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 ()
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Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

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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; Carbon allowance; Sustainability; Climate risks; Wavelet approach; Carbon emission price (search for similar items in EconPapers)
Date: 2024-10
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Published in Finance Research Letters, 2024, 68, pp.105962. ⟨10.1016/j.frl.2024.105962⟩

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Journal Article: Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05238460

DOI: 10.1016/j.frl.2024.105962

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