Cross-correlations between price and volume in Chinese gold markets

Ruan, Qingsong; Jiang, Wei; Guo, Ma

doi:10.1016/j.physa.2015.12.164

Cited by 56 publications

(18 citation statements)

References 43 publications

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“…In our findings, for the Bitcoin market, the crosscorrelation exponent is less than the average generalized Hurst exponent for q > 0. This result is consistent with the conclusions of the agricultural future market [59] and the gold market [10].…”

Section: Multifractal Detrended Cross-correlation Analysissupporting

confidence: 92%

“…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: Multifractal Detrended Cross-correlation Analysissupporting

confidence: 92%

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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“…Another data set used for a robustness check in this paper is futures gold prices quoted at the Shanghai Futures Exchange (the SHFE) in the period from 9 January 2008 to 6 May 2016. The considered period for futures prices only starts on 9 January 2008 because the SHFE only started gold futures trading on that date (Ruan et al ., ; Wu and Duan, ; Jena et al ., ; Lin et al ., 2018a). We follow Beckmann and Czudaj (2014), Lin et al .…”

Section: Methodsmentioning

confidence: 99%

The seasonality of gold prices in China does the risk‐aversion level matter?

Hoang

Zhu

Xiao

et al. 2018

Accounting & Finance

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This article aims to investigate the seasonality of gold prices at the Shanghai Gold Exchange over the 2002-2016 period. Our contributions rely in the distinction between risk-averse and risk-seeking investors regarding their investment strategies. The results show the existence of positive Monday and January effects. However, the Monday effect is more suitable to risk-seeking investors while the January effect is more suitable to risk-averse investors in bearish periods only. A robustness check shows that the Monday effect does not hold on gold futures prices. These results indicate the importance to consider the risk-aversion level of investors in seasonal investment strategies.

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“…El Alaoui (2017) find the existence of multifractal price-volume cross-correlations in Moroccan stock market. Ruan et al (2016) study the time-varying efficiency in the Chinese gold market and find the cross-correlations to be anti-persistent in general. Sukpitak and Hengpunya (2016) find a weak correlation between market efficiency and trading volume in the Thai market.…”

Section: Price-volume Relationship and Amhmentioning

confidence: 99%

Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature

Patil¹,

Rastogi

2019

JRFM

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This paper conducts a review of the literature on the price–volume relationship and its relation with the implications of the adaptive market hypothesis. The literature on market efficiency is classified as efficient market hypothesis (EMH) studies or adaptive market hypothesis (AMH) studies. Under each class, studies are categorized either as return predictability studies or price–volume relationship studies. Finally, review in each category is analyzed based on the methodology used. Our review shows that the literature on return predictability and price–volume relationship in classical EMH approach is extensive while studies in return predictability in the AMH approach have gained increased attention in the last decade. However, the studies in price–volume relationship under adaptive approach are limited, and there is a scope for studies in this area. Authors did not find any literature review on time-varying price–volume relationship. Authors find that there is a scope to study the nonlinear cross–correlation between price and volume using detrended fluctuation analysis (DFA)-detrended cross–correlational analysis (DXA) in the AMH domain. Further, it would be interesting to investigate whether the same cross–correlation holds across different measures of stock indices within a country and across different time scales.

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Cross-correlations between price and volume in Chinese gold markets

Cited by 56 publications

References 43 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 seasonality of gold prices in China does the risk‐aversion level matter?

Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature

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