IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v72y2020ics1057521920302118.html
   My bibliography  Save this article

A novel two-stage approach for cryptocurrency analysis

Author

Listed:
  • Yang, Boyu
  • Sun, Yuying
  • Wang, Shouyang

Abstract

Modelling and quantifying the underlying characteristics of the cryptocurrency market has drawn increasing attention since Bitcoin went online in 2009. This study proposes a two-stage decomposition and composition method (2SDC) that begins with a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for better interpreting cryptocurrency formations. This study involves daily closing price data from six cryptocurrencies (i.e., Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Monero and Dash) from July 23rd, 2017 to July 23rd, 2019. In the first stage, six time series are jointly decomposed into 10 independent intrinsic mode functions (IMF) from high to low frequency plus one residual. In the second stage, the IMFs for each cryptocurrency are composed into three components based on Wilcoxon signed-rank test, including high and low frequency components and a long-term trend. These three multi-scale components can be interpreted as short-term fluctuations caused by investor sentiment and micro-structure, the effect of significant events and fundamental values. Furthermore, we demonstrated that the low and high frequency compositions are determining factors of cryptocurrency prices, which supports for the existing evidence (e.g. Bouoiyour, Selmi, Tiwari, & Olayeni, 2016; Ji, Bouri, Lau, & Roubaud, 2019).

Suggested Citation

  • Yang, Boyu & Sun, Yuying & Wang, Shouyang, 2020. "A novel two-stage approach for cryptocurrency analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
  • Handle: RePEc:eee:finana:v:72:y:2020:i:c:s1057521920302118
    DOI: 10.1016/j.irfa.2020.101567
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521920302118
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.irfa.2020.101567?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. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2016. "What drives Bitcoin price?," Economics Bulletin, AccessEcon, vol. 36(2), pages 843-850.
    2. 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.
    3. Zhu, Bangzhu & Ye, Shunxin & Han, Dong & Wang, Ping & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "A multiscale analysis for carbon price drivers," Energy Economics, Elsevier, vol. 78(C), pages 202-216.
    4. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    5. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    6. 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.
    7. Zięba, Damian & Kokoszczyński, Ryszard & Śledziewska, Katarzyna, 2019. "Shock transmission in the cryptocurrency market. Is Bitcoin the most influential?," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 102-125.
    8. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    9. Giudici, Paolo & Abu-Hashish, Iman, 2019. "What determines bitcoin exchange prices? A network VAR approach," Finance Research Letters, Elsevier, vol. 28(C), pages 309-318.
    10. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    11. Urquhart, Andrew & Zhang, Hanxiong, 2019. "Is Bitcoin a hedge or safe haven for currencies? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 49-57.
    12. Aslanidis, Nektarios & Bariviera, Aurelio F. & Martínez-Ibañez, Oscar, 2019. "An analysis of cryptocurrencies conditional cross correlations," Finance Research Letters, Elsevier, vol. 31(C), pages 130-137.
    13. Jamal Bouoiyour & Refk Selmi, 2017. "Are Trump and Bitcoin Good Partners?," Papers 1703.00308, arXiv.org.
    14. Jin-Bom Han & Sun-Hak Kim & Myong-Hun Jang & Kum-Sun Ri, 2020. "Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 337-353, August.
    15. Scheitrum, Daniel P. & Carter, Colin A. & Revoredo-Giha, Cesar, 2018. "WTI and Brent futures pricing structure," Energy Economics, Elsevier, vol. 72(C), pages 462-469.
    16. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.
    17. Guesmi, Khaled & Saadi, Samir & Abid, Ilyes & Ftiti, Zied, 2019. "Portfolio diversification with virtual currency: Evidence from bitcoin," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 431-437.
    18. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    19. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
    20. Yu, Lean & Zha, Rui & Stafylas, Dimitrios & He, Kaijian & Liu, Jia, 2020. "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    21. de la Horra, Luis P. & de la Fuente, Gabriel & Perote, Javier, 2019. "The drivers of Bitcoin demand: A short and long-run analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 21-34.
    22. Jamal Bouoiyour & Refk Selmi & Mark E. Wohar, 2019. "Safe havens in the face of Presidential election uncertainty: A comparison between Bitcoin, oil and precious metals," Applied Economics, Taylor & Francis Journals, vol. 51(57), pages 6076-6088, December.
    23. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    24. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    25. Cagli, Efe Caglar, 2019. "Explosive behavior in the prices of Bitcoin and altcoins," Finance Research Letters, Elsevier, vol. 29(C), pages 398-403.
    26. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    27. Jiang, Yonghong & Nie, He & Ruan, Weihua, 2018. "Time-varying long-term memory in Bitcoin market," Finance Research Letters, Elsevier, vol. 25(C), pages 280-284.
    28. Dastgir, Shabbir & Demir, Ender & Downing, Gareth & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test," Finance Research Letters, Elsevier, vol. 28(C), pages 160-164.
    29. Kim, Thomas, 2017. "On the transaction cost of Bitcoin," Finance Research Letters, Elsevier, vol. 23(C), pages 300-305.
    30. 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.
    31. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    32. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    33. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Kang, Sang Hoon, 2019. "Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum," Finance Research Letters, Elsevier, vol. 29(C), pages 222-230.
    34. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    35. 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.
    36. Adam S. Hayes, 2019. "Bitcoin price and its marginal cost of production: support for a fundamental value," Applied Economics Letters, Taylor & Francis Journals, vol. 26(7), pages 554-560, April.
    37. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).
    38. Ji, Qiang & Bouri, Elie & Lau, Chi Keung Marco & Roubaud, David, 2019. "Dynamic connectedness and integration in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 257-272.
    39. Shahzad, Syed Jawad Hussain & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav & Lucey, Brian, 2019. "Is Bitcoin a better safe-haven investment than gold and commodities?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 322-330.
    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. Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
    2. Yue, Yao & Li, Xuerong & Zhang, Dingxuan & Wang, Shouyang, 2021. "How cryptocurrency affects economy? A network analysis using bibliometric methods," International Review of Financial Analysis, Elsevier, vol. 77(C).
    3. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    4. Bossman, Ahmed & Umar, Zaghum & Agyei, Samuel Kwaku & Junior, Peterson Owusu, 2022. "A new ICEEMDAN-based transfer entropy quantifying information flow between real estate and policy uncertainty," Research in Economics, Elsevier, vol. 76(3), pages 189-205.

