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This research paper aims to explore asymmetric market efficiency of the 13 Euro countries, i.e. Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherland, Portugal, Slovakia, Slovenia and Spain, concerning the period before global financial crisis (GFC), after GFC and period of COVID-19 pandemic.

Multifractal detrended fluctuation analysis (MF-DFA) is applied to examine the persistence and anti-persistency. It also discusses the random walk behavior hypothesis of these 13 countries non-stationary time series. Additionally, generalized Hurst exponents are applied to estimate the relative efficiency between short- and long-run horizons and small and large fluctuations.

The current study results suggest that most countries' markets are multifractal and exhibit long-term persistence in the short and long run. Moreover, the results with respect to full sample confirm that Portugal is the most efficient country in short run and Austria is the least efficient country. However, in long run, Austria appeared to be highly efficient, and Slovakia is the least efficient. In the pre-GFC period, Greece is said to be the relatively most efficient market in the short run, whereas Austria is the most efficient market in the long run. In the case of Post-GFC, Netherland and Ireland are the most efficient markets in short and long run, respectively. Lastly, COVID-19 results indicate that Finland's stock market is the most efficient in short run. Whereas, in the long run, the high efficiency is illustrated by Germany. In contrast, the most affected stock market due to COVID-19 is Belgium.

This study will add value to the present knowledge on efficient market hypothesis (EMH) with the MF-DFA approach. Also, with the MF-DFA approach, potential investors will be capable of ranking the stock markets of Eurozone countries based on their efficiency in the period before and after GFC and then specifically in the period of COVID-19.

本研究旨在探討13個歐元區國家在環球金融危機前後, 以及2019新型冠狀病毒病肆虐時期之不對稱市場效率; 這13個國家包括: 奧地利、比利時、芬蘭、法國、德國、希臘、愛爾蘭、義大利、荷蘭、葡萄牙、斯洛伐克、斯洛維尼亞和西班牙。

研究人員使用多重分形去趨勢波動分析法、來探討持續性與反持續性。這分析法也用來討論正在研究中的13個國家的非平穩時間序列的隨機漫步假說; 而且, 廣義赫斯特指數被用來估算長期/短期投資與大/小波動之間的相對效率。

研究結果間接表明了大部份國家的市場都是多重分形的; 而且, 它們無論以短期抑或以長期來審視觀察, 均能展示持久性。再者, 就整體樣本而言, 研究結果確認了在短期來看, 葡萄牙是效率最高的國家, 而奧地利則效率最低。唯以長期來審視觀察, 奧地利則似乎效率很高, 而效率最低的則是斯洛伐克。在環球金融危機爆發前, 就短期而言, 希臘被認為是相對效率最高的市場, 而長期而言, 效率最高的則是奧地利。至於在環球金融危機爆發後, 就短期而言, 荷蘭是效率最高的市場, 而就長期而言, 效率最高的則是愛爾蘭。最後, 2019新型冠狀病毒病的結果顯示, 就短期而言, 荷蘭的股票市場是效率最高的, 而長期而言, 德國則展示了其高效率性。而受疫情影響最大的股票市場則是比利時。

研究採用了多重分形去趨勢波動分析法、來探討股票市場的效率, 並以此分析法來討論有關國家的非平穩時間序列的隨機漫步假說, 這使我們對效率市場假說有進一步的認識; 就此而言, 本研究為有關的探討增添價值; 而且, 有意投資者在使用多重分形去趨勢波動分析法下, 能夠基於歐元區國家的股票市場在環球金融危機前後, 以及更明確地在2019新型冠狀病毒病肆虐時期的效率, 來把這些股票市場分等級。

環球金融危機、2019新型冠狀病毒病、效率市場假說、多重分形去趨勢波動分析.

This paper examines asymmetric multifractality (A-MF) in the leading Middle East and North Africa (MENA) stock markets under different turbulent periods (global financial crisis [GFC] and European sovereign debt crisis [ESDC], oil price crash and COVID-19 pandemic).

