ECONOMETRIC ANALYSIS OF THE FACTORS AFFECTING INTERBANK COMPETITION IN THE DIGITAL BANKING SERVICES MARKET

Authors

  • Karimov Odiljon Boqiyevich

DOI:

https://doi.org/10.5281/zenodo.21309137

Keywords:

interbank competition, digital banking, net interest margin (NIM), Herfindahl–Hirschman Index (HHI), Engle–Granger cointegration, ARDL model, OLS regression, Granger causality, Newey–West HAC, Uzbekistan.

Abstract

This article presents a comprehensive econometric analysis of the factors affecting interbank
competition in Uzbekistan’s digital banking services market. Based on official quarterly data from the Central
Bank of Uzbekistan for the period 2018 Q1–2024 Q4 (28 quarters) and monthly data from January 2024 to
January 2026 (25 months), the study applies a ten-stage econometric framework, including the Augmented
Dickey–Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root tests, the Engle–Granger
cointegration test, an Autoregressive Distributed Lag (ARDL) model, multivariate OLS regression with Newey–
West HAC standard errors, robustness checks across six alternative specifications, the Chow structural break
test, and bidirectional Granger causality testing. The empirical findings reveal a statistically significant long-run
negative relationship between remote banking users and the net interest margin (NIM): the OLS coefficient is β
= −1.144 (p < 0.001), with R² = 0.818 and F = 48.43, while the ARDL long-run coefficient of β = −1.247 confirms
the robustness of the result. The Engle–Granger test confirms cointegration between NIM and log(remote
users) at the 1 percent significance level (test statistic = −4.124, p = 0.008). The Herfindahl–Hirschman Index
(HHI) declined from 1,500 to 924, representing a 38.4 percent reduction over the analysis period, with a
trend coefficient of −20.06 units per quarter (p < 0.001). Granger causality testing confirms an asymmetric
relationship: digital service users Granger-cause NIM (F = 8.67, p = 0.008), but not vice versa. State-owned
banks exhibit significantly higher NPL levels than private banks (Cohen’s d = 0.664; t = 3.32, p = 0.003).

Author Biography

Karimov Odiljon Boqiyevich

Independent Researcher at Tashkent State University of Economics

References

Decree of the President of the Republic of Uzbekistan No. PF-5992 dated May 12, 2020, “On the Strategy

for Reforming the Banking System of the Republic of Uzbekistan for 2020–2025.” National Database of

Legislation of the Republic of Uzbekistan. https://lex.uz/docs/-4811025

Central Bank of the Republic of Uzbekistan. Statistical Bulletins and Banking System Indicators, 2018–

Tashkent: Central Bank of the Republic of Uzbekistan.

International Monetary Fund. Republic of Uzbekistan: Financial Sector Assessment Program—Financial

System Stability Assessment. IMF Country Report No. 25/145. Washington, D.C.: International Monetary

Fund, 2025.

Lerner, A.P. The concept of monopoly and the measurement of monopoly power. The Review of Economic

Studies. 1934. Vol. 1, No. 3. P. 157–175.

Panzar, J.C., Rosse, J.N. Testing for “monopoly” equilibrium. The Journal of Industrial Economics. 1987.

Vol. 35, No. 4. P. 443–456.

Boone, J. A new way to measure competition. The Economic Journal. 2008. Vol. 118, No. 531. P. 1245–

Basel Committee on Banking Supervision. Literature Review on Financial Technology and Competition for

Banking Services. BCBS Working Papers No. 43. Basel: Bank for International Settlements, 2024.

Koont, N. The Digital Banking Revolution: Effects on Competition and Stability. Stanford Graduate School

of Business Working Paper No. 4221. Stanford, 2023.

Dickey, D.A., Fuller, W.A. Distribution of the estimators for autoregressive time series with a unit root.

Journal of the American Statistical Association. 1979. Vol. 74, No. 366. P. 427–431.

Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y. Testing the null hypothesis of stationarity against the

alternative of a unit root. Journal of Econometrics. 1992. Vol. 54, No. 1–3. P. 159–178.

Engle, R.F., Granger, C.W.J. Co-integration and error correction: Representation, estimation, and testing.

Econometrica. 1987. Vol. 55, No. 2. P. 251–276.

Pesaran, M.H., Shin, Y., Smith, R.J. Bounds testing approaches to the analysis of level relationships.

Journal of Applied Econometrics. 2001. Vol. 16, No. 3. P. 289–326.

Newey, W.K., West, K.D. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent

covariance matrix. Econometrica. 1987. Vol. 55, No. 3. P. 703–708.

Granger, C.W.J. Investigating causal relations by econometric models and cross-spectral methods.

Econometrica. 1969. Vol. 37, No. 3. P. 424–438.

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Published

2026-07-01