Supply chain management based on volatility clustering: The effect of CBDC volatility

• Published in: Research in international business and finance Vol. 62; p. 101690
• Main Authors: Ding, Shusheng, Cui, Tianxiang, Wu, Xiangling, Du, Min
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• Subjects: CBDC; Digital currency; Machine learning; Supply chain management; Volatility clustering; CBDC; E42; Supply chain management; Machine learning; Volatility clustering; Digital currency; D81
• ISSN: 0275-5319; 1878-3384
• DOI: 10.1016/j.ribaf.2022.101690

A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction. [Display omitted] •We adopt DBSCAN to meld bitcoin and GBP volatility for CBDC volatility effect.•We optimize the production plan along supply chain based on CBDC volatility.•We demonstrate the CBDC volatility effect on supply chain management.•Our result unveils that DBSCAN outperforms GARCH model in volatility clustering.