(3.236.231.61) 您好!臺灣時間:2021/05/11 21:48
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:李家瑜
研究生(外文):LEE, CHIA-YU
論文名稱:GARCH選擇權定價模型之比較
論文名稱(外文):A Comparison on GARCH Option Pricing Models
指導教授:黃士峰黃士峰引用關係
指導教授(外文):HUANG, SHIH-FENG
口試委員:王功亮林良靖
口試委員(外文):WANG, David K.LIN, LIANG-CHING
口試日期:2017-06-12
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:統計學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:51
中文關鍵詞:GARCH模型蒙地卡羅馬可夫鏈法選擇權定價
外文關鍵詞:GARCH modelMarkov chain Monte Carlooption pricing
相關次數:
  • 被引用被引用:0
  • 點閱點閱:131
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本文針對Heston and Nandi (2000)所提出的GARCH選擇權定價模型、Christoffersen et al. (2008)所提出的Component GARCH模型、Cheng et al. (2016)所提出的Kernel GARCH模型,調查其在選擇權定價上的表現。其中Component GARCH模型是在Heston and Nandi (2000)的模型結構下,加入長期與短期波動元素所形成的模型。Kernel GARCH模型則是在Component GARCH模型上,進一步加入衡量波動溢酬的因子。本文推導出Component GARCH模型與Kernel GARCH模型需要滿足的參數限制式,以確保模型所生成的條件變異數恆正,並提出以蒙地卡羅馬可夫鏈法對模型參數進行估計。模擬研究發現所提出的參數估計方法具有良好的估計效果,亦在許多不同的參數結構下,比較三個模型的配適效果與調查選擇權價格對參數的敏感度。實證研究採用 2013/1/1 到 2015/10/15 的S&P500指數資料,比較三個模型對不同到期日與不同履約價之歐式買權價格的估計表現。
This study investigates the option pricing performance of Heston and Nandi (2000)’s GARCH model, Christoffersen et al. (2008)’s Component GARCH model and Cheng et al. (2016)’s Kernel GARCH model. The Component GARCH model extends Heston and Nandi (2000)’s GARCH model by including the short-run and long-run volatility components. The Kernel GARCH model generalizes the Component GARCH model by further considering the effect of volatility premium. We derive a proper set of constraints for the parameters in the Component GARCH model and the Kernel GARCH model to ensure the positivity of the conditional variances. The model parameters are estimated by a Markov chain Monte Carlo (MCMC) algorithm. Simulation results indicate that the proposed MCMC method has satisfactory estimation performance. We also conduct several simulation scenarios to investigate the sensitivity of the model parameters for the three GARCH models. The data of the S&P500 index from January 2, 1996, to December 30, 2016 are employed to compare the pricing performance for European call options with different maturities and different strike prices of the three GARCH models.
Abstract (Chinese) ii
Abstract (English) iii
1 Introduction 1
2 Literature Review 2
2.1 Heston and Nandi (2000) . . . . . . . . . . . . . . . . . . . . . 2
2.2 Christo ersen et al. (2008) . .. . . . . . . . . . . . . . . . . . 3
2.3 Cheng et al. (2016) . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Huang and Ciou (2017) . . . . . . . . . . . . . . . . . . . . . 4
3 Estimation procedure 5
3.1 Parameter constraints of the Component GARCH and Kernel GARCH models . 5
3.2 MCMC procedures of the three models. . . . . . . . . . . 7
4 Comparison Studies 9
4.1 Sensitivity analysis of the model parameters on the dynamics of volatilities 10
4.2 Sensitivity analysis of the model parameters on option pricing . . . . 11
5 Empirical Studies 12
6 Conclusion 14
Appendix 15
References 42
Andersen, T. G., Bollerslev, T., Diebold, F. X. and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71, 579-625.
Bera, A. K. and Higgins, M. L. (1993). ARCH models: Properties, estimation and testing. J. Economic Surveys, 7, 305-366.
Black, F. (1976). Stuedies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economic Statistics Section. American Statistical Association, 177-181.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. J. Econometrics, 31, 307-327.
Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. J. Econometrics, 52, 5-59.
Bollerslev, T., Engle, R. F. and Nelson, D. B. (1994). ARCH models. Handbook of Econometrics, 4, 2959-3038.
Chang, H. L., Chang, Y. C., Cheng, H. W. and Tseng, K. (2017). Jump variance risk: Evidence from option valuation and stock returns. Available at SSRN: https://ssrn.com/abstract=2934077 or http://dx.doi.org/10.2139/ssrn.2934077.
Chen, C. W., Lee, J. C., Lee, H. Y. and Niu, W. F. (2004). Bayesian estimation for time-series regressions improved with exact likelihoods. Journal of Statistical Computation and Simulation, 74, 727-740.
Cheng, H. W., Tsai, J. T. and Lei, Y. D. (2016). Variance premium, U-shape pricing kernel and option valuation. Manuscript.
Chernov, M. and Ghysels, E. (2000). A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation. J. Financial Economics, 56, 407-458.
Christoffersen, P., Heston, S. and Jacobs, K. (2006). Option valuation with conditional skewness. J. Econometrics, 131, 253-284.
Christoffersen, P., Heston, S. and Jacobs, K. (2013). Capturing option anomalies with a variance-dependent pricing kernel. Review of Financial Studies, 26, 1963-2006.
Christoffersen, P., Jacobs, K., Ornthanalai, C. and Wang, Y. (2008). Option valuation with long-run and short-run volatility components. J. Financial Economics, 90, 272-297.
Comte, F., Coutin, L. and Renault, E. (2012). Affine fractional stochastic volatility models. Annals of Finance, 8, 337-378.
Duan, J. C. (1995). The GARCH option pricing model. Mathematical Finance, 5, 13-32.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Engle, R. F. and Lee, G. (1999). A long-run and short-run component model of stock return volatility. Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive WJ Granger, 475-497.
Engle, R. F. and Mustafa, C. (1992). Implied ARCH models from options prices. J. Econometrics, 52, 289-311.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. The Journal of Portfolio Management}, 31, 92-100.
Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48, 1779-1801.
Goldfarb, E., Morriss, J., Chang, J. Y. and Facteau, W. M. (2017). Methods and devices for facilitating visualization in a surgical environment. U.S. Patent No. 9,603,506. Washington, DC: U.S. Patent and Trademark Office.
Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6, 327-343.
Heston, S. L. and Nandi, S. (2000). A closed-form GARCH option valuation model. Review of Financial Studies, 13, 585-625.
Huang, S. F. and Guo, M. H. (2014). Model risk of the implied GARCH-normal model. Quantitative Finance, 14, 2215-2224.
Huang, S. F. and Tsai, C. Y. (2015). Hedging barrier options in GARCH models with transaction costs. Australian & New Zealand Journal of Statistics, 57, 301-324.
Huang, S. F. and Ciou, G. C. (2017). Multi-asset empirical martingale price estimators for financial derivatives. Statistica Sinica, forthcoming.
Tsay, R. S. (2010). Analysis of Financial Time Series. Wiley, New Jersey.

電子全文 電子全文(網際網路公開日期:20220627)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