This article describes a quantitative framework for optimising bank balance sheet performance by integrating Significant Risk Transfer (SRT) transactions with dynamic, forward-looking asset allocation models. The research demonstrates that a constrained optimisation program that maximises profit under baseline economic forecasts while adhering to regulatory capital and liquidity constraints can substantially improve bank performance. Backtesting this model against the historical performance of major U.S. banks reveals significant gains, with an average improvement of 0.17 percentage points in Return on Assets and 1.98 percentage points in Return on Equity. Within the framework, SRTs function as a strategic tool, deployed not to directly generate profit but to alleviate binding capital and Return on Risk-Weighted Assets constraints, thereby unlocking more efficient and profitable asset allocations. The research also provides a disciplined approach to managing the inherent risks of SRTs, such as the cliff effect, by quantitatively modelling how the risk weights of retained tranches would deteriorate under stress, thus allowing for more robust, risk-aware strategic decisions. Ultimately, the findings present a practical, data-driven blueprint for banks to enhance risk-adjusted returns and achieve a competitive advantage through sophisticated, proactive balance sheet management.

Read the full article here: Optimising Bank Balance Sheets with SRT