A Bank Asset and Liability Management Model

The uncertainty of a bank's cash flows, cost of funds and return on investments due to inherent factors and variable economic conditions has emphasized the need for greater efficiency in the management of asset and liabilities. A primary goal is to determine an optimal tradeoff 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, along with their uncertain aspects, yet is computationally tractable for realistic sized problems. A version of the model was developed for the Vancouver City Savings Credit Union for a five year planning period. The results indicate that ALM is theoretically and operationally superior to a corresponding deterministic linear programming model and the effort required for the implementation of ALM and the computational costs are comparable to those of the deterministic model. Moreover, the qualitative and quantitative characteristics of the solutions are sensitive to the stochastic elements of the model such as the asymmetry of the cash flow distributions. ALM was also compared with the stochastic decision tree (SDT) model developed by Bradley and Crane. ALM is more computationally tractable on realistic sized problems than SDT and simulation results indicate that ALM generates superior policies

Incorporating diversification into risk management

SMU-Wharton Research Centr

A Bank Asset and Liability Management Model

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 policie