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

Ding, Shusheng; Cui, Tianxiang; Wu, Xiangling; Du, Min

Abstract

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.

Citation

Ding, S., Cui, T., Wu, X., & Du, M. (2022). Supply chain management based on volatility clustering: The effect of CBDC volatility. Research in International Business and Finance, 62, Article 101690. https://doi.org/10.1016/j.ribaf.2022.101690

• Journal Article Type: Article
• Acceptance Date: May 25, 2022
• Online Publication Date: Jun 6, 2022
• Publication Date: 2022-12
• Deposit Date: Jun 15, 2022
• Publicly Available Date: Jun 15, 2022
• Journal: Research in International Business and Finance
• Print ISSN: 0275-5319
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Article Number: 101690
• DOI: https://doi.org/10.1016/j.ribaf.2022.101690
• Keywords: CBDC, Volatility clustering, Machine learning, Digital currency, Supply chain management
• Public URL: http://researchrepository.napier.ac.uk/Output/2879113