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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
作者 黃孝慈
貢獻者 陳春龍<br>蔡政憲
黃孝慈
關鍵詞 模擬最佳化
財產保險
資產配置
simulation optimization
property-casualty insurance
asset allocation
日期 2008
上傳時間 14-Sep-2009 09:19:12 (UTC+8)
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。
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.
參考文獻 1. Back, T.H., 1996. Evolutionary Algorithms in Theory and Practice. Oxford University Press,
New York.
2. Björk, T., 1998. Arbitrage Theory in Continuous Time, Oxford University Press, New York,
52‐60.
3. Brennan, M. J., Schwartz, E. S., and Lagnado, R., 1997. Strategic asset allocation. Journal
of Economic Dynamics and Control 21, 1377‐1403.
4. Campbell, J. Y., 2000. Asset pricing at the millennium. Journal of Finance 55, 1515‐1567.
5. Chiu, M. C. and Li, D., 2006. Asset and liability management under a continuous‐time
mean–variance optimization framework. Insurance: Mathematics and Economics 39,
330‐355.
6. Cox, J. C. and Huang, C. F., 1989. Optimal consumption and portfolio policies when asset
prices follow a diffusion process. Journal of Economic Theory 49, 33‐83.
7. Cox, J. C., Ingersoll, J. E., and Ross, S. A., 1985. A theory of the term structure of interest
rates. Econometrica 53, 385‐407.
8. Craft, T. M., 2005. Impact of pension plan liabilities on real estate investment. Journal of
Portfolio Management, 23‐28.
9. Garai, G. and Chaudhuri, B.B., 2003. A hierarchical genetic algorithm with search space
partitioning scheme. International Conference on Integration of Knowledge Intensive
65
Multi‐Agent Systems, 139‐144.
10. Markowitz, H. M., 1952. Portfolio selection. Journal of Finance 7, 77‐91.
11. Merton, R. C., 1971. Optimal consumption and portfolio rules in a continuous‐time
model. Journal of Economic Theory 3, 373‐413.
12. Merton, R. C., 1990. Continuous Time Finance. Basil Blackwell, Cambridge, Chapters
4‐6.
13. Nissen, V. and Biethahn, J., 1995. An introduction to evolutionary algorithms in
management applications. In: J. Biethahn, V. Nissen (Eds.), Evolutionary Algorithms in
Management Applications, Springer, Berlin, 3‐43.
14. Rechenberg, I., 1973. Evolution strategie: Optimierung technischer systeme nach
prinzipien der biologischen evolution. Frommann‐Holzboog, Stuttgart.
15. Sharpe, W. F., and Tint, L. G., 1990. Liabilities‐a new approach. Journal of Portfolio
Management 16, 5‐10.
16. Schwefel, H.‐P., 1981. Numerical Optimization for Computer Models. John Wiley,
Chichester.
17. Tekin, E. and Sabuncuoglu, I., 2004. Simulation optimization: A comprehensive review
on theory and applications. IIE Transactions 36, 1067‐1081.
18. Vesterstrom, J. and Thomsen, R., 2004. A comparative study of differential evolution,
particle warm optimization, and evolutionary algorithms on numerical benchmark
66
problems. Congress on Evolutionary Computation, 19‐23.
19. Yao, X. and Liu, Y., 1996. Fast evolutionary programming. Proceedings of the Fifth
Annual Conference on Evolutionary Programming, 451‐460.
20. Zbigniew, M., 1996, Genetic Algorithms + Data Structures = Evolution Programs. Springer,
New York.
描述 博士
國立政治大學
資訊管理研究所
92356508
97
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0923565081
資料類型 thesis
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) 2008en_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) G0923565081en_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) 92356508zh_TW
dc.description (描述) 97zh_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 MULTIPHASE GENETIC ALGORITHM WITH A SIMULATION MODEL TO SEARCH
     FOR THE OPTIMAL MULTIPERIOD ASSET ALLOCATIONS OF A PROPERTYCASUALTY 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 MULTIPHASE EVOLUTIONARY STRATEGIES
     TO THE ASSET ALLOCATION OF A PROPERTYCASUALTY 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 INSURERS OPERATIONS ................................ 37
     3.1 Insurance Activities ........................................................................................... 38
     3.2 Investment Activities ......................................................................................... 39
     4. THE OPTIMIZATION OF THE INSURERS ASSET ALLOCATION ............ 43
     4.1 objective Function ............................................................................................. 43
     4.2 Investment Strategies ......................................................................................... 44
     5. MULTIPHASE EVOLUTION STRATEGIES (MPES) .................................... 46
     5.1 Basic Evolution Strategies .................................................................................. 46
     5.2 MultiPhase Evolution Strategies ...................................................................... 49
     5.3 Effectiveness and Robustness of the MultiPhase 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 MULTIPHASE GENETIC ALGORITHM WITH A SIMULATION MODEL TO
     SEARCH FOR THE OPTIMAL MULTIPERIOD ASSET ALLOCATIONS OF A PROPERTYCASUALTY 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 MULTIPHASE EVOLUTIONARY STRATEGIES
     TO THE ASSET ALLOCATION OF A PROPERTYCASUALTY 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 fivetime 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 51: Asset allocations across ruin probabilities under MPES reallocation (ruin probabilities
     ranges from 0.005 to 0.03) ........................................................................................... 59
     Table 52: 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/#G0923565081en_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 optimizationen_US
dc.subject (關鍵詞) property-casualty insuranceen_US
dc.subject (關鍵詞) asset allocationen_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) thesisen
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. Journalzh_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‐timezh_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 assetzh_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 interestzh_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 ofzh_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 spacezh_TW
dc.relation.reference (參考文獻) partitioning scheme. International Conference on Integration of Knowledge Intensivezh_TW
dc.relation.reference (參考文獻) 65zh_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‐timezh_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, Chapterszh_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 inzh_TW
dc.relation.reference (參考文獻) management applications. In: J. Biethahn, V. Nissen (Eds.), Evolutionary Algorithms inzh_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 nachzh_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 Portfoliozh_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 reviewzh_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 benchmarkzh_TW
dc.relation.reference (參考文獻) 66zh_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 Fifthzh_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