TY - JOUR
T1 - Supply chain management based on volatility clustering
T2 - the effect of CBDC volatility
AU - Ding, Shusheng
AU - Cui, Tianxiang
AU - Wu, Xiangling
AU - Du, Min
N1 - Funding Information:
This paper is supported by the Academy of Longyuan Construction Financial Research Grant (Grant Number: LYZDB2004 ) and is also supported by International Research Center for Sustainable Finance .
Publisher Copyright:
© 2022 The Authors
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - CBDC
KW - Digital currency
KW - Machine learning
KW - Supply chain management
KW - Volatility clustering
UR - http://www.scopus.com/inward/record.url?scp=85131912502&partnerID=8YFLogxK
U2 - 10.1016/j.ribaf.2022.101690
DO - 10.1016/j.ribaf.2022.101690
M3 - Article
AN - SCOPUS:85131912502
SN - 0275-5319
VL - 62
JO - Research in International Business and Finance
JF - Research in International Business and Finance
M1 - 101690
ER -