| 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 |
|
| DOI |
10.1016/j.frl.2024.105962 |
| 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. |
| Author Keywords |
Digital market risk; Long-term memory; Bonds; Temporal sequence learning architectures |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:001299936800001 |
| WoS Category |
Business, Finance |
| Research Area |
Business & Economics |
| PDF |
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