International Journal of Business Management and Economics and Trade, 2026, 7(2); doi: 10.38007/IJBMET.2026.070205.
Minxia Liu
State Street Corporation, SSGA Trust Company, Boston, 02114, MA, US
The Regulatory Environment in the Post-2023 Period has Changed the Standards for Assessing Financial Risk Management Tools. Capital and liquidity ratios are no longer sufficient on their own because the revised credit, market, operational, credit valuation adjustment and governance rules now require institutions to demonstrate resilience under combined risks, model limitations and severe but plausible scenarios. Evaluate the effectiveness of capital buffers, value-at-risk and expected shortfall, supervisory stress testing, liquidity coverage tools, hedging and risk-transfer instruments, and model-risk governance in this study. Based on the requirements of CRR3/CRD6 and recent empirical studies, descriptive data from the European Banking Authority's risk dashboards, and the 2023 and 2025 EU-wide stress tests are integrated for this analysis. The five indices of performance are: loss absorption capacity, tail-risk sensitivity, liquidity survival, operational usability and governance strength. The results show that regulatory capital and liquidity tools are still effective first-line defences: EU/EEA liquidity coverage and net stable funding ratios remain significantly above the 100% minimum, and the adverse end-point aggregate CET1 ratio has risen from 10.4% in the 2023 exercise to 12.1% in the 2025 exercise. It is not effective in all circumstances. Static balance-sheet assumptions, model risk, foreign-currency liquidity gaps, regulatory arbitrage, episodic investment in risk-management capacity and weak integration of cyber, climate and counterparty channels reduce the reliability of individual tools. Therefore, the paper puts forward an all-encompassing, evidence-based system that integrates dynamic multi-scenario stress testing, model-risk controls, granular liquidity analysis, transparent hedging limits and board-level accountability. Therefore, under good coordination among all the tools, the new regulatory environment will be relatively stable; otherwise, these individual compliance indicators might be mistaken for real safety indicators.
Financial Risk Management; CRR3; CRd6; Basel Iii; Stress Testing; Liquidity Coverage Ratio; Expected Shortfall; Model Risk
Minxia Liu. Evaluation of the Effectiveness of Financial Risk Management Tools Under the New Regulatory Environment. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 2: 35-50. https://doi.org/10.38007/IJBMET.2026.070205.
[1] Łuczak P. Credit Risk Management in Commercial Banks[J]. Przegląd Prawno-Ekonomiczny, 2025 (3): 11-21.
[2] Van Cleynenbreugel P. Administrative design obligations in European Union secondary legislation: the emerging contours and constitutional cracks of an implicit legislative template[J]. 2026.
[3] Aikman, D., Beale, D., Brinley-Codd, A., Covi, G., Hüser, A.-C., & Lepore, C. (2023). Macroprudential Stress-Test Models: A Survey. IMF Working Paper WP/23/173. International Monetary Fund.
[4] Han, X. (2026). Research on Standardized Pathways for Automotive Manufacturing Process Quality Systems Oriented Toward Defect Prevention. Advances in Computer and Communication, 7(2).
[5] Zhang, Z. (2026). Research on Performance Monitoring and Data-driven Optimization Mechanisms for AI Systems Oriented Toward Decision Support. Advances in Computer and Communication, 7(2).
[6] Ma, X. (2026). Research on the Design and Application of Observability Architectures for High-reliability Distributed Systems in Cloud-native Environments. Engineering Advances, 6(2).
[7] Wang, Z. (2026). Earnings Quality of Resource Enterprises Under Commodity Price Volatility Transmission.
[8] Ma, W. (2026). Reliable Data Infrastructure Supported by Automated Data Pipelines. Engineering Advances, 6(2).
[9] Jin, C. (2026). Research on the Optimization Strategy of Retail Enterprise Product Portfolio Based on Association Rule Analysis. Advances in Computer and Communication, 7(2).
[10] Wang, Z. (2026). Data-driven Pathways for Optimizing Supply Chain Operational Efficiency in Physical Industries. Advances in Computer and Communication, 7(2).
[11] Han, X. (2026). Research on Adaptive Optimization Methods for Multi-Objective Manufacturing Process Parameters in Complex Assembly Processes. Procedia Computer Science, 279, 366-374.
[12] Wang, N. (2026). Construction and Application of Interpretable Enterprise Performance Prediction Model Based on Multi-Source Operational Data.
[13] Ma, X. (2026). Distributed Fault Root Cause Localization and Self-Healing Strategy Generation Based on Causal Inference. Procedia Computer Science, 281, 753-760.
[14] Ma, W. (2026). Cloud Data Platform Governance Model under Infrastructure Automation.
[15] Wang, Z. (2026). Research on Data Analysis and Quality Prediction of Manufacturing Processes Based on Improved Deep Learning Algorithms. Procedia Computer Science, 281, 1328-1337.
[16] Chi, C. (2026). Research on Mortgage Asset Cash Flow Simulation and Risk Transmission Mechanisms across Structural Tranches Based on Loan-Level Data.
[17] Chi, C. (2026). Quantitative Evaluation of Tranche-Level Returns and Loss Distributions of Structured Credit Assets under Stress Scenarios.
[18] Su, J. (2026). Design and Implementation of Android High Reliability Communication Module Based on Hierarchical Architecture.
[19] Qian, X. (2026). Market Data–Driven Demand Forecasting and Inventory Coordination Mechanisms in Retail Supply Chains.
[20] Yang, Y. (2026). The Integration and Application of Ai Technology in Marketing Data Analysis and Business Decision-Making.
[21] Sun, L. (2026). Resource Scheduling and Elastic Scaling Optimization of Distributed Advertising Systems in Cloud-Native Environments.
[22] Wang, N. (2026). Research on Integrated Analysis Method of Sales and Operations Data for Management Decision Support.
[23] Han, X. (2026). Research on Manufacturing Deviation Vehicle Performance Transmission Mechanism and Intelligent Compensation Control for Automotive Engineering. Procedia Computer Science, 281, 594-602.