Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach
This study aims to evaluate the efficiency of Russian banks, identify the factors influencing it based on their size and ownership type, and forecast future trends in the banking sector. The analysis utilized data from 680 Russian banks over the period 2000–2023, employing Data Envelopment Analysis...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Voprosy Ekonomiki
2025-03-01
|
| Series: | Russian Journal of Economics |
| Online Access: | https://rujec.org/article/144303/download/pdf/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850277676407848960 |
|---|---|
| author | Jalal Abu-Alrop |
| author_facet | Jalal Abu-Alrop |
| author_sort | Jalal Abu-Alrop |
| collection | DOAJ |
| description | This study aims to evaluate the efficiency of Russian banks, identify the factors influencing it based on their size and ownership type, and forecast future trends in the banking sector. The analysis utilized data from 680 Russian banks over the period 2000–2023, employing Data Envelopment Analysis (DEA) to measure technical efficiency, panel data analysis to determine efficiency-related variables, and Monte Carlo simulation to predict future performance for the years 2024–2026. The findings indicate a general decline in bank efficiency over time, driven by economic and political crises, particularly those linked to oil price fluctuations and sanctions. The study reveals that an increase in client funds (non-credit organizations) and higher leverage ratios are associated with improved bank efficiency. Among bank categories, mega-banks with assets exceeding 1.05 trillion rubles demonstrated the highest efficiency, followed by medium banks, large banks, and small banks, respectively. Moreover, Russian domestic banks exhibited higher efficiency levels compared to their foreign counterparts. The study forecasts continued increases in interest rates in the coming years, driven by the instability of the local currency and rising inflation caused by the Russia–Ukraine conflict. Significant changes in client funds (non-credit organizations) are also anticipated, with a decline expected in 2024, a temporary increase in 2025, and another decline in 2026. These fluctuations reflect instability stemming from corporate performance downturns and capital outflows due to economic sanctions. In addition, the operational efficiency of Russian banks is expected to decline, with an increase in the proportion of distressed banks, especially among small and large banks struggling with rising funding costs. The study concludes that funding sources, associated costs and leverage are the most important factors affecting the efficiency of Russian banks. |
| format | Article |
| id | doaj-art-b599c3171a5248fb9b646cf490a4f829 |
| institution | OA Journals |
| issn | 2405-4739 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Voprosy Ekonomiki |
| record_format | Article |
| series | Russian Journal of Economics |
| spelling | doaj-art-b599c3171a5248fb9b646cf490a4f8292025-08-20T01:49:47ZengVoprosy EkonomikiRussian Journal of Economics2405-47392025-03-01111769210.32609/j.ruje.11.144303144303Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approachJalal Abu-Alrop0Kazan Federal UniversityThis study aims to evaluate the efficiency of Russian banks, identify the factors influencing it based on their size and ownership type, and forecast future trends in the banking sector. The analysis utilized data from 680 Russian banks over the period 2000–2023, employing Data Envelopment Analysis (DEA) to measure technical efficiency, panel data analysis to determine efficiency-related variables, and Monte Carlo simulation to predict future performance for the years 2024–2026. The findings indicate a general decline in bank efficiency over time, driven by economic and political crises, particularly those linked to oil price fluctuations and sanctions. The study reveals that an increase in client funds (non-credit organizations) and higher leverage ratios are associated with improved bank efficiency. Among bank categories, mega-banks with assets exceeding 1.05 trillion rubles demonstrated the highest efficiency, followed by medium banks, large banks, and small banks, respectively. Moreover, Russian domestic banks exhibited higher efficiency levels compared to their foreign counterparts. The study forecasts continued increases in interest rates in the coming years, driven by the instability of the local currency and rising inflation caused by the Russia–Ukraine conflict. Significant changes in client funds (non-credit organizations) are also anticipated, with a decline expected in 2024, a temporary increase in 2025, and another decline in 2026. These fluctuations reflect instability stemming from corporate performance downturns and capital outflows due to economic sanctions. In addition, the operational efficiency of Russian banks is expected to decline, with an increase in the proportion of distressed banks, especially among small and large banks struggling with rising funding costs. The study concludes that funding sources, associated costs and leverage are the most important factors affecting the efficiency of Russian banks.https://rujec.org/article/144303/download/pdf/ |
| spellingShingle | Jalal Abu-Alrop Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach Russian Journal of Economics |
| title | Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach |
| title_full | Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach |
| title_fullStr | Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach |
| title_full_unstemmed | Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach |
| title_short | Assessing and forecasting the efficiency of Russian banks (2000–2026): A DEA, panel data, and Monte Carlo simulation approach |
| title_sort | assessing and forecasting the efficiency of russian banks 2000 2026 a dea panel data and monte carlo simulation approach |
| url | https://rujec.org/article/144303/download/pdf/ |
| work_keys_str_mv | AT jalalabualrop assessingandforecastingtheefficiencyofrussianbanks20002026adeapaneldataandmontecarlosimulationapproach |