dc.contributor.advisor | 陳春龍<br>蔡政憲 | zh_TW |
dc.contributor.author (Authors) | 黃孝慈 | zh_TW |
dc.creator (作者) | 黃孝慈 | zh_TW |
dc.date (日期) | 2008 | en_US |
dc.date.accessioned | 14-Sep-2009 09:19:12 (UTC+8) | - |
dc.date.available | 14-Sep-2009 09:19:12 (UTC+8) | - |
dc.date.issued (上傳時間) | 14-Sep-2009 09:19:12 (UTC+8) | - |
dc.identifier (Other Identifiers) | G0923565081 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/31134 | - |
dc.description (描述) | 博士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 資訊管理研究所 | zh_TW |
dc.description (描述) | 92356508 | zh_TW |
dc.description (描述) | 97 | zh_TW |
dc.description.abstract (摘要) | 當產險公司需要同時兼顧競爭力並免於破產時,適當的資產配置就是一項相當重要的決策。然而採用均數-變異數分析(mean‐variance analysis)將受到許多限制,而動態控制理論則是難以實作,因此,我們提出一個新的解決方法。這個方法主要係應用模擬最佳化的演算法,例如基礎的基因演算法(basic genetic algorithm, GA),多階層演化策略(multi-phase evolutionary strategies, MPES)及多階層基因演算法(multi-phase genetic algorithm, MPGA)等並結合模擬模型,來求解保險公司之資產配置的問題。首先我們建立投資市場及保險業務市場的模擬模型,之後再利用本研究所發展出新的最佳化演算法來搜尋最佳的資產配置。在實務上無法實現的多期投資策略,在我們的研究架構下得以被採用,並且在比較求解結果下,多期投資策略(reallocation strategies)較定額投資策略(re‐balancing strategies)有顯著較佳的績效。在兼顧保險公司投資收益並避免破產的目標函數下,我們所提出的研究方法已證明可以用來協助保險公司建立較佳的資產配置。 | zh_TW |
dc.description.abstract (摘要) | Proper asset allocations are vital for property‐casualty insurers to be competitive and remain solvent. However, popular mean‐variance analysis is limited while dynamic control theory is difficult to implement. We thus propose to apply simulation optimizations such as basic genetic algorithm (GA), multi‐phase evolutionary strategies (MPES) and multi‐phase genetic algorithm (MPGA) to the asset allocation problems of the insurers. We first construct a simulation model of the property‐casualty insurer and then develop simulation optimization techniques to search optimal investment strategies upon the simulation results. The resulted reallocation strategies perform better than re‐balancing strategies used in practice with significant margins. Therefore, our proposal researches can be used to assist insurers to construct better asset allocations. | en_US |
dc.description.tableofcontents | Research 1 COUPLING A MULTIPHASE GENETIC ALGORITHM WITH A SIMULATION MODEL TO SEARCH FOR THE OPTIMAL MULTIPERIOD ASSET ALLOCATIONS OF A PROPERTYCASUALTY INSURER............. .............................................................................................................................. 5 1. NTRODUCTION ...............................................................................................5 2. THE SIMULATION MODEL ............................................................................ 8 3. THE OPTIMIZATION PROBLEM AND ALGORITHM .................................. 13 3.1 The Optimization Problem ............................................................................... 13 3.2 The Basic GA ................................................................................................. 14 3.3 MPGA ............................................................................................................ 19 4. INVESTMENT STRATEGIES AND OPTIMIZATION RESULTS ................... 22 4.1 Investment Strategies ....................................................................................... 22 4.2 Optimization Results ........................................................................................ 23 5. SUMMARIES AND CONCLUSIONS ............................................................... 28 REFERENCE ........................................................................................................ 29 Research 2 APPLYING SIMULATION OPTIMIZATION WITH MULTIPHASE EVOLUTIONARY STRATEGIES TO THE ASSET ALLOCATION OF A PROPERTYCASUALTY INSURER ... 32 1. INTRODUCTION ............................................................................................. 32 2. STOCHASTIC INVESTMENT AND INSURANCE MARKET MODELS .......... 35 2.1 Investment Markets ........................................................................................... 35 2.2 Insurance Markets ............................................................................................. 37 3. THE DYNAMICS OF THE INSURERS OPERATIONS ................................ 37 3.1 Insurance Activities ........................................................................................... 38 3.2 Investment Activities ......................................................................................... 