Tail risk transmission in technology-driven markets

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

Global Finance Journal, 2023, vol. 57, issue C

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.

Keywords: CAViaR; Cryptocurrencies; Tail risk transmission; Technology-driven markets (search for similar items in EconPapers)
JEL-codes: C32 D53 G01 G15 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:glofin:v:57:y:2023:i:c:s1044028323000509

DOI: 10.1016/j.gfj.2023.100855

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