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

Shusheng Ding, Tianxiang Cui, Xiangling Wu, Min Du

Research output: Journal PublicationArticlepeer-review

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.

Original languageEnglish
Article number101690
JournalResearch in International Business and Finance
Volume62
Early online date6 Jun 2022
DOIs
Publication statusPublished Online - 6 Jun 2022

Keywords

  • CBDC
  • Digital currency
  • Machine learning
  • Supply chain management
  • Volatility clustering

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Finance

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