39 4. THE OPTIMIZATION OF THE INSURERS ASSET ALLOCATION ............ 43 4.1 objective Function ............................................................................................. 43 4.2 Investment Strategies ......................................................................................... 44 5. MULTIPHASE EVOLUTION STRATEGIES (MPES) .................................... 46 5.1 Basic Evolution Strategies .................................................................................. 46 5.2 MultiPhase Evolution Strategies ...................................................................... 49 5.3 Effectiveness and Robustness of the MultiPhase Evolution Strategies ............... 50 6. SIMULATION RESULTS ................................................................................... 54 6.1 Objective Function Analysis ................................................................................ 55 6.2 ASSETS ALLOCATIONS ACROSS RUIN PROBABILITIES .......................... 58 7. SUMMARIES AND CONCLUSION .................................................................... 61 REFERENCES ........................................................................................................ 64 Appendix 1 .............................................................................................................. 67 Appendix 2 .............................................................................................................. 69 List of Figures and Tables Research 1 COUPLING A MULTIPHASE GENETIC ALGORITHM WITH A SIMULATION MODEL TO SEARCH FOR THE OPTIMAL MULTIPERIOD ASSET ALLOCATIONS OF A PROPERTYCASUALTY INSURER Figure 1: Three dimensional sketch of f1 when n = 2. .................................................. 21 Table 1: The benchmark functions used to test the performance of optimization algorithms ...... 18 Table 2: Optimization results of MPGA for the five benchmark functions ..................... 22 Table 3: Results of the three investment strategies ( 1 k =0.165, 2 k =2.50E+10, and p=1%) ..... 25 Table 4: Results of changing k1 while keeping 2.50 10 2 k = E + and p = 1% ................ 26 Table 5: Results of changing p while keeping 1 k = 0.165 and 2 k = 2.50E+10 ............... 27 Research 2 APPLYING SIMULATION OPTIMIZATION WITH MULTIPHASE EVOLUTIONARY STRATEGIES TO THE ASSET ALLOCATION OF A PROPERTYCASUALTY INSURER Figure 1: The simulated activities of the insurer ............................................................. 38 Figure 2: Three dimensional sketch of f3. ..................................................................... 52 Figure 3: Three dimensional sketch of f4 ...................................................................... 52 Figure 4: Three dimensional sketch of f5. ..................................................................... 53 Figure 5: Averages of the fivetime asset allocations across tolerable ruin probabilities.. 61 Table 1: Notations used in describing investment activities ............................................ 40 Table 2: High dimension benchmark functions .............................................................. 51 Table 3: Computational results of MPES for the five benchmark functions ..................... 54 Table 4: Comparisons of optimized objective value using different strategies and methods ....... 57 Table 51: Asset allocations across ruin probabilities under MPES reallocation (ruin probabilities ranges from 0.005 to 0.03) ........................................................................................... 59 Table 52: Asset allocations across ruin probabilities under MPES Reallocation (ruin probabilities ranges from 0.04 to 0.1) ............................................................................................... 60 | zh_TW |
dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0923565081 | en_US |
dc.subject (關鍵詞) | 模擬最佳化 | zh_TW |
dc.subject (關鍵詞) | 財產保險 | zh_TW |
dc.subject (關鍵詞) | 資產配置 | zh_TW |
dc.subject (關鍵詞) | simulation optimization | en_US |
dc.subject (關鍵詞) | property-casualty insurance | en_US |
dc.subject (關鍵詞) | asset allocation | en_US |
dc.title (題名) | 應用模擬最佳化來求解產險公司之資產配置的兩篇論文 | zh_TW |
dc.type (資料類型) | thesis | en |
dc.relation.reference (參考文獻) | 1. Back, T.H., 1996. Evolutionary Algorithms in Theory and Practice. Oxford University Press, | zh_TW |
