IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v545y2020ics0378437119320667.html
   My bibliography  Save this article

Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis

Author

Listed:
  • Kristjanpoller, Werner
  • Bouri, Elie
  • Takaishi, Tetsuya

Abstract

We study the asymmetric multifractality between five main cryptocurrencies (Bitcoin, Litecoin, Ripple, Monero, and Dash) and six equity ETFs from February 2, 2015 to April 30, 2019. The equity ETFs selected relate to emerging markets, China, Japan, the energy sector, financial sector, and technology–Nasdaq. Results from the multifractal asymmetric detrended cross-correlation analysis show a significant persistence and evidence of asymmetric multifractality in the cross-correlation between most of the pairs of cryptocurrencies and ETFs. These findings, which are consistent with previous findings on the susceptibility of Bitcoin to multifractality, indicate the presence of heterogeneity in the cross-relationship between most cryptocurrencies and equity ETFs.

Suggested Citation

  • Kristjanpoller, Werner & Bouri, Elie & Takaishi, Tetsuya, 2020. "Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
  • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320667
    DOI: 10.1016/j.physa.2019.123711
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119320667
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2019.123711?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    3. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 658-670.
    4. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar & H. Eugene Stanley & Boris Podobnik, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Papers 1803.08405, arXiv.org.
    5. Elie Bouri & Naji Jalkh & Peter Molnár & David Roubaud, 2017. "Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?," Applied Economics, Taylor & Francis Journals, vol. 49(50), pages 5063-5073, October.
    6. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    7. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
    8. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    9. Tetsuya Takaishi, 2017. "Statistical properties and multifractality of Bitcoin," Papers 1707.07618, arXiv.org, revised May 2018.
    10. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    11. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    12. Ji, Qiang & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2018. "Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 203-213.
    13. Zhi-Qiang Jiang & Wen-Jie Xie & Wei-Xing Zhou & Didier Sornette, 2018. "Multifractal analysis of financial markets," Papers 1805.04750, arXiv.org.
    14. Raffaello Morales & T. Di Matteo & Tomaso Aste, 2012. "Non stationary multifractality in stock returns," Papers 1212.3195, arXiv.org, revised May 2013.
    15. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    16. Yaya, OlaOluwa S. & Ogbonna, Ahamuefula E. & Olubusoye, Olusanya E., 2019. "How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    17. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    18. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    19. Liang Ding & Hiroyoki Miyake & Hao Zou, 2011. "Asymmetric correlations in equity returns: a fundamental-based explanation," Applied Financial Economics, Taylor & Francis Journals, vol. 21(6), pages 389-399.
    20. Kristoufek, Ladislav, 2018. "On Bitcoin markets (in)efficiency and its evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 257-262.
    21. Lee, Minhyuk & Song, Jae Wook & Kim, Sondo & Chang, Woojin, 2018. "Asymmetric market efficiency using the index-based asymmetric-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1278-1294.
    22. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
    23. Begušić, Stjepan & Kostanjčar, Zvonko & Eugene Stanley, H. & Podobnik, Boris, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 400-406.
    24. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    25. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 10(4), pages 1-15, October.
    26. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2018. "Long-range correlations and asymmetry in the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 948-955.
    27. Takaishi, Tetsuya, 2018. "Statistical properties and multifractality of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 507-519.
    28. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    29. Elie Bouri & Luis A. Gil‐Alana & Rangan Gupta & David Roubaud, 2019. "Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 412-426, January.
    30. B. Podobnik & I. Grosse & D. Horvatić & S. Ilic & P. Ch. Ivanov & H. E. Stanley, 2009. "Quantifying cross-correlations using local and global detrending approaches," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(2), pages 243-250, September.
    31. Aaron Yelowitz & Matthew Wilson, 2015. "Characteristics of Bitcoin users: an analysis of Google search data," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1030-1036, September.
    32. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    33. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    34. Cao, Guangxi & Han, Yan & Li, Qingchen & Xu, Wei, 2017. "Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 119-130.
    35. V Dimitrova & M Fernández-Martínez & M A Sánchez-Granero & J E Trinidad Segovia, 2019. "Some comments on Bitcoin market (in)efficiency," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
    36. Fan, Qingju, 2016. "Asymmetric multiscale detrended fluctuation analysis of California electricity spot price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 252-260.
    37. Juan Luis Lopez & Jesus Guillermo Contreras, 2013. "Performance of multifractal detrended fluctuation analysis on short time series," Papers 1311.2278, arXiv.org.
    38. Tetsuya Takaishi & Takanori Adachi, 2019. "Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study," Papers 1902.09253, arXiv.org.
    39. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    40. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    41. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    42. Kristoufek, Ladislav & Vosvrda, Miloslav, 2019. "Cryptocurrencies market efficiency ranking: Not so straightforward," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    43. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    44. Cao, Guangxi & Cao, Jie & Xu, Longbing & He, LingYun, 2014. "Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 460-469.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Syed Jawad Hussain Shahzad & Elie Bouri & Sang Hoon Kang & Tareq Saeed, 2021. "Regime specific spillover across cryptocurrencies and the role of COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    2. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    4. Zhang, Yue-Jun & Bouri, Elie & Gupta, Rangan & Ma, Shu-Jiao, 2021. "Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    5. Qureshi, Saba & Aftab, Muhammad & Bouri, Elie & Saeed, Tareq, 2020. "Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    6. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    7. Kwon, Ji Ho, 2020. "Tail behavior of Bitcoin, the dollar, gold and the stock market index," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    2. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    3. Li, Mu-Yao & Cai, Qing & Gu, Gao-Feng & Zhou, Wei-Xing, 2019. "Exponentially decayed double power-law distribution of Bitcoin trade sizes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    4. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 658-670.
    5. Takaishi, Tetsuya, 2020. "Rough volatility of Bitcoin," Finance Research Letters, Elsevier, vol. 32(C).
    6. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    7. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    8. Katsiampa, Paraskevi, 2019. "An empirical investigation of volatility dynamics in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 50(C), pages 322-335.
    9. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).
    10. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    11. Kristjanpoller, Werner & Bouri, Elie, 2019. "Asymmetric multifractal cross-correlations between the main world currencies and the main cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1057-1071.
    12. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    13. Tetsuya Takaishi & Takanori Adachi, 2020. "Market Efficiency, Liquidity, and Multifractality of Bitcoin: A Dynamic Study," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 145-154, March.
    14. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    15. Tetsuya Takaishi & Takanori Adachi, 2019. "Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study," Papers 1902.09253, arXiv.org.
    16. Telli, Şahin & Chen, Hongzhuan, 2020. "Multifractal behavior in return and volatility series of Bitcoin and gold in comparison," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    17. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.
    18. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    19. Kurka, Josef, 2019. "Do cryptocurrencies and traditional asset classes influence each other?," Finance Research Letters, Elsevier, vol. 31(C), pages 38-46.
    20. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320667. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.