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Memon BA, Aslam F, Asadova S, Ferreira P. Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic. Heliyon 2023; 9:e22694. [PMID: 38213596 PMCID: PMC10782163 DOI: 10.1016/j.heliyon.2023.e22694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/05/2023] [Accepted: 11/16/2023] [Indexed: 01/13/2024] Open
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.
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Affiliation(s)
- Bilal Ahmed Memon
- School of Business and Economics, Westminster International University in Tashkent, Uzbekistan
| | - Faheem Aslam
- Department of Management Sciences, COMSATS University Islamabad, Pakistan
| | - Shakhnoza Asadova
- School of Business and Economics, Westminster International University in Tashkent, Uzbekistan
| | - Paulo Ferreira
- VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal
- Department of Economic Sciences and Organizations, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal
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Mnif E, Salhi B, Trabelsi L, Jarboui A. Efficiency and herding analysis in gold-backed cryptocurrencies. Heliyon 2022; 8:e11982. [PMID: 36506392 PMCID: PMC9730126 DOI: 10.1016/j.heliyon.2022.e11982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/02/2022] [Accepted: 11/22/2022] [Indexed: 12/03/2022] Open
Abstract
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.
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Affiliation(s)
- Emna Mnif
- Department of Finance, University of Sfax, Sfax, Tunisia
| | - Bassem Salhi
- Department of Accounting, College of Business Administration, Majmaah University, Majmaah, 11952, Saudi Arabia
| | - Lotfi Trabelsi
- Department of Finance and Accounting, University of Sfax, Sfax, Tunisia
| | - Anis Jarboui
- Department of Management, University of Sfax, Sfax, Tunisia
- Corresponding author.
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Malaina I, Aranburu L, Martínez L, Fernández-Llebrez L, Bringas C, De la Fuente IM, Pérez MB, González L, Arana I, Matorras R. Labor estimation by informational objective assessment (LEIOA) for preterm delivery prediction. Arch Gynecol Obstet 2018; 297:1213-1220. [PMID: 29508063 DOI: 10.1007/s00404-018-4729-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 02/28/2018] [Indexed: 10/17/2022]
Abstract
PURPOSE To introduce LEIOA, a new screening method to forecast which patients admitted to the hospital because of suspected threatened premature delivery will give birth in < 7 days, so that it can be used to assist in the prognosis and treatment jointly with other clinical tools. METHODS From 2010 to 2013, 286 tocographies from women with gestational ages comprehended between 24 and 37 weeks were collected and studied. Then, we developed a new predictive model based on uterine contractions which combine the Generalized Hurst Exponent and the Approximate Entropy by logistic regression (LEIOA model). We compared it with a model using exclusively obstetric variables, and afterwards, we joined both to evaluate the gain. Finally, a cross validation was performed. RESULTS The combination of LEIOA with the medical model resulted in an increase (in average) of predictive values of 12% with respect to the medical model alone, giving a sensitivity of 0.937, a specificity of 0.747, a positive predictive value of 0.907 and a negative predictive value of 0.819. Besides, adding LEIOA reduced the percentage of incorrectly classified cases by the medical model by almost 50%. CONCLUSIONS Due to the significant increase in predictive parameters and the reduction of incorrectly classified cases when LEIOA was combined with the medical variables, we conclude that it could be a very useful tool to improve the estimation of the immediacy of preterm delivery.
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Affiliation(s)
- Iker Malaina
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain.
| | - Larraitz Aranburu
- Department of Applied Mathematics, Statistics and Operation Research, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Luis Martínez
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain
| | | | - Carlos Bringas
- Department of Cell Biology and Histology, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Ildefonso M De la Fuente
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain.,Department of Nutrition, CEBAS-CSIC Institute, Murcia, Spain
| | - Martín Blás Pérez
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain
| | - Leire González
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain
| | - Itziar Arana
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain
| | - Roberto Matorras
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain.,Department of Medical-Surgical Specialties, University of the Basque Country UPV/EHU, Leioa, Spain
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