IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v173y2018icp138-142.html
   My bibliography  Save this article

Bitcoin risk modeling with blockchain graphs

Author

Listed:
  • Akcora, Cuneyt Gurcan
  • Dixon, Matthew F.
  • Gel, Yulia R.
  • Kantarcioglu, Murat

Abstract

A key challenge for Bitcoin cryptocurrency holders is managing FX risk. We identify certain sub-graphs (‘chainlets’) which exhibit predictive influence on Bitcoin price and volatility and characterize the types of chainlets that signify extreme losses.

Suggested Citation

  • Akcora, Cuneyt Gurcan & Dixon, Matthew F. & Gel, Yulia R. & Kantarcioglu, Murat, 2018. "Bitcoin risk modeling with blockchain graphs," Economics Letters, Elsevier, vol. 173(C), pages 138-142.
    • Handle: RePEc:eee:ecolet:v:173:y:2018:i:c:p:138-142
    DOI: 10.1016/j.econlet.2018.07.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176518302994
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2018.07.039?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. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    3. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    4. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    5. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    6. Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
    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. 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. 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.
    3. Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).
    4. Matthew F. Dixon & Cuneyt Gurcan Akcora & Yulia R. Gel & Murat Kantarcioglu, 2019. "Blockchain analytics for intraday financial risk modeling," Digital Finance, Springer, vol. 1(1), pages 67-89, November.
    5. Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
    6. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    7. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    8. Yufang Wang & Haiyan Wang, 2020. "Using Networks and Partial Differential Equations to Predict Bitcoin Price," Papers 2001.03099, arXiv.org.
    9. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    10. Apergis, Nicholas & Koutmos, Dimitrios & Payne, James E., 2021. "Convergence in cryptocurrency prices? the role of market microstructure," Finance Research Letters, Elsevier, vol. 40(C).
    11. Samuel W. Akingbade & Marian Gidea & Matteo Manzi & Vahid Nateghi, 2023. "Why Topological Data Analysis Detects Financial Bubbles?," Papers 2304.06877, arXiv.org.

    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. Cuneyt Akcora & Matthew Dixon & Yulia Gel & Murat Kantarcioglu, 2018. "Bitcoin Risk Modeling with Blockchain Graphs," Papers 1805.04698, arXiv.org.
    2. 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.
    3. Matthew F. Dixon & Cuneyt Gurcan Akcora & Yulia R. Gel & Murat Kantarcioglu, 2019. "Blockchain analytics for intraday financial risk modeling," Digital Finance, Springer, vol. 1(1), pages 67-89, November.
    4. Rehman, Mobeen Ur, 2020. "Do bitcoin and precious metals do any good together? An extreme dependence and risk spillover analysis," Resources Policy, Elsevier, vol. 68(C).
    5. 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.
    6. Pedro Bação & António Portugal Duarte & Helder Sebastião & Srdjan Redzepagic, 2018. "Information Transmission Between Cryptocurrencies: Does Bitcoin Rule the Cryptocurrency World?," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 65(2), pages 97-117, June.
    7. 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.
    8. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    9. 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.
    10. Manavi, Seyed Alireza & Jafari, Gholamreza & Rouhani, Shahin & Ausloos, Marcel, 2020. "Demythifying the belief in cryptocurrencies decentralized aspects. A study of cryptocurrencies time cross-correlations with common currencies, commodities and financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    11. Garcia-Jorcano, Laura & Benito, Sonia, 2020. "Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying," Research in International Business and Finance, Elsevier, vol. 54(C).
    12. Rubaiyat Ahsan Bhuiyan & Afzol Husain & Changyong Zhang, 2023. "Diversification evidence of bitcoin and gold from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    13. Parthajit Kayal & G. Balasubramanian, 2021. "Excess Volatility in Bitcoin: Extreme Value Volatility Estimation," IIM Kozhikode Society & Management Review, , vol. 10(2), pages 222-231, July.
    14. 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.
    15. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    16. Toan Luu Duc Huynh & Rizwan Ahmed & Muhammad Ali Nasir & Muhammad Shahbaz & Ngoc Quang Anh Huynh, 2024. "The nexus between black and digital gold: evidence from US markets," Annals of Operations Research, Springer, vol. 334(1), pages 521-546, March.
    17. Julián A. Parra & Carlos Arango & Joaquín Bernal & José E. Gómez & Javier Gómez & Carlos León & Clara Machado & Daniel Osorio & Daniel Rojas & Nicolás Suárez & Eduardo Yanquen, 2019. "Criptoactivos: análisis y revisión de literatura," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, issue 92, pages 1-37, November.
    18. Natalya Apopo & Andrew Phiri, 2019. "On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks?," Working Papers 1904, Department of Economics, Nelson Mandela University, revised Jun 2019.
    19. 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.
    20. Demiralay, Sercan & Golitsis, Petros, 2021. "On the dynamic equicorrelations in cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 524-533.

    More about this item

    Keywords

    Cryptocurrencies; Graph analysis; Forecasting; Financial risk; ICOs;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

    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:ecolet:v:173:y:2018:i:c:p:138-142. 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/ecolet .

    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.