Maksym Ivasenko

The purpose of the study is to explore the influence of central bank digital currency on stock markets. To realize the purpose, the TVP-VAR model was built, which determines the impact of volatility of the CBDC attention index (CBDCAI) on the volatility of stock market indices. The study uses a time-varying vector autoregressive model that analyzes weekly data from the first week of January 2015 to the first week of July 2023. The endogenous vector to be assessed by VAR contains CBDCAI and stock market indices of different countries (France: CAC 40, The United States of America: S&P 500, Germany: DAX 40, United Kingdom: FTSE 100, China: SSEC, The Netherlands AEX 25, Switzerland: SMI 20, Japan: Nikkei 225, India: NIFTY 50, Brazil: BVSP, South Korea: KOSPI). The results of the TVP-VAR model show that compared to stock market indices, CBDCAI appeared to be relatively independent and isolated. Interdependence and mutual influence between the digital currency market of central banks and stock markets were also revealed. In addition, CBDC functions primarily as a volatility absorber rather than a source of volatility. Despite the overall ability of the CBDC market to absorb fluctuations in volatility, it may also change its function with the widespread adoption of central bank digital currencies in many countries.

This paper investigates the interaction between decentralized financial services and the traditional banking system by building VAR models, conducting Granger causality tests, building impulse response functions, and performing variance analysis. To implement the model, banking indicators of the USA, India, and Great Britain were selected: the volume of commercial and industrial loans, interest rate, consumer price index, total liabilities and capital of banks, aggregate deposits, federal funds rate (for the USA), and repo rate (for India). The study examined central bank data of the specified countries from July 2018 to January 2024 with the TVL indicator, which measures the sum of all assets locked in DeFi protocols. The results of the impulse response function (IRF) for countries demonstrate different interactions between TVL and bank indicators. The US response to TVL shocks demonstrates a stimulative monetary policy, with significant Fed rate reductions and increased commercial lending to boost economic activity. In contrast, India’s monetary stimulus, marked by declining repo rates and growth in banking sector liabilities and deposits, aims to enhance economic resilience. The UK, however, adopts a conservative monetary approach, with sharp bank rate increases and mixed lending and deposit responses, prioritizing financial stability. Analysis across these nations highlights different impacts of financial indicators on TVL. In the US, the evolving relationship between TVL and bank indicators reflects the financial system’s complexity. India’s sensitivity to monetary policy, credit conditions, and inflation significantly influences TVL. In the UK, central bank decisions, particularly the bank rate, play a crucial role in financial market dynamics.

Acknowledgment
The authors appreciate the assistance in the preparation of the article provided by the University of Debrecen Program for Scientific Publication and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

This article highlights the results of a study investigating whether the growth of syndicated loan activity among US commercial banks was driven by measurable operational cost savings through blockchain-powered back-office automation. Quarterly data from Q1 2010 to Q4 2024 on syndicated loan stocks, commercial and industrial loans, real GDP, bank assets, and non-interest expenses were obtained from the Federal Reserve System’s FRED database. A dummy variable was applied after 2016 to denote the implementation of the first production-level Distributed Ledger Technology (DLT) pilots. Using the Autoregressive Distributed Lag Model (ARDL) bounds testing approach, evidence of cointegration is found and long-run elasticity is estimated: a steady 1% increase in the volume of syndicated loans reduces the operating expense ratio by 0.147%, which means that almost doubling the volume of loans in the resulting sample leads to approximately 15% structural reduction in the burden on banks’ back offices. The associated error correction model gives a short-run elasticity of –0.276 (i.e., a 1% quarterly shock to loan volume reduces expenses by 0.276 p.p.) and a 47% correction rate to a new equilibrium. Diagnostic tests confirm the absence of sequential correlation and resistance to heteroscedasticity by White’s standard errors. System-wide process improvements were evaluated by examining Hyperledger Fabric’s permissioned channel blockchain, smart contract automation, and multi-signature approval policies, which together simplify Know Your Customer (KYC) document workflows and settlement processes. The findings provide empirical evidence that enterprise DLT platforms deliver significant cost reductions for syndicated loan transactions, with implications for bank, fintech, and regulatory strategies.