dc.relation.reference (參考文獻) | New York. | zh_TW |
dc.relation.reference (參考文獻) | 2. Björk, T., 1998. Arbitrage Theory in Continuous Time, Oxford University Press, New York, | zh_TW |
dc.relation.reference (參考文獻) | 52‐60. | zh_TW |
dc.relation.reference (參考文獻) | 3. Brennan, M. J., Schwartz, E. S., and Lagnado, R., 1997. Strategic asset allocation. Journal | zh_TW |
dc.relation.reference (參考文獻) | of Economic Dynamics and Control 21, 1377‐1403. | zh_TW |
dc.relation.reference (參考文獻) | 4. Campbell, J. Y., 2000. Asset pricing at the millennium. Journal of Finance 55, 1515‐1567. | zh_TW |
dc.relation.reference (參考文獻) | 5. Chiu, M. C. and Li, D., 2006. Asset and liability management under a continuous‐time | zh_TW |
dc.relation.reference (參考文獻) | mean–variance optimization framework. Insurance: Mathematics and Economics 39, | zh_TW |
dc.relation.reference (參考文獻) | 330‐355. | zh_TW |
dc.relation.reference (參考文獻) | 6. Cox, J. C. and Huang, C. F., 1989. Optimal consumption and portfolio policies when asset | zh_TW |
dc.relation.reference (參考文獻) | prices follow a diffusion process. Journal of Economic Theory 49, 33‐83. | zh_TW |
dc.relation.reference (參考文獻) | 7. Cox, J. C., Ingersoll, J. E., and Ross, S. A., 1985. A theory of the term structure of interest | zh_TW |
dc.relation.reference (參考文獻) | rates. Econometrica 53, 385‐407. | zh_TW |
dc.relation.reference (參考文獻) | 8. Craft, T. M., 2005. Impact of pension plan liabilities on real estate investment. Journal of | zh_TW |
dc.relation.reference (參考文獻) | Portfolio Management, 23‐28. | zh_TW |
dc.relation.reference (參考文獻) | 9. Garai, G. and Chaudhuri, B.B., 2003. A hierarchical genetic algorithm with search space | zh_TW |
dc.relation.reference (參考文獻) | partitioning scheme. International Conference on Integration of Knowledge Intensive | zh_TW |
dc.relation.reference (參考文獻) | 65 | zh_TW |
dc.relation.reference (參考文獻) | Multi‐Agent Systems, 139‐144. | zh_TW |
dc.relation.reference (參考文獻) | 10. Markowitz, H. M., 1952. Portfolio selection. Journal of Finance 7, 77‐91. | zh_TW |
dc.relation.reference (參考文獻) | 11. Merton, R. C., 1971. Optimal consumption and portfolio rules in a continuous‐time | zh_TW |
dc.relation.reference (參考文獻) | model. Journal of Economic Theory 3, 373‐413. | zh_TW |
dc.relation.reference (參考文獻) | 12. Merton, R. C., 1990. Continuous Time Finance. Basil Blackwell, Cambridge, Chapters | zh_TW |
dc.relation.reference (參考文獻) | 4‐6. | zh_TW |
dc.relation.reference (參考文獻) | 13. Nissen, V. and Biethahn, J., 1995. An introduction to evolutionary algorithms in | zh_TW |
dc.relation.reference (參考文獻) | management applications. In: J. Biethahn, V. Nissen (Eds.), Evolutionary Algorithms in | zh_TW |
dc.relation.reference (參考文獻) | Management Applications, Springer, Berlin, 3‐43. | zh_TW |
dc.relation.reference (參考文獻) | 14. Rechenberg, I., 1973. Evolution strategie: Optimierung technischer systeme nach | zh_TW |
dc.relation.reference (參考文獻) | prinzipien der biologischen evolution. Frommann‐Holzboog, Stuttgart. | zh_TW |
dc.relation.reference (參考文獻) | 15. Sharpe, W. F., and Tint, L. G., 1990. Liabilities‐a new approach. Journal of Portfolio | zh_TW |
dc.relation.reference (參考文獻) | Management 16, 5‐10. | zh_TW |
dc.relation.reference (參考文獻) | 16. Schwefel, H.‐P., 1981. Numerical Optimization for Computer Models. John Wiley, | zh_TW |
dc.relation.reference (參考文獻) | Chichester. | zh_TW |
dc.relation.reference (參考文獻) | 17. Tekin, E. and Sabuncuoglu, I., 2004. Simulation optimization: A comprehensive review | zh_TW |
dc.relation.reference (參考文獻) | on theory and applications. IIE Transactions 36, 1067‐1081. | zh_TW |
dc.relation.reference (參考文獻) | 18. Vesterstrom, J. and Thomsen, R., 2004. A comparative study of differential evolution, | zh_TW |
dc.relation.reference (參考文獻) | particle warm optimization, and evolutionary algorithms on numerical benchmark | zh_TW |
dc.relation.reference (參考文獻) | 66 | zh_TW |
dc.relation.reference (參考文獻) | problems. Congress on Evolutionary Computation, 19‐23. | zh_TW |
dc.relation.reference (參考文獻) | 19. Yao, X. and Liu, Y., 1996. Fast evolutionary programming. Proceedings of the Fifth | zh_TW |
dc.relation.reference (參考文獻) | Annual Conference on Evolutionary Programming, 451‐460. | zh_TW |
dc.relation.reference (參考文獻) | 20. Zbigniew, M., 1996, Genetic Algorithms + Data Structures = Evolution Programs. Springer, | zh_TW |
dc.relation.reference (參考文獻) | New York. | zh_TW |