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Non-Linearities, Cyber Attacks and Cryptocurrencies

Author

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  • Guglielmo Maria Caporale
  • Woo-Young Kang
  • Fabio Spagnolo
  • Nicola Spagnolo

Abstract

This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015 - 28/2/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.

Suggested Citation

  • Guglielmo Maria Caporale & Woo-Young Kang & Fabio Spagnolo & Nicola Spagnolo, 2019. "Non-Linearities, Cyber Attacks and Cryptocurrencies," CESifo Working Paper Series 7692, CESifo.
  • Handle: RePEc:ces:ceswps:_7692
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    1. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 61-82, Suppl. De.
    2. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    3. Luc Bauwens & Arie Preminger & Jeroen V. K. Rombouts, 2010. "Theory and inference for a Markov switching GARCH model," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 218-244, July.
    4. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    5. Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
    6. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    7. 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.
    8. Bauwens, Luc & De Backer, Bruno & Dufays, Arnaud, 2014. "A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 207-229.
    9. Michel Rauchs & Garrick Hileman, 2017. "Global Cryptocurrency Benchmarking Study," Cambridge Centre for Alternative Finance Reports 201704-gcbs, Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
    10. Platanakis, Emmanouil & Urquhart, Andrew, 2019. "Portfolio management with cryptocurrencies: The role of estimation risk," Economics Letters, Elsevier, vol. 177(C), pages 76-80.
    11. Antoine Bouveret, 2018. "Cyber Risk for the Financial Sector: A Framework for Quantitative Assessment," IMF Working Papers 2018/143, International Monetary Fund.
    12. Emanuel Kopp & Lincoln Kaffenberger & Christopher Wilson, 2017. "Cyber Risk, Market Failures, and Financial Stability," IMF Working Papers 2017/185, International Monetary Fund.
    13. Thies, Sven & Molnár, Peter, 2018. "Bayesian change point analysis of Bitcoin returns," Finance Research Letters, Elsevier, vol. 27(C), pages 223-227.
    14. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
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    Cited by:

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    2. Lars Hornuf & Paul P. Momtaz & Rachel J. Nam & Ye Yuan, 2023. "Cybercrime on the Ethereum Blockchain," CESifo Working Paper Series 10598, CESifo.
    3. Tian, Shu & Zhao, Bo & Olivares, Resi Ong, 2023. "Cybersecurity risks and central banks’ sentiment on central bank digital currency: Evidence from global cyberattacks," Finance Research Letters, Elsevier, vol. 53(C).
    4. Mircea Constantin Șcheau & Simona Liliana Crăciunescu & Iulia Brici & Monica Violeta Achim, 2020. "A Cryptocurrency Spectrum Short Analysis," JRFM, MDPI, vol. 13(8), pages 1-16, August.
    5. Yousaf, Imran & Pham, Linh & Goodell, John W., 2024. "Dynamic spillovers between leading cryptocurrencies and derivatives tokens: Insights from a quantile VAR approach," International Review of Financial Analysis, Elsevier, vol. 94(C).
    6. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    7. Raza, Syed Ali & Shah, Nida & Guesmi, Khaled & Msolli, Badreddine, 2022. "How does COVID-19 influence dynamic spillover connectedness between cryptocurrencies? Evidence from non-parametric causality-in-quantiles techniques," Finance Research Letters, Elsevier, vol. 47(PA).
    8. Martin Eling & Kwangmin Jung, 2022. "Heterogeneity in cyber loss severity and its impact on cyber risk measurement," Risk Management, Palgrave Macmillan, vol. 24(4), pages 273-297, December.
    9. Kazeem Abimbola Sanusi & Zandri Dickason-Koekemoer, 2022. "Cryptocurrency Returns, Cybercrime and Stock Market Volatility: GAS and Regime Switching Approaches," International Journal of Economics and Financial Issues, Econjournals, vol. 12(6), pages 52-64, November.
    10. Conlon, Thomas & Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2024. "Bitcoin forks: What drives the branches?," Research in International Business and Finance, Elsevier, vol. 69(C).
    11. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    12. Ozili, Peterson K, 2021. "Central bank digital currency can lead to the collapse of cryptocurrency," MPRA Paper 111218, University Library of Munich, Germany.
    13. Guglielmo Maria Caporale & Woo-Young Kang & Fabio Spagnolo & Nicola Spagnolo, 2020. "Cyber-Attacks, Cryptocurrencies, and Cyber Security," CESifo Working Paper Series 8124, CESifo.
    14. Caporale, Guglielmo Maria & Kang, Woo-Young & Spagnolo, Fabio & Spagnolo, Nicola, 2021. "Cyber-attacks, spillovers and contagion in the cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    15. Yousaf, Imran & Goodell, John W., 2023. "Reputational contagion and the fall of FTX: Examining the response of tokens to the delegitimization of FTT," Finance Research Letters, Elsevier, vol. 54(C).
    16. Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021. "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    17. Costantini, Mauro & Maaitah, Ahmad & Mishra, Tapas & Sousa, Ricardo M., 2023. "Bitcoin market networks and cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    18. Imran Yousaf & Shoaib Ali & Elie Bouri & Anupam Dutta, 2021. "Herding on Fundamental/Nonfundamental Information During the COVID-19 Outbreak and Cyber-Attacks: Evidence From the Cryptocurrency Market," SAGE Open, , vol. 11(3), pages 21582440211, July.
    19. Atef Ghalwesh & Shimaa Ouf & Amr Sayed, 2020. "A Proposed System for Securing Cryptocurrency Via the Integration of Internet of Things with Blockchain," International Journal of Economics and Financial Issues, Econjournals, vol. 10(3), pages 166-173.
    20. Goodell, John W., 2020. "COVID-19 and finance: Agendas for future research," Finance Research Letters, Elsevier, vol. 35(C).

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    More about this item

    Keywords

    crypto currencies; cyber attacks; regime switching;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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