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題名 Implied Volatility Function - Genetic Algorithm Approach
作者 沈昱昌
貢獻者 陳威光<br>江彌修
<br>
沈昱昌
關鍵詞 基因演算法
隱含波動度
Genetic Algorithm
Implied Volatility Function
日期 2004
上傳時間 14-九月-2009 09:33:12 (UTC+8)
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。
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.
參考文獻 1. Ait-Sahalia Y., Wang Y., Yared F. (1998). “Do Option Markets Correctly Asses the Probabilities of Movements of the Underlying Asset?” Forthcoming, Journal of Econometrics.
2. Andersen L., Brotherton-Ratcliffe R. (1998). “The Equity OptionVolatility Smile: AFinite Difference Approach,” Journal of Computational Finance 1, 2, 5–38.
3. Andersen T., Benzoni L., Lund J. (1999). “Estimating Jump-Diffusions for Equity Returns,” Working Paper, Northwestern University and Aarhus School of Business.
4. Bakshi G., Cao C., Chen Z. (1997). “Empirical Performance of Alternative Option Pricing Models,” Journal of Finance 52, 2003–2049.
5. Bates D. (1996). “Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options,” Review of Financial Studies 9, 1, 69–107.
6. Black F., Scholes M. (1973). “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy 81, 637–654
7. Das S., Foresi S. (1996). “Exact Solutions for Bond and Option Prices with Systematic Jump Risk,” Review of Derivatives Research 1, 7–24.
8. Dumas B., Fleming J., Whaley R.E. (1996). Implied Volatility Functions: Empirical Test, Working paper, National Bureau of Economic Research, Cambridge.
9. Dupire B. (1994). “Pricing with a Smile,” RISK Magazine January, 18–20.
10. Goldberg D., Deb K. (1991). “A comparative analysis of selection schemes used in genetic algorithms,” Foundations of Genetic Algorithms, San Francisco.
11. Goldberg D., Korb B., Deb K. (1989). “Messy genetic algorithms: Motivation, analysis, and first results,” Complex Systems 3, 5.
12. Heston S. (1993). “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options,” Review of Financial Studies 6, 2, 327–343.
13. Holland J. H. (1975). “Adaption in Natural and Artificial Systems,” The University of Michigan Press.
14. Hull J, White A. (1987). “The Pricing of Options with Stochastic Volatilities,” Journal of Finance 42, 281–300.
15. Koza, J.R. (1992). “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, Cambridge MA.
16. Lagnado R., Osher S. (1997). “Reconciling Differences,” RISK Magazine April, 79–83.
17. Merton R. (1976). “Option Pricing when Underlying Stock Returns are Discontinuous,” Journal of Financial Economics May, 125–144.
18. Rubinstein M. (1994). “Implied Binomial Trees,” Journal of Finance 49, 771–818.
19. Smith S. (1980). “A Learning System Based on Genetic Adaptive Algorithms,” Ph.D. dissertation. University of Pittsburgh.
20. Stein E, Stein J. (1991). “Stock Price Distributions with Stochastic Volatility: An Analytic Approach,” Review of Financial Studies 4, 4, 727–752.
