A Dynamic Risk-Adjustment Framework for Bank Liquidity Standards

Thesis Abstract

This study addresses the persistent gap between static regulatory liquidity thresholds and dynamic macroprudential risk in banking systems, where rigid standards inadequately capture evolving liquidity pressures and market shocks. The problem is the limited capacity of existing Basel-like frameworks to adjust liquidity risk buffers in real time, potentially leading to suboptimal capital allocation, procyclicality, and systemic vulnerability during stress episodes. The aim is to develop a Dynamic Risk-Adjustment Framework (DRAF) that calibrates bank liquidity standards through endogenous, data-driven adjustments anchored in observable risk indicators and exogenous macro-financial stress signals. Specific objectives are (1) to identify a comprehensive set of forward-looking liquidity risk drivers including intraday funding volatility, funding market liquidity premia, liquidity coverage ratio (LCR) dynamics, and macro-financial indicators; (2) to construct a dynamic adjustment mechanism that modulates liquidity buffers in response to evolving risk, incorporating regime-switching and Bayesian updating; (3) to validate the framework on a multi-country panel of banks with varying business models and regulatory regimes; (4) to compare the performance of the proposed framework against static Basel-like thresholds in terms of stability, procyclicality, and resilience during simulated stress scenarios; and (5) to derive policy implications for central banks and supervisory authorities regarding calibration of dynamic liquidity requirements. Methodologically, the study adopts a mixed-methods, positivist research design combining quantitative panel data analysis with a theoretical integration from liquidity risk management and dynamic asset-liability modeling. The population includes commercial banks from three advanced and three emerging-market economies over a ten-year window (2015–2024). The sample comprises approximately 180 banks, selected via stratified sampling to ensure representation by size, funding structure, and geographic region. Data collection relies on publicly available bank-level balance sheet and income statement data, supervisory liquidity indicators, daily and monthly market funding spreads, and macroeconomic variables sourced from central banks and international financial databases. Primary data are obtained through expert elicitation with 15 risk officers and 8 regulatory technologists to inform model specification and parameter priors. Instruments include a standardized liquidity risk questionnaire and structured interview guides, complemented by archival data. The analytical framework integrates (i) time-varying coefficient models to capture how the impact of risk drivers on liquidity buffers evolves over time, (ii) a Markov regime-switching model to identify shifts between stress and tranquil regimes, and (iii) a Bayesian hierarchical framework to synthesize cross-country heterogeneity and to update parameters as new information arrives. Model specification features a dynamic buffer calibration equation that links regulatory liquidity requirements to a composite risk score, incorporating intraday liquidity risk, funding market liquidity premia, and macro-financial stress indicators. Robustness checks include alternative priors, out-of-sample forecasting, and counterfactual simulations comparing dynamic versus static buffering policies. Hypothesis testing focuses on whether the dynamic framework reduces liquidity shortfalls during stress episodes, lowers procyclicality in capital allocation, and improves predictive accuracy of liquidity distress events. Econometric methods include panel vector autoregression (PVAR), generalized method of moments (GMM) estimators, and Bayesian dynamic model averaging to determine model-structure uncertainty. Complementary qualitative analysis uses thematic coding of interview transcripts to corroborate the relevance and operational feasibility of the proposed adjustment rules within bank risk governance. Key expected findings anticipate that the DRAF outperforms static thresholds in maintaining liquidity during shocks, with statistically significant reductions in shortfall incidences and improved forecast accuracy for liquidity distress. The framework is expected to reveal regime-dependent effects, where dynamic adjustments dampen procyclicality in credit supply and funding costs, while maintaining adequate liquidity buffers in buoyant periods. The study contributes to knowledge by integrating dynamic risk-based calibration into liquidity standards, advancing the theory of adaptive prudential regulation, and providing empirical evidence across diverse regulatory and macroeconomic contexts. Policy implications include guidance for central banks on enabling data-sharing, supervisory dashboards, and back-testing requirements for dynamic buffer calibration. The main conclusion posits that a properly specified Dynamic Risk-Adjustment Framework enhances financial stability without imposing undue constraints on bank credit creation, provided calibration is transparent, data-driven, and subject to rigorous governance controls. Recommendations emphasize (a) phased implementation with pilot testing, (b) standardization of risk-aggregation methods for cross-border comparability, and (c) continuous refinement of priors and model structures to reflect evolving market microstructure and macro-financial linkages.