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On the Prediction of Systemic Risk Tolerance of Cryptocurrencies

Author

Listed:
  • Sabri Boubaker

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie)

  • Sitara Karim

    (Sunway University [Malaysia])

  • Muhammad Abubakr Naeem

    (Swansea University)

  • Molla Ramizur Rahman

Abstract

The role of big data in finance is pivotal, especially in forecasting stock prices, mitigating risk, and assessing market anomalies. With the financial system becoming more interconnected, analytical models using large data are gaining prominence in developing risk spillover models. This study estimates the systemic risk tolerance of twenty-five high-valued cryptocurrencies and finds that Fantom has the highest tolerance, while Bitcoin and Ethereum have a lower tolerance due to their large market share. It also shows that the common trend of cryptocurrencies enhances each other's tolerance and develops a predictive model for systemic risk tolerance. The study can help investors and market participants devise strategies for safe haven investment, hedging, and speculation during a market downturn. \textcopyright 2023 Elsevier Inc.

Suggested Citation

  • Sabri Boubaker & Sitara Karim & Muhammad Abubakr Naeem & Molla Ramizur Rahman, 2024. "On the Prediction of Systemic Risk Tolerance of Cryptocurrencies," Post-Print hal-04432400, HAL.
    • Handle: RePEc:hal:journl:hal-04432400
    DOI: 10.1016/j.techfore.2023.122963
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