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題名 台指選擇權之波動率-以馬可夫轉換模型分析
Regime-switched Volatility of TAIEX Options Using Markov-switching variance model作者 陳宛頤
Chen, Wan Yi貢獻者 謝淑貞
Shieh, Shwu Jane
陳宛頤
Chen, Wan Yi關鍵詞 馬可夫移轉
波動率
台指選擇權
時間價值
Markov-switching variance
volatility
TAIEX Options
time value日期 2015 上傳時間 3-Aug-2015 13:16:28 (UTC+8) 摘要 本篇論文使用馬可夫移轉變異數模型探討台指選擇權之買權的波動性。馬可夫移轉變異數模型將條件變異設定為可隨時間變動而改變，甚至移轉到不同區間上。樣本在不同區間下的平滑機率估計值有助於捕捉資料特性，實證結果顯示當樣本落在高波動率區間上時，會對應著重大事件的發生，例如2004年台灣319槍擊案、2006年全球股災、2008年金融海嘯等。當樣本落在低波動率區間上時，會對應著投資人傾向將台股指數的上漲或下跌視為超漲或超跌，而賦予台指選擇權之買權負的時間價值。
This paper investigates the volatility of TAIEX Call Options using Markov-switching variance model. The Markov-switching variance model allows the conditional disturbances to change as time passes and even switch between different regimes. The estimation of smoothed probabilities under different regimes facilitates to capture the characteristics of data. The empirical result shows that the high volatility regime is related to extraordinary events, such as 319 shooting incident in 2004, the global stock market crash in 2006, and the Financial Crisis in 2008. When in low volatility regime, investors tend to treat rise or fall in TAIEX as overreactions and give TAIEX Call Options turning points of time values.參考文獻 I. Chinese References王祝三，莊益源，張鐘霖(2003)，「波動率模型預測能力的比較-以臺指選擇權為例」，臺灣金融財務季刊，4(2)，41-63。吳仰哲，廖四郎，林士貴(2009)，「Lévy與GARCH- Lévy過程之選擇權評價與實證分析：臺灣加權股價指數選擇權為例」，管理與系統，17(1)，49-74。徐正憲(2014)，「馬可夫轉換模型在黃金現貨、石油價格之實證研究」，政大統計系碩士論文。張志向(2006)，「台指選擇權推出對領先落後關係的影響：內含價值與權利類型」，亞太經濟管理評論，10(1)，1-26。郭玟秀，陳仁龍，邱永金(2010)，「台指選擇權隱含波動率指標對真實波動率與指數報酬的資訊內涵之研究」，創新與管理，7(2)，127-146。粘瑞益(1999/2006?)，建構臺灣股市之隱含波動度避險模型—以馬可夫轉換模型為例，第六屆證券暨期貨金椽獎，市場組佳作。郭維裕，陳鴻隆，陳威光(2013)，「選擇權市場效率性檢定；隱含波動率成對交易檢定法」，管理與系統，23(3)，425-458。詹錦宏，施介人 (2005)，「台股指數現貨、期貨與選擇權價格發現之研究」，臺灣金融財務季刊，6(1)，31-51。Ⅱ. English ReferencesBackus, D., Chernov, M., Martin, I. (2011), “Disasters Implied by Equity Index Options,” The Journal of Finance, 66(6), 1969-2012.Black, F., Scholes, M. (1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, 81, 637-659.Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, 307-327.Cai, J. (1994), “A Markov Model of Switching-Regime ARCH,” Journal of Business and Economic Statistics, 12(3), 309-316.Cont, R., Deguest, R. (2013), “Equity Correlations Implied by Index Options: Estimation and Model Uncertainty Analysis,” Mathematical Finance, 23(3), 496-530.Chan, K.C., Cheng, L.T.W., Lung, P.P. (2005), “Asymmetric Volatility and Trading Activity in Index Futures Options,” The Financial Review, 40, 381-407.Chen, J., Zou, W. (2013), “A Markov regime-switching model for crude-oil markets: Comparison of composite likelihood and full likelihood,” The Canadian Journal of Statistics, 41(2), 353-367.Daouk, H., Guo, J.Q. (2004), “Switching Asymmetric GARCH and Options on a Volatility Index,” The Journal of Futures Markets, 24(3), 251-282.Diebold, F.X. (1986), “Modelling the persistence of conditional variance: A comment,” Econometric Reviews, 5(1), 51-56.Dueker, M., Neely, C. (2007), “Can Markov switching models predict excess foreign exchange returns?” Journal of Banking and Finance, 31, 279–296.Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50(4), 987-1008.Figlewski, S. (1997), “Forecasting Volatility,” Financial Markets, Institutions & Instruments, 6(1).Hamilton, J.D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57(2), 357-384.Hamilton, J.D., Susmel, R. (1994), “Autoregressive conditional heteroskedasticity and changes in regime,” Journal of Econometrics, 64, 307-333.Huang, Y.L., Kuan, C.M., Lin, K.S. (1998), “Identifying the turning points and business cycles and forecasting real GNP growth rate in Taiwan,” Taiwan Economic Review, 26, 431–457.Kim, C.J., Nelson, C.R., Startz, R. (1998), “Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization,” Journal of Empirical Finances, 5, 131-154.Kim, C.J., Nelson, C.R. (1999), “State-space models with regime switching : classical and Gibbs-sampling approaches with applications,” 1st edition, 59-93, England, The MIT Press.Lamourex, C.G., Lastrapes, W.D. (1990), “Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,” The Journal of Finance, 45(1), 221-229.Lamourex, C.G., Lastrapes, W.D. (1993), “Forecasting Stock-Return Variance: Toward an understanding of Stochastic Implied Volatilities,” The Review of Financial Studies, 6(2), 293-326.Mayhew, S., Stivers, C. (2003), “Stock Return Dynamics, Option Volume, and the