| dc.contributor.advisor | 陳威光<br>江彌修 | zh_TW |
| dc.contributor.advisor | <br> | en_US |
| dc.contributor.author (Authors) | 沈昱昌 | zh_TW |
| dc.creator (作者) | 沈昱昌 | zh_TW |
| dc.date (日期) | 2004 | en_US |
| dc.date.accessioned | 14-Sep-2009 09:33:12 (UTC+8) | - |
| dc.date.available | 14-Sep-2009 09:33:12 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Sep-2009 09:33:12 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0913520312 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/31218 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融研究所 | zh_TW |
| dc.description (描述) | 91352031 | zh_TW |
| dc.description (描述) | 93 | zh_TW |
| dc.description.abstract (摘要) | 本文主要探討基因演算法(genetic algorithms)與S&P500指數選擇權為研究對象，利用基因演算法的模型來估測選擇權的隱含波動度後，進而求出選擇權的最適價值，用此來比較過去文獻中利用Jump-Diffusion Model、Stochastic Volatility Model與Local Volatility Model來估算選擇權的隱含波動度，使原始BS model中隱含波動度之估測更趨完善。在此篇論文中，以基因演算法求估的選擇權波動度以0.052的平均誤差值優於以Jump-Diffusion Model、Stochastic Volatility Model與Local Volatility Model求出之平均誤差值0.308，因此基因演算法確實可應用於選擇權波動度之求估。 | zh_TW |
| dc.description.abstract (摘要) | In this paper a different approach to the BS Model is proposed, by using genetic algorithms a non-parametric procedure for capturing the volatility smile and assess the stability of it. Applying genetic algorithm to this important issue in option pricing illustrates the strengths of our approach. Volatility forecasting is an appropriate task in which to highlight the characteristics of genetic algorithms as it is an important problem with well-accepted benchmark solutions, the models mention in the previous literatures mentioned above. Genetic algorithms have the ability to detect patterns in the conditional mean on both time and stock depend volatility. In addition, the stability test of the genetic algorithm approach will also be accessed. We evaluate the stability of the new approach by examining how well it predicts future option prices. We estimate the volatility function based on the cross-section of reported option prices one week, and then we examine the price deviations from theoretical values one week later. | en_US |
| dc.description.tableofcontents | CONTENTS 1 1 INTRODUCTION 3 2 IMPLIED VOLATILITY MODELS 5 2.1 Jump-Diffusion Model 5 2.2 Stochastic Volatility Model 5 2.3 Local Volatility Model 6 3 GENETIC ALGORITHMS 8 3.1 Background of Genetic Algorithms 8 3.2 Genetic Algorithm Preparatory 9 3.3 Genetic Algorithm Procedures 12 4 THE METHODOLGY 15 4.1 Data Description 15 4.2 Genetic Algorithms: Terminal Set and Function Set 21 4.3 Genetic Algorithms: Initialization scheme 24 4.4 Genetic Algorithms: Selection Scheme 25 4.5 Genetic Algorithms: Fitness Function 26 4.6 Genetic Algorithms: Genetic Operations 26 4.7 Genetic Algorithms: Other Operations 28 5 EXPERIMENT RESULTS 29 5.1 Black and Scholes Implied Volatility Patterns 29 5.2 GA Derived Approximation 32 5.3 Prediction Results 32 6 CONCLUSION REMARKS 34 7 REFERENCES 35 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0913520312 | en_US |
| dc.subject (關鍵詞) | 基因演算法 | zh_TW |
| dc.subject (關鍵詞) | 隱含波動度 | zh_TW |
| dc.subject (關鍵詞) | Genetic Algorithm | en_US |
| dc.subject (關鍵詞) | Implied Volatility Function | en_US |
| dc.title (題名) | Implied Volatility Function - Genetic Algorithm Approach | zh_TW |
| dc.type (資料類型) | thesis | en |
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| dc.relation.reference (參考文獻) | 4. Bakshi G., Cao C., Chen Z. (1997). “Empirical Performance of Alternative Option Pricing Models,” Journal of Finance 52, 2003–2049. | zh_TW |
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| dc.relation.reference (參考文獻) | 16. Lagnado R., Osher S. (1997). “Reconciling Differences,” RISK Magazine April, 79–83. | zh_TW |
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