A Dynamic Risk-Sharing Framework for Sustainable Banking Performance

Thesis Abstract

In the wake of increasing operational and market volatility, banks face heightened demand for dynamic risk allocation mechanisms that align profitability with long-term sustainability and stakeholder resilience. This study develops a Dynamic Risk-Sharing Framework (DRSF) designed to optimize capital allocation, liquidity management, and risk transfer decisions under evolving macroeconomic and regulatory environments, thereby enhancing sustainable banking performance. The aim is to integrate multi-tier risk-sharing contracts, real options reasoning, and payoff-based risk measures to produce adaptive decision rules for lending, asset-liability management, and equity funding. Specific objectives are (1) to conceptualize a dynamic risk-sharing model that unites credit, market, and operational risks within a single framework; (2) to quantify the impact of risk-sharing arrangements on bank performance indicators, including return on risk-adjusted capital (RORAC), sustainable funding costs, and resilience to tail events; (3) to identify governance and information-structure determinants that facilitate or hinder effective risk sharing; and (4) to simulate policy stress scenarios to evaluate framework robustness under climate-related and systemic shocks. The methodology adopts a mixed-methods design combining theoretical development, empirical validation, and scenario analysis anchored in contract theory and risk management literature. The study uses a population of commercial and retail banks across five major economies, with a target sample of 120 banks over a ten-year panel (2015–2024). Data sources include bank-level financial statements, risk disclosures, supervisory reports, and macroeconomic indicators from regulatory authorities and international databases. Instruments consist of structured firm-level survey instruments administered to risk managers, complemented by archival data. Validity and reliability are ensured through triangulation, pilot testing of survey items, and confirmatory factor analysis to validate constructs related to risk-sharing capability, governance quality, and sustainability orientation. Analytical techniques include panel data regression (fixed and random effects), system GMM to address endogeneity, and quantile regression to capture heterogeneous effects across performance distributions. A dynamic stochastic general equilibrium-inspired model component will be integrated to capture intertemporal risk-sharing decisions, while Monte Carlo simulations assess resilience across stress scenarios. The model specification embeds a dynamic risk-sharing constraint that links capital adequacy, liquidity buffers, and credit risk transfer costs to expected sustainable profitability. The theoretical underpinning draws on risk-sharing theory, portfolio choice under uncertainty, and the natural-resources-inspired notion of resilience, with explicit references to the Basel III framework and the evolving IFRS 9 impairment rules. Anticipated findings indicate that banks implementing a Dynamic Risk-Sharing Framework exhibit statistically significant improvements in RORAC, reduction in cost of risk, and enhanced liquidity resilience during adverse conditions. The results are expected to demonstrate that adaptive risk-sharing arrangements, supported by transparent governance mechanisms and information-sharing protocols, reduce dependency on external funding during stress periods and improve capital efficiency without compromising lending to productive sectors. The study also anticipates heterogeneity across bank size, ownership structure, and regional regulatory regimes, with larger banks leveraging more sophisticated hedging and risk-transfer channels, while smaller banks benefit disproportionately from governance-driven information symmetry. The contribution to knowledge resides in the integration of dynamic risk-sharing contracts into a coherent framework that directly links risk transfer, capital adequacy, and sustainable performance, offering a practical blueprint for banks to align risk management with long-term value creation under uncertainty. Policy implications include recommendations for supervisory guidance on risk-sharing disclosures, governance standards for risk-sharing committees, and calibration of stress-testing protocols to incorporate dynamic risk-sharing effects. The study concludes that a calibrated Dynamic Risk-Sharing Framework enhances sustainable bank performance by improving risk-adjusted returns, lowering fragility, and facilitating prudent growth. Recommendations for practice emphasize the development of standardized risk-sharing instruments, robust data infrastructures for real-time risk transfer analytics, and ongoing monitoring of governance antecedents that enable effective implementation.