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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/118607
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/118607


    Title: 運用最小平方蒙地卡羅與類神經網路法評價可贖回永續債券
    Pricing the callable perpetual bonds with least squares Monte Carlo & artificial neural network method
    Authors: 蔡維豪
    Tsai, Wei-Hao
    Contributors: 林士貴
    莊明哲

    Lin, Shih-Kuei
    Chuang, Ming-Che

    蔡維豪
    Tsai, Wei-Hao
    Keywords: 可贖回永續債券
    最小平方蒙地卡羅模擬法
    類神經網路
    Hull & White模型
    Callable perpetual bonds
    Least squares Monte Carlo simulation approach
    Neural network
    Hull & White model
    Date: 2018
    Issue Date: 2018-07-12 13:42:05 (UTC+8)
    Abstract: 本研究以可贖回永續債券為評價目標,引用Hull & White (1990)模型刻劃短期利率之動態過程,首先運用Longstaff & Schwartz (2001)所提出之最小平方蒙地卡羅模擬法(Least Squares Monte Carlo Simulation Approach)進行評價,其計算方法簡單且直覺,且可有效地評價具有路徑相依特性之金融商品。再將原方法所使用之多元迴歸模型改以倒傳遞類神經網路模型替代,依據模型估計結果計算非線性關係下之繼續持有價值並進行後續評價,以提供另一種可贖回永續債券之評價方法。期望能透過本研究之成果,使投資人與發行機構對於可贖回永續債券之評價有一基礎之認知。
    This study takes callable perpetual bonds as evaluation target, using the Hull & White (1990) model to characterize the dynamic process of short-term interest rates. Firstly, using the Least Squares Monte Carlo simulation approach proposed by Longstaff & Schwartz (2001), it is simple and intuitive, and can effectively evaluate financial instruments with path-dependent characteristics. Then replace the multiple regression model used in the original method with the back-propagation neural network model, calculate the continuing holding value under the nonlinear relationship and carry out subsequent evaluation based on the model estimation results, to provide another evaluation method of callable perpetual bonds. It is expected that through the results of this research, investors and issuers will have a basic understanding of the evaluation of callable perpetual bonds.
    Reference: 中文部分:
    葉怡成,(2009)。類神經網路模式應用與實作。台北市:儒林出版社。
    陳松男,(2006)。利率金融工程學:理論模型與實務應用。台北市:新陸書局。
    陳松男,(2008)。金融工程學:金融商品創新選擇權理論。台北市:新陸書局。
    英文部分:
    Brigo,D., & Mercurio,F., (2007).Interest rate models-theory and practice:with smile, inflation and credit., New York:Springer Science & Business Media.
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    Black, F., Derman, E., & Toy, W. (1990).A one-factor model of interest rates and its application to treasury bond options., Financial Analysts Journal, 46(1), 33-39.
    Brace, A., Gatarek, D., & Msiela, M. (1997).The market model of interest rate dynamics., Mathematical Finance, 7(2), 127-155.
    Carr, P., Jarrow, R., & Myneni, R. (1992).Alternative characterizations of American put options., Mathematical Finance, 2(2), 87-106.
    Cox, J. C., Ingersoll Jr, J. E., & Ross, S. A. (1985).A theory of the term structure of interest rates., Econometrica, 53(2), 385-408.
    Cybenko, G. (1989).Approximation by superpositions of a sigmoidal function., Mathematics of Control, Signals and Systems, 2(4), 303-314.
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    Grant, D., Vora, G., & Weeks, D. (1996).Simulation and the early exercise option problem., Journal of Financial Engineering, 5(3), 211-227.
    Heath, D., Jarrow, R., & Morton, A. (1992).Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation. Econometrica, 60(1), 77-105.
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    Hull, J., & White, A. (1994).Numerical procedures for implementing term structure models I:single-factor models., Journal of Derivatives, 2(1), 7-16.
    Hull, J., & White, A. (1994).Numerical procedures for implementing term structure models II:two-factor models., Journal of Derivatives, 2(2), 37-48.
    Jamshidian, F. (1997).LIBOR and swap market models and measures., Finance and Stochastics, 1(4), 293-330.
    Longstaff, F. A., & Schwartz, E. S. (2001).Valuing American options by simulation: a simple least-squares approach., The Review of Financial Studies, 14(1), 113-147.
    Merton, R. C. (1973).Theory of rational option pricing., The Bell Journal of economics and Management Science, 4(1), 141-183.
    Raymar, S., & Zwecher, M. (1997).Monte Carlo estimation of American call options on the maximum of several stocks., Journal of Derivatives, 5(1), 7-23.
    Riedmiller, M., & Braun, H. (1993).A direct adaptive method for faster backpropagation learning:The RPROP algorithm., In Neural Networks, 1993.,IEEE International Conference on. IEEE., 586-591.
    Stentoft, L. (2004).Convergence of the least squares Monte Carlo approach to American option valuation., Management Science, 50(9), 1193-1203.
    Tilley, J. A., (1993).Valuing American options in a path simulation model., Transactions of the Society of Actuaries, 45, 499-520.
    Vasicek, O. (1977).An equilibrium characterization of the term structure., Journal of Financial Economics, 5(2), 177-188.
    Description: 碩士
    國立政治大學
    金融學系
    105352027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105352027
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MB.018.2018.F06
    Appears in Collections:[金融學系] 學位論文

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