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.

Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence for Peru

This paper quantifies and assesses the impact of an adverse loan supply (LS) shock on Peru's main macroeconomic aggregates using a Bayesian vector autoregressive (BVAR) model in combination with an identification scheme with sign restrictions. The main results indicate that an adverse LS shock: (i) reduces credit and real GDP growth by 372 and 75 basis points in the impact period, respectively; (ii) explains 11.2% of real GDP growth variability on average over the following 20 quarters; and (iii) explained a 180-basis point fall in real GDP growth on average during 2009Q1-2010Q1 in the wake of the Global Financial Crisis (GFC). Additionally, the sensitivity analysis shows that the results are robust to alternative identification schemes with sign restrictions; and that an adverse LS shock has a greater impact on non-primary real GDP growth.

Heterogeneity in the Relationship Between NPLs and Real Economy: Evidence from the Mongolian Banking System

The paper analyses the relationship between the banks’ credit risk and macroeconomic conditions by addressing the following questions; (i) How are macroeconomic shocks transmitted to lending risk depending on the ban-specific features? (ii) Are the effects of macroeconomic shocks different across the loan portfolios in various economic sectors? Unlike the common assumption in the literature, the empirical analysis considers banks’ heterogeneity and diversification across borrowers. It employs heterogeneous panel SVARs and standard SVAR models on a dataset from 2002. Q1 to 2019.Q1. The results suggest that the deterioration in credit quality is affected by both macroeconomic and bank-specific factors, with substantial heterogeneity in the magnitudes and timing in terms of the type of loans in various business sectors and bank characteristics. In particular, we find strong evidence of cyclical sensitivity of loan quality, and about 1/4 of banks’ NPLs increases stronger in response to the shocks to growth, exchange rate, interest rate, and profitability. The highly profitable banks tend to less engage in excessive risk-taking, resulting in lower NPLs, whereas the relation of asset size to NPLs is not significant for the sample. A growth shock plays a prominent role in explaining the variation of NPLs for the trade and mining sectors. Similarly, the loan supply shock is the main determinant for the construction sector’s NPLs, while the exchange rate shock is the most responsible for the manufacturing sector. The interest rate shock and exchange rate shock are the most effective factors on NPLs of consumer loans. Finally, the feedback effect of NPLs shows that deterioration of credit quality slows down economic growth.