Goal Programming Techniques for Bank Asset Liability Management

Preface1: Introduction 1. Asset liability management 1.1 ALM model structure 1.1.1 Objective functions1.1.1.1 The Von Neumann-Morgenstern theory1.1.1.2 Classical utility functions1.1.1.3 The Von Neumann-Morgenstern theory and utility functions1.2 Asset management models1.2.1 Stochastic programming 1.2.2 Decision rules 1.2.3 Capital growth1.2.4 Stochastic control1.2.5 Advantages and disadvantages of the four approaches1.3 Applications of the asset liability management model 2. General characteristics of the banking institutions2.1 The economic role of banking institutions 2.2 Management of commercial banks2.3 Basic policies of commercial banks2.3.1 The accumulation of capital2.3.2 Loans 2.3.3 Liquidity2.4 Economic statements3. Uncertainty in the banking risk management 3.1 Risk of financial institutions 3.2 Evaluation and management risk techniques4. 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. Data3. 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-optimality4. 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