| Title | Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models |
|---|---|
| ID_Doc | 62594 |
| Authors | Asl, MG; Ben Jabeur, S; Goodell, JW; Omri, A |
| Title | Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models |
| Year | 2024 |
| Published | |
| 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 longterm memory of digital market risk, while this is not the case for Sukuk. |
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