    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. 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.
    2. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    3. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. 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.
    5. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    6. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    7. 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.
    8. 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).
    9. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2021. "Bitcoin versus high-performance technology stocks in diversifying against global stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    10. 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.
    11. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    12. Park, Sangjin & Jang, Kwahngsoo & Yang, Jae-Suk, 2021. "Information flow between bitcoin and other financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    13. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
    14. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    15. Ji Ho Kwon, 2021. "On the factors of Bitcoin’s value at risk," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    16. Νikolaos A. Kyriazis & Paraskevi Prassa, 2019. "Which Cryptocurrencies Are Mostly Traded in Distressed Times?," JRFM, MDPI, vol. 12(3), pages 1-12, August.
    17. Ahmed H. Elsayed & Giray Gozgor & Chi Keung Marco Lau, 2022. "Causality and dynamic spillovers among cryptocurrencies and currency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2026-2040, April.
    18. Wei Zhang & Pengfei Wang, 2020. "Investor attention and the pricing of cryptocurrency market," Evolutionary and Institutional Economics Review, Springer, vol. 17(2), pages 445-468, July.
    19. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    20. 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.

    More about this item

    Keywords

    Bitcoin; Cryptocurrency; Noise-assisted multivariate empirical mode decomposition; Two-stage decomposition and composition;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    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:finana:v:72:y:2020:i:c:s1057521920302118. See general information about how to correct material in RePEc.

    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.elsevier.com/locate/inca/620166 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.