off-peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.

show abstract

Section: Introductionmentioning

confidence: 99%

Predictability of price movements in deregulated electricity markets

Uritskaya

Uritsky

2015

Energy Economics

View full text Add to dashboard Cite

show abstract

“…As one practical method to test whether a non-stationary time series has multifractal characteristics, MF-DFA is widely applied in the financial markets [68], molecular biology [73], disaster prevention and control [69], power pricing analysis [74,75] and electricity market [71,76]. Assume the sample X = {x k , k = 1, 2, .…”

Section: Long-term Memorymentioning

confidence: 99%

The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

Zhang

2018

Energies

View full text Add to dashboard Cite

Abstract:The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.

show abstract

“…As an effective methodologies to explore the cross-correlations between two signals, the detrended cross-correlation analysis (DCCA, also DXA) 10 and its multifractal version-MF-DXA 11 were proposed to be applied into lots of fields. [12][13][14][15][16][17][18][19][20][21][22] To further quantify the cross-correlated levels, a new crosscorrelation index called DXA coefficient, denoted as q DXA , was proposed by Zebende. 23 In addition, Podobnik et al…”

Section: Introductionmentioning

confidence: 99%

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis

Wang

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

In order to detect and quantify asymmetry of two time series, a novel cross-correlation coefficient is proposed based on recent asymmetric detrended cross-correlation analysis (A-DXA), which we called A-DXA coefficient. The A-DXA coefficient, as an important extension of DXA coefficient ρDXA, contains two directional asymmetric cross-correlated indexes, describing upwards and downwards asymmetric cross-correlations, respectively. By using the information of directional covariance function of two time series and directional variance function of each series itself instead of power-law between the covariance function and time scale, the proposed A-DXA coefficient can well detect asymmetry between the two series no matter whether the cross-correlation is significant or not. By means of the proposed A-DXA coefficient conducted over the asymmetry for California electricity market, we found that the asymmetry between the prices and loads is not significant for daily average data in 1999 yr market (before electricity crisis) but extremely significant for those in 2000 yr market (during the crisis). To further uncover the difference of asymmetry between the years 1999 and 2000, a modified H statistic (MH) and ΔMH statistic are proposed. One of the present contributions is that the high MH values calculated for hourly data exist in majority months in 2000 market. Another important conclusion is that the cross-correlation with downwards dominates over the whole 1999 yr in contrast to the cross-correlation with upwards dominates over the 2000 yr.

show abstract

Multifractal detrended fluctuation analysis for clustering structures of electricity price periods

Cited by 59 publications

References 23 publications

Predictability of price movements in deregulated electricity markets

Predictability of price movements in deregulated electricity markets

The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis

Contact Info

Product

Resources

About