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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/95179
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/95179


    Title: 以展望理論修正GCMG模型:集中交易市場的配適度分析
    Authors: 徐心傳
    Contributors: 李桐豪
    徐心傳
    Keywords: 複雜性
    展望理論
    ABM
    GCMG
    Date: 2009
    Issue Date: 2016-05-09 15:19:00 (UTC+8)
    Abstract: 由於經濟體系中的”複雜性”使得標準財務理論的研究受到了侷限,有鑑於傳統財務理論對於厚尾現象等”stylized facts”並無法提出有效的解釋,近年來由物理學學者所發展出的”物理經濟學派”開始嘗試使用agent-based model(ABM)的電腦模擬技術來替代以常態隨機漫步理論作為資產變動路徑的假設;許多的實證研究都顯示:高頻率的集中交易市場之資產價格波動具有”scaling behavior”的現象,同時報酬率尾端的機率分配較常態分配所描述的更為極端;因此Mantegna認為報酬率應服從TLF分配。而由Neil Johnson所提出的grand canonical minority game(GCMG)可以在簡單的模型架構下有效地模擬出金融市場的特質,因而可以解釋標準財務理論之不足。
    本論文為了檢視GCMG模型使否可以有效解釋台灣股票市場的特質,採用Mantegna演算法估計Levy分配之參數α,並比較其與集中交市場的真實價格分配是否一致,以此來檢測模型是否可以用來解釋台灣股票交易市場之性質。為了真實捕捉經濟個體之決策行為模式,本研究修改了GCMG中對於策略評價採線性的方式,取而代之的是採取諾貝爾獎得主Kanehman所提出的展望理論架構來捕捉真實投資人對於正負報酬會具有不同風險傾向的心理特質。由於MG模型具有”phase transiton”之特質,本論文對GCMG模型進行測試後發現市場波動度與記憶之間呈現嚴格負相關,其原因來自於GCMG對於投資人信心水準之假設。市場配適度分析則顯示GCMG在m=10時最為接近實際市場之α值,但仍有顯著的差異,顯示出模擬股價路徑較接近常態分配。而以展望理論修改評價函數後,雖然可以有效地使模擬股價路徑更為穩定,但仍無法改善GCMG模型的模擬股價路徑較實際市場變動更接近常態分配的缺點。很顯然地,模型架構過於簡化是造成此現象的可能原因;特別是台灣股票市場的制度性因素:散戶市場 以及漲跌幅限制等,都是造成模型配適度不高之原因。
    Reference: 中文參考文獻
    1. 王碩濱(2006),「以經濟物理學觀點分析台灣股市日內時間序列」,國立東華大學應用物理研究所碩士論文
    2. 田瀅嫆(2006),「厚尾分配下風險值與ETL探討--穩定分配與一般化誤差分配的應用」,銘傳大學財務金融系碩士論文
    3. 任青松(2002),「台灣股價指數與期貨指數之價量關聯性研究」,國立高雄第一科技大學財務管理系碩士論文
    4. 苟成玲 李英姿 劉玉萍(2006),「金融市場研究的新視角」,中國科技論文在線
    5. 保羅∙奧莫羅德(2000),「蝴蝶效應經濟學」,聯經
    6. 保羅∙奧莫羅德(2008),「敗部經濟學—99%失敗與1%成功的道理」,早安財經文化
    7. 馬克∙布侃南(2003),「連結—混沌、複雜之後,最具開創性的小世界理論」,天下文化
    7. 陳焙焿(2007),「台灣股價指數期貨報酬率與成交量關係之研究」,南華大學財務管理研究所碩士論文
    9. 菲利浦∙鮑爾(2008),「用物理學找到美麗新世界—洞悉事務如何環環相扣」,木馬文化
    10. 華德羅普(1994),「複雜—走在秩序與混沌邊緣」,天下文化

