IDEAS home Printed from https://ideas.repec.org/a/ipn/esecon/v14y2019i51p45-60.html

El bitcoin, ¿una burbuja especulativa? Análisis de la estabilidad paramétrica de series de tiempo para el periodo 2009-2018

Author

Listed:
  • Espinoza-Licona, David R.

    (Universidad Autónoma de Querétaro)

  • Pérez-Sosa, Felipe A.

    (Universidad Autónoma de Querétaro)

Abstract

El bitcoin es el medio de cambio más reconocido en la actualidad, representa una alternativa potencial a las monedas fiduciarias actuales. En el presente trabajo se analizará el comportamiento del precio para determinar si existe una burbuja especulativa originada por factores irracionales mediante un análisis de estabilidad paramétrica de la serie de tiempo de las cotizaciones de esta criptomoneda. Se demuestra que existe un cambio estructural durante 2017 y 2018, que ocasionó que la relación entre precio del bitcoin y el tiempo se modificara, la evidencia estadística confirma la formación de una burbuja especulativa durante dicho periodo.

Suggested Citation

  • Espinoza-Licona, David R. & Pérez-Sosa, Felipe A., 2019. "El bitcoin, ¿una burbuja especulativa? Análisis de la estabilidad paramétrica de series de tiempo para el periodo 2009-2018," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 14(51), pages 45-60, Segundo s.
    • Handle: RePEc:ipn:esecon:v:14:y:2019:i:51:p:45-60
    as

    Download full text from publisher

    File URL: http://yuss.me/revistas/ese/ese2019v14n51a03p045_060.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Bornholdt & Kim Sneppen, 2014. "Do Bitcoins make the world go round? On the dynamics of competing crypto-currencies," Papers 1403.6378, arXiv.org.
    2. 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.
    3. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    4. Robert J. Shiller, 2014. "Speculative Asset Prices (Nobel Prize Lecture)," Cowles Foundation Discussion Papers 1936, Cowles Foundation for Research in Economics, Yale University.
    5. Robert Z. Aliber & Charles P. Kindleberger & Robert N. McCauley, 2023. "Manias, Panics, and Crashes," Springer Books, Springer, edition 8, number 978-3-031-16008-0, January.
    6. Zeira, Joseph, 1999. "Informational overshooting, booms, and crashes," Journal of Monetary Economics, Elsevier, vol. 43(1), pages 237-257, February.
    7. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    8. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    9. Fama, Eugene F, et al, 1969. "The Adjustment of Stock Prices to New Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 1-21, February.
    10. Robert J. Shiller, 2014. "Speculative Asset Prices," American Economic Review, American Economic Association, vol. 104(6), pages 1486-1517, June.
    Full references (including those not matched with items on IDEAS)

    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. Ladislav Kristoufek, 2015. "What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    2. 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.
    3. 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.
    4. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    5. 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.
    6. 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).
    7. Alexandre Bovet & Carlo Campajola & Jorge F. Lazo & Francesco Mottes & Iacopo Pozzana & Valerio Restocchi & Pietro Saggese & Nicol'o Vallarano & Tiziano Squartini & Claudio J. Tessone, 2018. "Network-based indicators of Bitcoin bubbles," Papers 1805.04460, arXiv.org.
    8. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    9. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    10. Ke Wu & Spencer Wheatley & Didier Sornette, 2018. "Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations," Papers 1803.03088, arXiv.org, revised May 2018.
    11. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Long range dependence in the Bitcoin market: A study based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 625-640.
    12. 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.
    13. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    14. Abeer ElBahrawy & Laura Alessandretti & Anne Kandler & Romualdo Pastor-Satorras & Andrea Baronchelli, 2017. "Evolutionary dynamics of the cryptocurrency market," Papers 1705.05334, arXiv.org, revised Nov 2017.
    15. Xiao Fan Liu & Xin-Jian Jiang & Si-Hao Liu & Chi Kong Tse, 2020. "Knowledge Discovery in Cryptocurrency Transactions: A Survey," Papers 2010.01031, arXiv.org.
    16. Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2019. "A percolation model for the emergence of the Bitcoin Lightning Network," Papers 1912.03556, arXiv.org.
    17. 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.
    18. Tseng, Fang-Mei & Palma Gil, Eunice Ina N. & Lu, Louis Y.Y., 2021. "Developmental trajectories of blockchain research and its major subfields," Technology in Society, Elsevier, vol. 66(C).
    19. Paulo Ferreira & Éder Pereira, 2019. "Contagion Effect in Cryptocurrency Market," JRFM, MDPI, vol. 12(3), pages 1-8, July.
    20. Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    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:ipn:esecon:v:14:y:2019:i:51:p:45-60. 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: Juan Marroquín-Arreola The email address of this maintainer does not seem to be valid anymore. Please ask Juan Marroquín-Arreola to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/eeipnmx.html .

    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.