Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach

被引：46

作者：

Duan, Kun ^{[1
]}

Li, Zeming ^{[2
]}

Urquhart, Andrew ^{[3
]}

Ye, Jinqiang ^{[2
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Econ, Wuhan 430074, Peoples R China

[2] Univ Southampton, Southampton Business Sch, Southampton SO17 1BJ, Hants, England

[3] Univ Reading, ICMA Ctr, Henley Business Sch, Reading RG6 6BA, Berks, England

来源：

INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS | 2021年 / 75卷

关键词：

Bitcoin; Market efficiency; Cryptocurrency; Long memory; FCVAR; BECOMING WEAKLY EFFICIENT; LOCAL WHITTLE ESTIMATION; MARKET-EFFICIENCY; CROSS-MARKET; COMMODITY FUTURES; TERM-MEMORY; SAFE HAVEN; INEFFICIENCY; VOLATILITY; TIME;

D O I：

10.1016/j.irfa.2021.101725

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Employing a long-memory approach, we provide a study of the evolution of informational efficiency in five major Bitcoin markets and its influence on cross-market arbitrage. While all the markets are close to full informational efficiency over the whole sample period, the degree of market efficiency varies across markets and over time. The cross-market discrepancy in market efficiency gradually vanishes, suggesting the segmented markets are developing to a consensus where all markets are equally efficient. Through a fractionally cointegrated vector autoregressive (FCVAR) model we show that when the efficiency in Bitcoin/USD and Bitcoin/AUD markets improves the cross-market arbitrage potential narrows, whereas it widens when the efficiency in Bitcoin/CAD, Bitcoin/EUR, and Bitcoin/GBP markets improves. A battery of robustness checks reassure our main findings.

引用

页数：19

共 74 条

[1] Do bitcoins follow a random walk model? [J].

Aggarwal, Divya .

RESEARCH IN ECONOMICS, 2019, 73 (01) :15-22

[2] Prediction of cryptocurrency returns using machine learning [J].

Akyildirim, Erdinc ;

Goncu, Ahmet ;

Sensoy, Ahmet .

ANNALS OF OPERATIONS RESEARCH, 2021, 297 (1-2) :3-36

[3] Intraday efficiency-frequency nexus in the cryptocurrency markets [J].

Aslan, Aylin ;

Sensoy, Ahmet .

FINANCE RESEARCH LETTERS, 2020, 35

[4] The inefficiency of Bitcoin revisited: A dynamic approach [J].

Bariviera, Aurelio F. .

ECONOMICS LETTERS, 2017, 161 :1-4

[5] What drives volatility persistence in the foreign exchange market? [J].

Berger, David ;

Chaboud, Alain ;

Hjalmarsson, Erik .

JOURNAL OF FINANCIAL ECONOMICS, 2009, 94 (02) :192-213

[6] Conditional tail-risk in cryptocurrency markets [J].

Borri, Nicola .

JOURNAL OF EMPIRICAL FINANCE, 2019, 50 :1-19

[7] Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks [J].

Bouri, Elie ;

Gil-Alana, Luis A. ;

Gupta, Rangan ;

Roubaud, David .

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, 2019, 24 (01) :412-426

[8] Trading volume and the predictability of return and volatility in the cryptocurrency market [J].

Bouri, Elie ;

Lau, Chi Keung Marco ;

Lucey, Brian ;

Roubaud, David .

FINANCE RESEARCH LETTERS, 2019, 29 :340-346

[9] Persistence and cyclical dynamics of US and UK house prices: Evidence from over 150 years of data [J].

Canarella, Giorgio ;

Gil-Alana, Luis ;

Gupta, Rangan ;

Miller, Stephen M. .

URBAN STUDIES, 2021, 58 (01) :53-72

[10] Long Memory Interdependency and Inefficiency in Bitcoin Markets [J].

Cheah, Eng-Tuck ;

Mishra, Tapas ;

Parhi, Mamata ;

Zhang, Zhuang .

ECONOMICS LETTERS, 2018, 167 :18-25

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