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. 2023 Nov 22;9(12):e22694.
doi: 10.1016/j.heliyon.2023.e22694. eCollection 2023 Dec.

Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic

Bilal Ahmed Memon  1 , Faheem Aslam  2 , Shakhnoza Asadova  1 , Paulo Ferreira  3   4
Affiliations

Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic

Bilal Ahmed Memon et al. Heliyon. .

Abstract

The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.

Keywords: COVID19; Clean and renewable energy; Generalized Hurst exponent; Herd behavior; MLM; Multifractal detrended fluctuation analysis.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Paulo Ferreira reports article publishing charges was provided by Fundação para a Ciência e a Tecnologia.

Figures

Fig. 1
Fig. 1
Fluctuation function F(q) for q=−10,q=0,q=10 against each clean energy markets during the overall period and two sub-sample periods.
Fig. 1
Fig. 1
Fluctuation function F(q) for q=−10,q=0,q=10 against each clean energy markets during the overall period and two sub-sample periods.
Fig. 2
Fig. 2
Generalized Hurst exponent depending on q of clean energy markets during the overall period and two sub-sample periods.
Fig. 2
Fig. 2
Generalized Hurst exponent depending on q of clean energy markets during the overall period and two sub-sample periods.
Fig. 3
Fig. 3
Renyi exponent τ(q) of clean energy markets during the overall period and two sub-sample periods.
Fig. 3
Fig. 3
Renyi exponent τ(q) of clean energy markets during the overall period and two sub-sample periods.
Fig. 4
Fig. 4
Multifractal spectrum of clean energy markets during the overall period and two sub-sample periods.
Fig. 4
Fig. 4
Multifractal spectrum of clean energy markets during the overall period and two sub-sample periods.
See this image and copyright information in PMC

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