Central bank digital currency,loan supply,and bank failure risk:a microeconomic approach

Central bank digital currencies(CBDCs),which are legal tenders in digital form,are expected to reduce currency issuance and circulation costs and broaden the scope of monetary policy.In addition,these currencies may also reduce consumers’need for conventional demand deposits,which,in turn,increases banks’loan provision costs because deposits require higher rates of return.We use a microeconomic banking model to investigate the effects of introducing an economy-wide,account-type CBDC on a bank’s loan supply and its failure risk.Given that a CBDC is expected to lower the cost of liquidity circulation and become a strong substitute for demand deposits,both the loan supply and the bank failure risk increase.These increases are countered by subsequent increases in the rates of return on term deposits and loans,which,in turn,reduce the loan supply and thus bank failure risk.These offsetting forces lead to no significant change in banking,as long as the rate of return on loans is below a certain threshold.However,once the rate is above the threshold,bank failure risk increases,thereby undermining banking stability.The problem is more pronounced when the degree of pass-through of funding costs to the loan rate is high and the profitability of a successful project is low.Our results imply that central banks wishing to introduce an economy-wide,account-type CBDC should first monitor yields on bank loans and consider policy measures that induce banks to maintain adequate liquidity reserve levels.

The Digital Currency of China’s Central Bank:Digital Currency Electronic Payment(DCEP)

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.

The impact of FinTech start-ups on incumbent retail banks’share prices

Background:This study aims to clarify the role of FinTech digital banking start-ups in the financial industry.We examine the impact of the funding of such start-ups on the stock returns of 47 incumbent US retail banks for 2010 to 2016.Methods:To capture the importance of FinTech start-ups,we use data on both the dollar-volume of funding and number of deals.We relate these to the stock returns with panel data regression methods.Results:Our results indicate a positive relationship exists between the growth in FinTech funding or deals and the contemporaneous stock returns of incumbent retail banks.Conclusions:Although these results suggest complementarity between FinTech and traditional banking,we note that our results at the banking industry level are not statistically significant,and that the coefficient signs for about one-third of the banks are negative,but not statistically significant.Since the FinTech industry is young and our sample period short,we cannot rule out that our findings are spurious.

Economic uncertainty,central bank digital currency,and negative interest rate policy

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.

从监管视角分析中央银行数字货币与私人加密货币的竞争均衡

This paper constructs a searching model to study the impact of monetary policy,fiscal policy,transaction costs of private cryptocurrency and regulatory uncertainty on the competitive equilibrium between Central Bank Digital Currency and private cryptocurrency.The results show:(1)Central Bank Digital Currency and private crypto-currency can conditionally coexist.(2)The level of economic welfare is related to monetary policy and fiscal policy but independent from the issuance and transaction of private cryptocurrency.(3)Under complete competition of private cryptocurrency mining,the mining cost becomes deadweight loss for whole society.

Consumer choices under new payment methods

This study suggests a payment portfolio model that includes new payment methods that have emerged from the development of cryptocurrency markets and central bank digital currencies(CBDCs).Our model analyzes the optimal payment choice for consumers under various macroeconomic conditions.We determine that an individual economic agent chooses payment methods under specific conditions by incorporating policy interest rates on CBDCs and stablecoins used on cryptocurrency exchanges.We analyze the impacts of CBDCs and stablecoins on the choice of whether to use cash or deposits.We also examine how the agent changes her portfolio compositions in response to exogenous macroeconomic policies.If a government replaces cash with a CBDC,the convenience of digital currency would not affect consumer choices.The higher the government’s interest rate on CBDCs,the more consumers will use CBDCs than deposits.

Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China

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.