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論文名稱 Title |
比較GARCH(1,1)模型與Black-Scholes模型的選擇權避險部位
Comparison of Hedging Option Positions of the GARCH(1,1) and the Black-Scholes Models |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
45 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2003-06-06 |
繳交日期 Date of Submission |
2003-06-30 |
關鍵字 Keywords |
隱含波幅、選擇權定價、Black-Scholes模型、Delta 避險、蒙的卡羅模擬法、GARCH(1-1)模型 Delta hedging, Black-Scholes model, Option pricing, Monte Carlo simulation, Implied volatility, GARCH(1-1) model |
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統計 Statistics |
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中文摘要 |
這篇論文是分別探討Black-Scholes模型與GARCH(1,1)模型的選擇權避險部位,當標的物的對數報酬率呈現GARCH(1,1)的過程. 結果顯示Black-Scholes模型與GARCH模型的其中一個避險參數,delta值,在接近價平時是相似的;在深入價外時,Black-Scholes模型的選擇權delta值會大於GARCH模型;在深入價內時,Black-Scholes模型的選擇權delta值會小於GARCH模型. 我們也會呈現GARCH(1,1)與Black-Sholes模型的模擬避險過程,其結果也支持我們的發現. |
Abstract |
This article examines the hedging positions derived from the Black-Scholes(B-S) model and the GARCH(1,1) models, respectively, when the log returns of underlying asset exhibits GARCH(1,1) process. The result shows that Black-Scholes and GARCH options deltas, one of the hedging parameters, are similar for near-the-money options, and Black-Scholes options delta is higher then GARCH delta in absolute terms when the options are deep out-of-money, and Black-Scholes options delta is lower then GARCH delta in absolute terms when the options are deep in-the-money. Simulation study of hedging procedure of GARCH(1,1) and B-S models are performed, which also support the above findings. |
目次 Table of Contents |
1 Introduction..................1 2 Preliminaries.................4 3 Literature Review............11 4 The GARCH(1,1) Model.........15 5 Main Result..................21 6 Simulation Study.............29 7 Conclusion...................36 References.....................37 Figure 1~3.....................39 Table 1........................40 Table 2........................41 Table 3........................42 Table 4........................43 Table 5........................44 Table 6........................45 |
參考文獻 References |
1. Black, Fischer, and Myron scholes [1973], ”The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, 81, 637-654. 2. Bollerslev, Tim [1986], ”Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, 307-327. 3. Boyle, P.P. [1977], ”Options: A Monte-Carlo Approach”, Journal of Financial Economics, 4, 323-338. 4. Chang, Bin [2002], ”Evaluating the Black-Scholes Model and the GARCH Option Pricing Model”, Department of Economics, Queen’s University, Canada. 5. Duan, Jin-Chuan [1995], ”The GARCH Option Pricing Model”, Mathematical Finance, 5, 13-32. 6. Engle, Robert F. [1982], ”Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50, 987-1007. 7. Engle, Robert F., Joshua Rosenberg [1994], ”Hedging Options in a GARCH Environment: Testing the Term Structure of Stochastic Volatility Models”, NBER Working Paper Series no. 4958. 8. Engle, Robert F., Joshua Rosenberg [1995], ”GARCH Gamma”, Journal of Derivatives, Summer 1995, 47-59. 9. Hagerud, Gustaf E. [1996], ”Discrete Time Hedging of OTC Options in a GARCH Environment: A Simulation Experiment”, Working Paper Series in Economics and Finance No. 165. 10. Huang, Liu-Yuen [2002], ”Fitting Financial Time Series Data to Heavy Tailed Distribution”, Department of Applied Mathematics, National Sun Yat-Sen University, Taiwan. 11. Hull and White [1987], ”The Pricing of Options on Assets with Stochastic Volatilities”, Journal of Finance, 42, 281-300. 12. Hull, J.C. [1997], Options, Futures, & Other Derivatives, 4th ed. Prentice-Hall International, Inc. 13. Michael Sabbatini, Oliver Linton [1998], ”A GARCH Model of the Implied Volatility of the Swiss Market Index from Option Prices”, International Journal of Forecasting, 14, 199-213. 14. Ritchken, Peter [1996], Derivative Markets: Theory, Strategy, and Applications, HarperCollins College Publishers, Inc. 15. Tsay, Ruey S. [2002], Analysis of Financial Time Series, John Wiley & Sons, Inc. |
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