Blockchain ETFs and the cryptocurrency and Nasdaq markets: Multifractal and asymmetric cross-correlations

被引:9
作者
Kristjanpoller, Werner [1 ]
Nekhili, Ramzi [2 ]
Bouri, Elie [3 ,4 ]
机构
[1] Univ Tecn Federico Santa Maria, Dept Obras Civiles, Valparaiso, Chile
[2] Appl Sci Univ, Dept Accounting & Finance, Eker, Bahrain
[3] Lebanese American Univ, Sch Business, Byblos, Lebanon
[4] Kyung Hee Univ, Coll Business, 26 Kyungheedae, Seoul 02447, South Korea
关键词
Multifractal and asymmetric detrended cross; correlation (MF-ADCC); Blockchain ETF; Cryptocurrencies; Nasdaq index; COVID-19; outbreak; DETRENDED FLUCTUATION ANALYSIS; LONG-RANGE CORRELATIONS; VOLATILITY;
D O I
10.1016/j.physa.2024.129589
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Blockchain exchange-traded funds (ETFs) are nascent products in the financial industry. A limited literature focuses on the multifractal analysis of some conventional ETFs, but the multifractal behaviour of the blockchain ETF market has not been studied. In this paper, we investigate the multifractal and asymmetric cross-correlation features between blockchain ETFs and the cryptocurrency and Nasdaq markets. Multifractality exists in the cross-correlations between blockchain ETFs and the cryptocurrency and Nasdaq markets. There is a higher persistence in the crosscorrelation behaviours between blockchain ETFs and cryptocurrencies in the uptrend, whereas the persistence between blockchain ETFs and Nasdaq is more pronounced in the downtrend. On one hand, this suggests that large fluctuations in the cryptocurrency markets lead to large fluctuations in blockchain ETF markets. On the other hand, large fluctuations in the Nasdaq index lead to small fluctuations in blockchain ETF markets. These results reflect stronger ties between blockchain ETFs and cryptocurrencies compared to blockchain ETFs and the Nasdaq index. Such heterogeneous behaviour in the cross-correlation structure provides insights for short-term investment, hedging strategies, and market efficiency. Further analysis shows that long-range crosscorrelation and fat-tail distributions are sources of multifractality.
引用
收藏
页数:29
相关论文
共 55 条
  • [11] Measuring multiscaling in financial time-series
    Buonocore, R. J.
    Aste, T.
    Di Matteo, T.
    [J]. CHAOS SOLITONS & FRACTALS, 2016, 88 : 38 - 47
  • [12] Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market
    Cao, Guangxi
    Cao, Jie
    Xu, Longbing
    He, LingYun
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 393 : 460 - 469
  • [13] Are shocks on the returns and volatility of cryptocurrencies really persistent?
    Charfeddine, Lanouar
    Maouchi, Youcef
    [J]. FINANCE RESEARCH LETTERS, 2019, 28 : 423 - 430
  • [14] NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
    Chowdhury, Mohammad Ashraful Ferdous
    Abdullah, Mohammad
    Alam, Masud
    Abedin, Mohammad Zoynul
    Shi, Baofeng
    [J]. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2023, 87
  • [15] Quantitative features of multifractal subtleties in time series
    Drozdz, S.
    Kwapien, J.
    Oswiecimka, P.
    Rak, R.
    [J]. EPL, 2009, 88 (06)
  • [16] Multifractal cross-correlations between green bonds and financial assets
    Fernandes, Leonardo H. S.
    Silva, Jose W. L.
    de Araujo, Fernando H. A.
    Tabak, Benjamin M.
    [J]. FINANCE RESEARCH LETTERS, 2023, 53
  • [17] Option-Implied Volatility Measures and Stock Return Predictability
    Fu, Xi
    Arisoy, Y. Eser
    Shackleton, Mark B.
    Umutlu, Mehmet
    [J]. JOURNAL OF DERIVATIVES, 2016, 24 (01): : 58 - 78
  • [18] Does Bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and DJIA as the Euro, Great British Pound and Yen?
    Gajardo, Gabriel
    Kristjanpoller, Werner D.
    Minutolo, Marcel
    [J]. CHAOS SOLITONS & FRACTALS, 2018, 109 : 195 - 205
  • [19] Do the global grain spot markets exhibit multifractal nature?
    Gao, Xing-Lu
    Shao, Ying-Hui
    Yang, Yan-Hong
    Zhou, Wei-Xing
    [J]. CHAOS SOLITONS & FRACTALS, 2022, 164
  • [20] A new approach to quantify power-law cross-correlation and its application to commodity markets
    He, Ling-Yun
    Chen, Shu-Peng
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2011, 390 (21-22) : 3806 - 3814