21. Webster’s II. (1994). New Riverside University Dictionary, Houghton Mifflin Company.
描述 碩士
國立政治大學
金融研究所
91352031
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0913520312
資料類型 thesis
dc.contributor.advisor 陳威光<br>江彌修zh_TW
dc.contributor.advisor <br>en_US
dc.contributor.author (作者) 沈昱昌zh_TW
dc.creator (作者) 沈昱昌zh_TW
dc.date (日期) 2004en_US
dc.date.accessioned 14-九月-2009 09:33:12 (UTC+8)-
dc.date.available 14-九月-2009 09:33:12 (UTC+8)-
dc.date.issued (上傳時間) 14-九月-2009 09:33:12 (UTC+8)-
dc.identifier (其他 識別碼) G0913520312en_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) 91352031zh_TW
dc.description (描述) 93zh_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/#G0913520312en_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 Algorithmen_US
dc.subject (關鍵詞) Implied Volatility Functionen_US
dc.title (題名) Implied Volatility Function - Genetic Algorithm Approachzh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Ait-Sahalia Y., Wang Y., Yared F. (1998). “Do Option Markets Correctly Asses the Probabilities of Movements of the Underlying Asset?” Forthcoming, Journal of Econometrics.zh_TW
dc.relation.reference (參考文獻) 2. Andersen L., Brotherton-Ratcliffe R. (1998). “The Equity OptionVolatility Smile: AFinite Difference Approach,” Journal of Computational Finance 1, 2, 5–38.zh_TW
dc.relation.reference (參考文獻) 3. Andersen T., Benzoni L., Lund J. (1999). “Estimating Jump-Diffusions for Equity Returns,” Working Paper, Northwestern University and Aarhus School of Business.zh_TW
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
dc.relation.reference (參考文獻) 5. Bates D. (1996). “Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options,” Review of Financial Studies 9, 1, 69–107.zh_TW
dc.relation.reference (參考文獻) 6. Black F., Scholes M. (1973). “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy 81, 637–654zh_TW
dc.relation.reference (參考文獻) 7. Das S., Foresi S. (1996). “Exact Solutions for Bond and Option Prices with Systematic Jump Risk,” Review of Derivatives Research 1, 7–24.zh_TW
dc.relation.reference (參考文獻) 8. Dumas B., Fleming J., Whaley R.E. (1996). Implied Volatility Functions: Empirical Test, Working paper, National Bureau of Economic Research, Cambridge.zh_TW
dc.relation.reference (參考文獻) 9. Dupire B. (1994). “Pricing with a Smile,” RISK Magazine January, 18–20.zh_TW
dc.relation.reference (參考文獻) 10. Goldberg D., Deb K. (1991). “A comparative analysis of selection schemes used in genetic algorithms,” Foundations of Genetic Algorithms, San Francisco.zh_TW
dc.relation.reference (參考文獻) 11. Goldberg D., Korb B., Deb K. (1989). “Messy genetic algorithms: Motivation, analysis, and first results,” Complex Systems 3, 5.zh_TW
dc.relation.reference (參考文獻) 12. Heston S. (1993). “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options,” Review of Financial Studies 6, 2, 327–343.zh_TW
dc.relation.reference (參考文獻) 13. Holland J. H. (1975). “Adaption in Natural and Artificial Systems,” The University of Michigan Press.zh_TW
dc.relation.reference (參考文獻) 14. Hull J, White A. (1987). “The Pricing of Options with Stochastic Volatilities,” Journal of Finance 42, 281–300.zh_TW
dc.relation.reference (參考文獻) 15. Koza, J.R. (1992). “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, Cambridge MA.zh_TW
dc.relation.reference (參考文獻) 16. Lagnado R., Osher S. (1997). “Reconciling Differences,” RISK Magazine April, 79–83.zh_TW
dc.relation.reference (參考文獻) 17. Merton R. (1976). “Option Pricing when Underlying Stock Returns are Discontinuous,” Journal of Financial Economics May, 125–144.zh_TW
dc.relation.reference (參考文獻) 18. Rubinstein M. (1994). “Implied Binomial Trees,” Journal of Finance 49, 771–818.zh_TW
dc.relation.reference (參考文獻) 19. Smith S. (1980). “A Learning System Based on Genetic Adaptive Algorithms,” Ph.D. dissertation. University of Pittsburgh.zh_TW
dc.relation.reference (參考文獻) 20. Stein E, Stein J. (1991). “Stock Price Distributions with Stochastic Volatility: An Analytic Approach,” Review of Financial Studies 4, 4, 727–752.zh_TW
dc.relation.reference (參考文獻) 21. Webster’s II. (1994). New Riverside University Dictionary, Houghton Mifflin Company.zh_TW