DCEP is the Chinese version of Central Bank Digital Currency(CBDC).It is the only legal digital currency in China and meets four conditions:(a)it is issued by the central bank;(b)it is digitized;(c)it is account and w...DCEP is the Chinese version of Central Bank Digital Currency(CBDC).It is the only legal digital currency in China and meets four conditions:(a)it is issued by the central bank;(b)it is digitized;(c)it is account and wallet based;(d)it is oriented towards the general public.As a retail central bank digital currency,it has three main technical features:a“tiered limit arrangement”(small-scale payments can be made anonymously while large-scale payments cannot),a“two-tier operating system”(as with the central bank-commercial bank traditional model),and a“dual offline payment system”(supporting both parties of the transaction).Compared with CBDCs in other countries,China’s DCEP has smaller economic impacts,more obscure strategic goals,and more scarce technical details.But its progress in testing is ahead of central banks of other countries.This article is based on public information and is intended to explain what DCEP is and why and how it was developed.It also offers suggestions for future research.展开更多
The covID-19 outbreak has brought unprecedented social attention to economic uncertainty and negative interest rate policy(NIRP).How does uncertainty affect economic activity,and how effective is a NIRP based on centr...The covID-19 outbreak has brought unprecedented social attention to economic uncertainty and negative interest rate policy(NIRP).How does uncertainty affect economic activity,and how effective is a NIRP based on central bank digital currency(CBDC)?To answer the two questions,we constructed a dynamic stochastic general equilibrium(DSGE)model that accommodates sticky prices and wages.The results indicated:(i)Economic uncertainty has substantially reduced investment,output,wage,and loans,which increases unemployment risk.In the short term,it has triggered impulsive consumption by households,while consumption has fallen into a slump in the long run.(ii)After suffering an uncertainty shock,the economy entered short-term stagflation and long-term deflation.The short-term stagflation was mainly caused by resident wage adjustment,and the long-term deflation was due to the decline in effective demand caused by unemployment risk.(ii)CBDC could eliminate the zero lower bound(ZLB)constraint,thereby improving the effectiveness of NIRP.Compared with traditional currency,CBDCbased NIRP could more effectively smooth macroeconomic fluctuations and alleviate the negative impact of an uncertainty shock,which is more conducive to restoring market confidence and promoting economic recovery.展开更多
With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considere...With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.展开更多
文摘DCEP is the Chinese version of Central Bank Digital Currency(CBDC).It is the only legal digital currency in China and meets four conditions:(a)it is issued by the central bank;(b)it is digitized;(c)it is account and wallet based;(d)it is oriented towards the general public.As a retail central bank digital currency,it has three main technical features:a“tiered limit arrangement”(small-scale payments can be made anonymously while large-scale payments cannot),a“two-tier operating system”(as with the central bank-commercial bank traditional model),and a“dual offline payment system”(supporting both parties of the transaction).Compared with CBDCs in other countries,China’s DCEP has smaller economic impacts,more obscure strategic goals,and more scarce technical details.But its progress in testing is ahead of central banks of other countries.This article is based on public information and is intended to explain what DCEP is and why and how it was developed.It also offers suggestions for future research.
基金the National Planning Office of Philosophy and Social Science of China(Grant No.21BJY206)。
文摘The covID-19 outbreak has brought unprecedented social attention to economic uncertainty and negative interest rate policy(NIRP).How does uncertainty affect economic activity,and how effective is a NIRP based on central bank digital currency(CBDC)?To answer the two questions,we constructed a dynamic stochastic general equilibrium(DSGE)model that accommodates sticky prices and wages.The results indicated:(i)Economic uncertainty has substantially reduced investment,output,wage,and loans,which increases unemployment risk.In the short term,it has triggered impulsive consumption by households,while consumption has fallen into a slump in the long run.(ii)After suffering an uncertainty shock,the economy entered short-term stagflation and long-term deflation.The short-term stagflation was mainly caused by resident wage adjustment,and the long-term deflation was due to the decline in effective demand caused by unemployment risk.(ii)CBDC could eliminate the zero lower bound(ZLB)constraint,thereby improving the effectiveness of NIRP.Compared with traditional currency,CBDCbased NIRP could more effectively smooth macroeconomic fluctuations and alleviate the negative impact of an uncertainty shock,which is more conducive to restoring market confidence and promoting economic recovery.
基金supported by the National Science Foundation of China(No.61602536)the Emerging Interdisciplinary Project of Central University of Finance and Economics(CUFE),and Financial Sustainable Development Research Team.
文摘With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.
