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Tail risk transmission in technology-driven markets

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  • Naeem, Muhammad Abubakr
  • Shahzad, Mohammad Rahim
  • Karim, Sitara
  • Assaf, Rima

Abstract

The soaring popularity of blockchain investing and cryptocurrencies has captured the attention of policymakers and investors alike. However, as cryptocurrencies remain the most volatile and high-risk investment options, they have displayed extreme asymmetric patterns over time. Considering these concerns, we conducted a comprehensive analysis of the tail risk transmission of these technology-driven markets using the Conditional autoregressive Value at risk (CAViaR) model, which sheds valuable light on the market's tail characteristics. We combined the CAViaR approach with the time-frequency methods proposed by Diebold and Yilmaz (2012) and Barunik and Krehlik (2018) to further enhance our analysis. Our results revealed a range of asymmetric economic and financial patterns across markets and highlighted the varying exposure of these markets to different circumstances over time. Finally, we investigated the impact of global factors on the tail risk transmission of technology-driven markets both in the long and short term. This study offers a wealth of insights for policymakers, investors, financial market participants, and scholars of digital finance to help navigate these rapidly evolving markets.

Suggested Citation

  • Naeem, Muhammad Abubakr & Shahzad, Mohammad Rahim & Karim, Sitara & Assaf, Rima, 2023. "Tail risk transmission in technology-driven markets," Global Finance Journal, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:glofin:v:57:y:2023:i:c:s1044028323000509
    DOI: 10.1016/j.gfj.2023.100855
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    Cited by:

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    5. Wu, Juan, 2023. "Nexus analysis of financial management, digital finance and new technologies," Global Finance Journal, Elsevier, vol. 57(C).
    6. Abdullah, Mohammad & Sarker, Provash Kumer & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Rehman, Mohd Ziaur, 2024. "Tail risk intersection between tech-tokens and tech-stocks," Global Finance Journal, Elsevier, vol. 61(C).
    7. Xu, Dandan & Liu, Yuting, 2024. "How does technological progress affect provincial financial resilience? Evidence at the provincial level in China," Emerging Markets Review, Elsevier, vol. 60(C).
    8. Naeem, Muhammad Abubakr & Nguyen, Thi Thu Ha & Karim, Sitara & Lucey, Brian M., 2023. "Extreme downside risk transmission between green cryptocurrencies and energy markets: The diversification benefits," Finance Research Letters, Elsevier, vol. 58(PA).
    9. Naeem, Muhammad Abubakr & Farid, Saqib & Yousaf, Imran & Kang, Sang Hoon, 2023. "Asymmetric efficiency in petroleum markets before and during COVID-19," Resources Policy, Elsevier, vol. 86(PA).

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    More about this item

    Keywords

    CAViaR; Cryptocurrencies; Tail risk transmission; Technology-driven markets;
    All these keywords.

    JEL classification:

    • 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
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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