Price–volume multifractal analysis of the Moroccan stock market

Alaoui, Marwane El

doi:10.1016/j.physa.2017.05.052

Cited by 25 publications

(10 citation statements)

References 52 publications

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“…In the empirical section, we will draw logarithmic graphs according to (6). The cross-correlation exponent H xy q in (5) can describe the power-law relationship between two nonstationary time series.…”

Section: Methodsmentioning

confidence: 99%

“…Studying the crosscorrelations between price and volume can reveal the speed, spread, and absorption of market information flows and the extent to which prices reflect market information. This issue has been explored in the stock market [6][7][8][9], commodity market [10,11], and so on. We believe it is necessary to conduct this study in the Bitcoin market.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, the relationships between the volume and price of the stock market [6][7][8][9], agricultural commodity future markets [11], and the gold market [10] have been studied, using the multifractal detrended cross-correlation analysis method. Therefore, we conduct the MF-DCCA to investigate the cross-correlations between the return and volume of the Bitcoin market.…”

Section: Introductionmentioning

confidence: 99%

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Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Zhang

Wang

et al. 2018

Complexity

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We investigate the cross-correlations of return-volume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended cross-correlation analysis (MF-DCCA) reveal that (1) the nonlinear dependencies and power-law cross-correlations in return-volume relationship are found; (2) all cross-correlations are multifractal, and there are antipersistent behaviors of cross-correlation for q = 2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the cross-correlations of return-volume are antipersistent in the entire sample period.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Zhang

Wang

et al. 2018

Complexity

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“…Matilla-García et al [29] investigate the volume-stock price relation using the method of non-parametric testing based on permutation entropy. Hasan and Salim [30] and El Alaoui [31] use multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods to report cross-correlations between price and volume change. Gupta et al [32] use the Maximum Overlap Discrete Wavelet Transform (MODWT)-VAR approach to examine the price-volume relation in China and India's stock markets; the relationship is found to vary across different time horizons.…”

Section: Literature Reviewmentioning

confidence: 99%

The Price-Volume Relationship of the Shanghai Stock Index: Structural Change and the Threshold Effect of Volatility

Wang

2020

Sustainability

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The price–volume relationship of stocks can be impacted substantially by structural changes and market volatility. In this paper, we analyze China’s stock market behavior and subsequent price–volume equation, with emphasis on two periods of market volatility and structural changes during 2007–2008 and 2015–2016. To account for the impacts of unknown volatility and time breaks, we embed the price–volume relationship into a vector autoregression (VAR) framework with structural breaks and volatility thresholds. Our results indicate that significant time-breaking effects exist and that the high-low volatility effects are substantial. Finally, in its entirety, we identify only a linear causal relationship from price to volume.

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“…These nonlinear dependencies have been found in several studies ( Almudhaf et al., 2020 ; Ghazani et Ebrahimi, 2019 ; Lim et Brooks, 2011 ; Ghazani et Araghi, 2014 ; Shahid et al., 2019 ). Therefore, adding nonlinear test may provide more accurate results as the Moroccan stock exchange exhibits nonlinear effects ( El Alaoui, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Does the Adaptive Market Hypothesis explain the evolution of emerging markets efficiency? Evidence from the Moroccan financial market

Lekhal

Oubani

2020

Heliyon

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This paper scrutinizes different aspects of the Adaptive Market Hypothesis (AMH) in the Moroccan financial market over the period from January 1992 to September 2019 through different approaches. On the basis of daily returns on MASI index, we measure the evolution of efficiency degree based on the linear and nonlinear tests with rolling window. One of the practical implications of the AMH is that the profit opportunities arise from time to time depending on the degree of market efficiency and according to market conditions. To investigate this implication, we track the evolving performance of momentum-based trading strategies and the extent to which this performance is related to the market efficiency degree and certain market conditions. The linear and nonlinear tests reveal that the efficiency degree is time-varying. Moreover, we find via momentum test that profit opportunities appear from time to time and disappear once they are exploited. Interestingly, the momentum profits depend on both the degree of market efficiency and some market conditions. Thus, the investors can capitalize on the inefficiency and certain market conditions using trading strategies such momentum. Overall, our findings are consistent with the AMH framework, which is proved to be a better explanation of the behavior of emerging markets than the Efficiency Market Hypothesis (EMH).

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Price–volume multifractal analysis of the Moroccan stock market

Cited by 25 publications

References 52 publications

Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

The Price-Volume Relationship of the Shanghai Stock Index: Structural Change and the Threshold Effect of Volatility

Does the Adaptive Market Hypothesis explain the evolution of emerging markets efficiency? Evidence from the Moroccan financial market

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