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

Mahdi Ghaemi Asl, Sami Ben Jabeur, John W. Goodell and Anis Omri ()

Finance Research Letters, 2024, vol. 68, issue C

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.

Keywords: Digital market risk; Long-term memory; Bonds; Temporal sequence learning architectures (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:68:y:2024:i:c:s1544612324009929

DOI: 10.1016/j.frl.2024.105962

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