Goal Programming Techniques for Bank Asset Liability Management

Preface

1: Introduction
1. Asset liability management
1.1 ALM model structure
1.1.1 Objective functions
1.1.1.1 The Von Neumann-Morgenstern theory
1.1.1.2 Classical utility functions
1.1.1.3 The Von Neumann-Morgenstern theory and utility functions
1.2 Asset management models
1.2.1 Stochastic programming
1.2.2 Decision rules
1.2.3 Capital growth
1.2.4 Stochastic control
1.2.5 Advantages and disadvantages of the four approaches
1.3 Applications of the asset liability management model
2. General characteristics of the banking institutions
2.1 The economic role of banking institutions
2.2 Management of commercial banks
2.3 Basic policies of commercial banks
2.3.1 The accumulation of capital
2.3.2 Loans
2.3.3 Liquidity
2.4 Economic statements
3. Uncertainty in the banking risk management
3.1 Risk of financial institutions
3.2 Evaluation and management risk techniques
4. The proposed methodological approach and the objective of the book
2: Review Of The Asset Liability Management Techniques
1. Asset liability management techniques
1.1 Deterministic models
1.1.1 Multiobjective linear programming model
1.2 Stochastic models
1.2.1 Chance constrained programming models
1.2.2 Sequential decision theoretic approach
1.2.3 Dynamic programming
1.2.4 Stochastic linear programming
1.2.5 Simulation models
1.2.6 Dynamic generalized networks
Appendix: Asset liability management programming models
3: Bank Asset Liability Management Methodology
1. Objective of the research
2. Data
3. Multiobjective linear programming
3.1 Simple methods of multiobjective linear programming
3.1.1 Lexicographic optimisation
3.1.2 Global criterion method
3.1.3 Interactive procedures
3.1.4 Goal programming
3.1.4.1 Goal programming as an extension of linear programming
3.1.5 The optimisation role
3.1.6 Dominance analysis
3.1.7 Issues related to goal programming model formulation
3.1.7.1 Dominance, inferiority and efficiency in goal programming solutions
3.1.7.2 Naïve relative weighting, incommensurability, naïve prioritization and redundancy in goal programming model formulation
3.1.7.3 Other goal programming algorithms and methodology
4. Interest rate simulation analysis
4.1 Monte Carlo simulation
4: Application
1. Description of the sample data
2. Formulation of the problem
2.1 Constraints
2.2 Goals
2.3 Mathematical formulation
3. Post-optimality
4. Interest rate simulation analysis
5. Analysis of results
5.1 Sensitivity analysis to the priorities of goals
5.2 Forecasting analysis
6. Policy and strategy standards of the banks
5: Conclusions And Future Perspectives
1. Summary of main findings
2. Issues for further research
References
Subject Index