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

被引:17
|
作者
Ding, Shusheng [1 ]
Cui, Tianxiang [2 ]
Wu, Xiangling [1 ]
Du, Min [3 ]
机构
[1] Ningbo Univ, Sch Business, Ningbo, Zhejiang, Peoples R China
[2] Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo, Zhejiang, Peoples R China
[3] Edinburgh Napier Univ, Business Sch, Edinburgh, Scotland
关键词
CBDC; Volatility clustering; Machine learning; Digital currency; Supply chain management; PRICE VOLATILITY; BLOCKCHAIN; BITCOIN; GARCH; RISK; UNCERTAINTY; DESIGN; MODELS;
D O I
10.1016/j.ribaf.2022.101690
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
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页数:14
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