A Bank Asset and Liability Management Model

TLDR

In managing its assets and liabilities amid uncertainties in cash flows, cost of funds, and return on investments, a bank must determine its optimal trade‑off between risk, return, and liquidity. The study develops a multiperiod stochastic linear programming model for bank asset and liability management that incorporates institutional, legal, financial, and policy uncertainties while remaining computationally tractable. The model, applied to a 5‑year planning horizon for Vancouver City Savings Credit Union, is compared with a stochastic decision tree approach and is designed to be computationally tractable for realistic problem sizes. Results show that the stochastic ALM outperforms a deterministic linear programming model, is computationally comparable to it, is sensitive to stochastic cash‑flow asymmetry, and yields superior, more tractable policies than the stochastic decision tree model.

Abstract

In managing its assets and liabilities in light of uncertainties in cash flows, cost of funds and return on investments, a bank must determine its optimal trade-off between risk, return and liquidity. In this paper we develop a multiperiod stochastic linear programming model (ALM) that includes the essential institutional, legal, financial, and bank-related policy considerations, and their uncertainties, yet is computationally tractable for realistically sized problems. A version of the model was developed for the Vancouver City Savings Credit Union for a 5-year planning period. The results indicate that ALM is theoretically and operationally superior to a corresponding deterministic linear programming model, and that the effort required for the implementation of ALM, and its computational requirements, are comparable to those of the deterministic model. Moreover, the qualitative and quantitative characteristics of the solutions are sensitive to the model's stochastic elements, such as the asymmetry of cash flow distributions. We also compare ALM with the stochastic decision tree (SDT) model developed by S. P. Bradley and D. B. Crane. ALM is computationally more tractable on realistically sized problems than SDT, and simulation results indicate that ALM generates superior policies.