Goal Programming Techniques for Bank Asset Liability Management

16Other publications that exist on this topic, are mainly focused on the general aspects and methodologies of the field and do not refer extensively to bank ALM. On the other hand the existing books on goal programming techniques do not involve the ALM problem and more specifically the bank ALM one. Therefore, there is a lack in the existing literature of a comprehensive text book that combines both the concepts of bank ALM and goal programming techniques and illustrates the contribution of goal programming techniques to bank ALM. This is the major contributing feature of this book and its distinguishing characteristic as opposed to the existing literature.This volume would be suitable for academics and practitioners in operations research, management scientists, financial managers, bank managers, economists and risk analysts. The book can also be used as a textbook for graduate courses of asset liability management, financial risk management and banking risks.04Preface1: 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 book2: Review Of The Asset Liability Managem04ent 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 models3: 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 Naïve04 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 simulation4: 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 banks5: Conclusions And Future Perspectives 1. Summary of main findings 2. Issues for further researchReferences Subject Index