This study applies the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method of Cao et al. (2013) to identify A-MF and MENA stock market efficiency during the COVID-19 pandemic.

The results show strong evidence of different patterns of MF during upward and downward trends. Inefficiency is higher during upward trends than during downward trends in most of the stock markets in the whole sample period, and the opposite is true during financial crises. The Turkish stock market is the least inefficient during upward and downward trends. A-MF intensifies with an increase in scales. The evolution of excessive A-MF for MENA stock returns is heterogeneous. Most of the stock markets are more inefficient during a pandemic crisis than during an oil crash and other financial crises. However, the inefficiency of the Saudi Arabia and Qatar stock markets is highly sensitive to oil price crashes. Overall, the level of inefficiency varies across market trends, scales and stock markets and over time. The findings of this study provide investors and policymakers with valuable insights into efficient investment strategies, risk management and financial stability.

This paper first explores A-MF in the MENA emerging stock markets. The A-MF analysis provides useful information to investors regarding asset allocation, portfolio risk management and investment strategies during bullish and bearish market states. In addition, this paper examines A-MF under different turbulent periods, such as the GFC, the ESDC, the 2014–2016 oil crash and the COVID-19 pandemic.

The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic.

Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method.

The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes.

The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period.

To the authors’ knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period.

This study uses the MF-DFA to analyze house price fluctuations.

The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result.

The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

The mining process is essential in cryptocurrency networks. However, it consumes considerable electrical energy, which is undoubtedly harmful to the environment. In response, energy-conserving cryptocurrency projects with reduced energy requirements or based on renewable energies have been developed. Recently, the COVID-19 pandemic and the Russian invasion of Ukraine ignited an unprecedented upheaval in financial products, especially in cryptocurrency and energy markets. Therefore, the paper aims to explore the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict.

This paper investigates the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Their competitiveness is compared with conventional ones by analyzing their efficiency through multifractal detrended fluctuation analysis and automatic variance ratio during the COVID-19 and Russian invasion periods.

The empirical results show that all investigated energy-conserving cryptocurrencies negatively responded to the pandemic and positively reacted to the Russian invasion. On the other hand, all conventional cryptocurrencies reacted negatively to the COVID-19 pandemic and the amid-Russian attack. Besides, Bitcoin and SolarCoin were the least inefficient before the outbreak of COVID-19. Nevertheless, the Ethereum market became the most efficient after the pandemic spread. Similarly, the efficiency of Ripple was the most significant during the conflict between Russia and Ukraine. The energy crisis caused by Russia benefited the efficiency of the studied energy-conserving cryptocurrencies.

This research is of interest to investors seeking opportunities in these energy-conserving cryptocurrencies and policymakers working to implement reforms to improve their market efficiency and promote long-term financial market growth.

To the best of the authors' knowledge, the behavior of cryptocurrencies based on renewable and reduced energy during the recent conflict between Russia and Ukraine has not been explored.

This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.

The authors used MultiFractal Detrended Fluctuation Cross-Correlation Analysis (MF-X-DFA) to explore the correlation behavior before and during conflict. The authors analyzed the price connections between future prices for crude oil and agricultural commodities. Data consists of daily futures price returns for agricultural commodities (Corn, Soybean and Wheat) and Crude Oil (Brent) traded on the Chicago Mercantile Exchange from Aug 3, 2020, to July 29, 2022.

The results suggest that cross-correlation behavior changed after the conflict. The multifractal behavior was observed in the cross correlations. The Russia–Ukraine conflict caused an increase in the series' fractal strength. The study findings showed that the correlations involving the wheat market were higher and anti-persistent behavior was observed.

The study was limited by the number of observations after the Russia–Ukraine conflict.

This study contributes to the literature that investigates the impact of the Russia–Ukraine conflict on the financial market. As this is a recent event, as far as we know, we did not find another study that investigated cross-correlation in agricultural commodities using multifractal analysis.