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Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models

Author

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  • Ghaemi Asl, Mahdi
  • Ben Jabeur, Sami
  • Goodell, John W.
  • Omri, Anis

Abstract

We examine the impact of conventional, green, and Islamic bonds on the long-term memory of cryptocurrency market risk. Utilizing a time-varying parameter vector autoregressive deep learning model, we integrate time-varying parameter vector autoregressive methods with advanced deep learning sequence modeling architectures, including temporal convolutional network, gated recurrent unit, and long short-term memory for December 18, 2017, to April 19, 2024. Results indicate that incorporating all fixed-income securities reduces digital market risk. However, conventional and green bonds have a particularly strong impact on improving the long-term memory of digital market risk, while this is not the case for Sukuk.

Suggested Citation

  • Ghaemi Asl, Mahdi & Ben Jabeur, Sami & Goodell, John W. & Omri, Anis, 2024. "Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models," Finance Research Letters, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:finlet:v:68:y:2024:i:c:s1544612324009929
    DOI: 10.1016/j.frl.2024.105962
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