IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/109329.html
   My bibliography  Save this paper

A Markov Chain Analysis for Capitalization Dynamics in the Cryptocurrency Market

Author

Listed:
  • Ballis, Antonis
  • Drakos, Konstantinos

Abstract

Utilizing all cryptocurrencies since market inception, we investigate the mobility properties of the market. Using a Markov Chain model, we estimate the Transition Matrix, describing the probabilistic structure of cross-sectional capitalization transitions. We further apply various indices providing the anatomy of cross-sectional dynamics. Additionally, we compare the early cryptocurrency market period to the more recent era, investigating whether there are any discernible changes in the mobility structure. We find that persistence, in the first decade of the crypto market’s operation has been substantial. Moreover, mobility (persistence) is found to be lower (higher) in the recent era of the market. Also, we document that the exit probability monotonically decreases with the cryptocurrency's capitalization. Exit probability exhibits a clear reduction in the recent market era. Overall, the results of this study can also be interpreted as signs that the cryptocurrency market has entered into a maturity phase.

Suggested Citation

  • Ballis, Antonis & Drakos, Konstantinos, 2020. "A Markov Chain Analysis for Capitalization Dynamics in the Cryptocurrency Market," MPRA Paper 109329, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:109329
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/109329/1/MPRA_paper_109329.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Randall Jackson & Alan Murray, 2004. "Alternative Input-Output Matrix Updating Formulations," Economic Systems Research, Taylor & Francis Journals, vol. 16(2), pages 135-148.
    2. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    3. 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.
    4. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    5. Robert B. Israel & Jeffrey S. Rosenthal & Jason Z. Wei, 2001. "Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings," Mathematical Finance, Wiley Blackwell, vol. 11(2), pages 245-265, April.
    6. Maasoumi, Esfandiar & Zandvakili, Sourushe, 1986. "A class of generalized measures of mobility with applications," Economics Letters, Elsevier, vol. 22(1), pages 97-102.
    7. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
    8. Antonis Ballis & Konstantinos Drakos, 2021. "The explosion in cryptocurrencies: a black hole analogy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-8, December.
    9. repec:bla:revinw:v:44:y:1998:i:4:p:535-54 is not listed on IDEAS
    10. Wei, Wang Chun, 2018. "Liquidity and market efficiency in cryptocurrencies," Economics Letters, Elsevier, vol. 168(C), pages 21-24.
    11. Jamal Bouoiyour & Refk Selmi, 2015. "What Does Bitcoin Look Like?," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 449-492, November.
    12. Carpenter, Jennifer N. & Lynch, Anthony W., 1999. "Survivorship bias and attrition effects in measures of performance persistence," Journal of Financial Economics, Elsevier, vol. 54(3), pages 337-374, December.
    13. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
    14. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    15. Schluter, Christian, 1998. "Statistical inference with mobility indices," Economics Letters, Elsevier, vol. 59(2), pages 157-162, May.
    16. Dwyer, Gerald P., 2015. "The economics of Bitcoin and similar private digital currencies," Journal of Financial Stability, Elsevier, vol. 17(C), pages 81-91.
    17. Geweke, John & Marshall, Robert C & Zarkin, Gary A, 1986. "Mobility Indices in Continuous Time Markov Chains," Econometrica, Econometric Society, vol. 54(6), pages 1407-1423, November.
    18. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    19. A. Bigard & Y. Guillotin & C. Lucifora, 1998. "Earnings Mobility: An International Comparison Of Italy And France," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(4), pages 535-554, December.
    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. Koutras, Vasileios M. & Drakos, Konstantinos, 2013. "A migration approach for USA banks' capitalization: Are the 00s the same with the 90s?," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 131-140.
    2. 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.
    3. 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.
    4. Frankovic, Jozo & Liu, Bin & Suardi, Sandy, 2022. "On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia," Global Finance Journal, Elsevier, vol. 54(C).
    5. 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).
    6. 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.
    7. Frank A Cowell & Christian Schluter, 1998. "Measuring Income Mobility with Dirty Data (published in Ethnic and Racial Studies, 22(3), May 1999)," CASE Papers 016, Centre for Analysis of Social Exclusion, LSE.
    8. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    9. 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.
    10. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    11. Areski Cousin & Mohamed Reda Kheliouen, 2016. "A comparative study on the estimation of factor migration models," Working Papers halshs-01351926, HAL.
    12. Beneki, Christina & Koulis, Alexandros & Kyriazis, Nikolaos A. & Papadamou, Stephanos, 2019. "Investigating volatility transmission and hedging properties between Bitcoin and Ethereum," Research in International Business and Finance, Elsevier, vol. 48(C), pages 219-227.
    13. 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.
    14. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
    15. Markus Jäntti & Stephen P. Jenkins, 2013. "Income Mobility," SOEPpapers on Multidisciplinary Panel Data Research 607, DIW Berlin, The German Socio-Economic Panel (SOEP).
    16. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    17. Ed Saiedi & Anders Broström & Felipe Ruiz, 2021. "Global drivers of cryptocurrency infrastructure adoption," Small Business Economics, Springer, vol. 57(1), pages 353-406, June.
    18. Cowell, Frank & Schluter, Christian, 1998. "Income mobility : a robust approach," LSE Research Online Documents on Economics 2210, London School of Economics and Political Science, LSE Library.
    19. Cowell, Frank & Schluter, Christian, 1998. "Measuring income mobility with dirty data," LSE Research Online Documents on Economics 2079, London School of Economics and Political Science, LSE Library.
    20. Eross, Andrea & McGroarty, Frank & Urquhart, Andrew & Wolfe, Simon, 2019. "The intraday dynamics of bitcoin," Research in International Business and Finance, Elsevier, vol. 49(C), pages 71-81.

    More about this item

    Keywords

    Cryptocurrencies; Markov Chain; Transition Matrix;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:109329. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.