    英文參考文獻
    1. Arthur W.B., “Complexity and the Economy”, Science, 2 April 1999, 284, 107-109
    2. Bonabeau Eric, “Agent-based modeling: Methods and techniques for simulating human systems”, Proceedings of the National Academy of Sciences , May 14, 2002 vol. 99 no. Suppl 3 7280-7287
    3. Challet D., “Inter-pattern speculation beyond Minority, Majority and $-games”, arXiv:physics/0502140v3
    4. Challet D. and Matteo Marsili, “Criticality and finite size effects in a simple realistic model of stock market” , Phys. Rev. E Volume 68 (2003),Issue 3
    5. Challet D. and Y.C. Zhang, “Emergence of Cooperation and Organization in an Evolutionary Game”, Physica A 246 (1997), pp. 407–418.
    6. Challet D., Marsili M. and Riccardo Zecchina, “Exact solution of a modified El Farol`s bar problem: Efficiency and the role of market impact” , arXiv:cond-mat/9908480v3.
    7. Challet D., and Yi-Cheng Zhang, “On the minority game: Analytical and numerical studies”, Physica A 256 (1998), pp. 514–532.
    8. Challet D., Marsili M. and Andrea De Martinoc, “Stylized facts in minority games with memory: a new challenge”, Physica A 338 (2004), pp. 143-150.
    9. Challet D. and M. Marsili, “Phase transition and symmetry breaking in the minority game”, Physical Review E 60 (1999), pp. R6271–R6274.
    10. DuMouchel, W.H. (1983), “Estimating the Stable Index α in Order to Measure Tail Thickness: A Critique ”, The Annals of Statistics 11, 1019-1031.
    11. Ferreira F.F., Oliveira V.M.D., Crepaldi A.F. and P.R.A. Campos, “Agent based model with heterogeneous fundamental prices”, Physica A 357 (2005), pp. 534–542.
    12. Ferreira F. F., Francisco G., Machado B. S. and Paulsamy Muruganandam, “Time series analysis for minority game simulations of financial markets”, Physica A 321 (2003), pp. 619-632.

    13. Fama E. F., “The Behavior of Stock-Market Prices”, Journal of Business, Vol. 38, 1965, pp. 34-105.
    14. Galla T., Mosetti G. and Yi-Cheng Zhang, “Anomalous fluctuations in Minority Games and related multi-agent models of financial markets”, arXiv:physics/0608091v1.
    15. Guptaa N., Hausera R. and Neil F. Johnson, “Using Artificial Market Models to Forecast Financial Time-Series”, arXiv:physics/0506134v2.
    16. Hart M.L., Johnson N.F., Lamper D., Jefferies P., and S. Howison, “Application of multi-agent games to the prediction of financial time-series”, arXiv:cond-mat/0105303 v1, Physica A 299 (2001), pp. 222–227.
    17. Hart M.L., D Lamper, and N.F. Johnson “ An investigation of crash avoidance in a complex system”, Physica A, Volume 316, Number 1, 15 December 2002 , pp. 649-661(13).
    18. Jefferies P. and Neil F. Johnson ,“Designing agent-based market models”, arXiv:cond-mat/0207523.
    19. M.L. Hart, Jefferies P., Hui P.M. and N.F. Johnson, “From market games to real-world markets”, Eur. Phys. J. B 20 (2001), pp. 493–501.
    20. Marsili M. , “Market mechanism and expectations in minority and majority games”, Physica A 299 (2001), pp. 93–103.
    21. Mantegna, R.N. (1994), “Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes”, Physical Review E 49: 4677-4683.
    22. Mantegna, R.N. and H.E. Stanley(1994), “Stochastic processes with ultraslow convergence to a Gaussian: The truncated Lévy flight”, Physical Review Letters 73: 2946-2949.
    23. Mantegna R.N. and Stanley, H.E. (1995). “Scaling behavior in the dynamics of an economic index”, Nature 376: 46-49.
    24. Nolan J. P., “Fitting Data and Assessing Goodness with Stable Distributions”, Engineering and Statistics, American University, Washington, DC, June 3-5, 1999.

    25. Yi-Cheng Zhang, “ Why Financial Markets Will Remain Marginally Inefficient”, arXiv:cond-mat/0105373v1.
    26. Yi-Cheng Zhang, “Toward a Theory of Marginally Efficient Markets”, Physica A 269 (1999), pp. 30-44.
    27. Johnson N.F., Jefferies P. and P.M. Hui, “Financial Market Complexity”, Oxford University Press, Oxford (2003).
    28. Mantegna R.N., and H. E. Stanley, “An Introduction to Econophysics: Correlations and Complexity in Finance”, Cambridge University Press, Cambridge, 2000
    Description: 碩士
    國立政治大學
    金融研究所
    96352015
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0096352015
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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