Information Content of Implied Volatility,” The Journal of Futures Markets, 23(7), 615-646.Poon, S.H., C.W.J. Granger (2003), “Forecasting Volatility in Financial Market,” Journal of Economic Literature, 41, 475-539.Ramchond, L., Susmel, R. (1998), “ Volatility and Cross Correlation Across Major Stock Markets,” Journal of Empirical Finance, 5(4), 397-416.Smith, D.R. (2002), “Markov-switching and stochastic volatility diffusion models of short-term interest rates,” Journal of Business and Economic Statistics, 20, 183–197.Su et al. (2006), “Pricing and Hedging performance of Taiwan Stock Index Options under two-state volatility condition,” Proceedings of the 11th annual conference of Asia Pacific Decision Sciences Institute, June 14-18, 707-711.Turner, C.M., Startz, R., Nelson, C.R. (1989), “A Markov Model of Hetereskedasticity, Risk, and Learning in the Stock Market,” Journal of Financial Economics, 25, 3-22.Wang, J. (2007), “Research on the Markov switching model,” Quantitative and Technical Economics, 3, 39–48. 描述 碩士
國立政治大學
國際經營與貿易研究所
102351030資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102351030 資料類型 thesis dc.contributor.advisor 謝淑貞 zh_TW dc.contributor.advisor Shieh, Shwu Jane en_US dc.contributor.author (Authors) 陳宛頤 zh_TW dc.contributor.author (Authors) Chen, Wan Yi en_US dc.creator (作者) 陳宛頤 zh_TW dc.creator (作者) Chen, Wan Yi en_US dc.date (日期) 2015 en_US dc.date.accessioned 3-Aug-2015 13:16:28 (UTC+8) - dc.date.available 3-Aug-2015 13:16:28 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2015 13:16:28 (UTC+8) - dc.identifier (Other Identifiers) G0102351030 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/77149 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易研究所 zh_TW dc.description (描述) 102351030 zh_TW dc.description.abstract (摘要) 本篇論文使用馬可夫移轉變異數模型探討台指選擇權之買權的波動性。馬可夫移轉變異數模型將條件變異設定為可隨時間變動而改變，甚至移轉到不同區間上。樣本在不同區間下的平滑機率估計值有助於捕捉資料特性，實證結果顯示當樣本落在高波動率區間上時，會對應著重大事件的發生，例如2004年台灣319槍擊案、2006年全球股災、2008年金融海嘯等。當樣本落在低波動率區間上時，會對應著投資人傾向將台股指數的上漲或下跌視為超漲或超跌，而賦予台指選擇權之買權負的時間價值。 zh_TW dc.description.abstract (摘要) This paper investigates the volatility of TAIEX Call Options using Markov-switching variance model. The Markov-switching variance model allows the conditional disturbances to change as time passes and even switch between different regimes. The estimation of smoothed probabilities under different regimes facilitates to capture the characteristics of data. The empirical result shows that the high volatility regime is related to extraordinary events, such as 319 shooting incident in 2004, the global stock market crash in 2006, and the Financial Crisis in 2008. When in low volatility regime, investors tend to treat rise or fall in TAIEX as overreactions and give TAIEX Call Options turning points of time values. en_US dc.description.tableofcontents Abstract……………………………………………………………………iContents…………………………………………………………………iiiList of Figures…………………………………………………ivList of Tables……………………………………………………iv1. Introduction……………………………………………………12. Literature Review………………………………………43. Methodology………………………………………………………9 3-1 Pricing of Call Options 3-2 Markov-switching variance model 3-2-1 Markov chain and Markov-switching variance model 3-2-2 Filtered Probability and Smoothed Probability4. Data………………………………………………………………………17 4-1 Data Introduction 4-2 Statistics of TAIEX 4-3 Statistics Characteristics of Premium for TAIEX Call Options 4-3-1 Q-Q plot of Premium for TAIEX Call Options 4-3-2 Unit Root Test of Premium for TAIEX Call Options 4-4 Statistics Characteristics of Time Value for TAIEX Call Options 4-4-1 Q-Q plot of Time Value for TAIEX Call Options 4-4-2 Unit Root Test of Time Value for TAIEX Call Options5. Empirical Results………………………………………295-1 The Characteristics of Time Value Data5-2 The estimation model and probability under different regimes of Premium for TAIEX Call Options5-3 The estimation model and probability under different regimes of Time Value for TAIEX Call Options 5-4 Comparison of Premium and Time Value for TAIEX Call Options 5-5 Analysis 5-6 Goodness of fit6. Conclusion…………………………………………………………507. References…………………………………………………………52 zh_TW dc.format.extent 1304752 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102351030 en_US dc.subject (關鍵詞) 馬可夫移轉 zh_TW dc.subject (關鍵詞) 波動率 zh_TW dc.subject (關鍵詞) 台指選擇權 zh_TW dc.subject (關鍵詞) 時間價值 zh_TW dc.subject (關鍵詞) Markov-switching variance en_US dc.subject (關鍵詞) volatility en_US dc.subject (關鍵詞) TAIEX Options en_US dc.subject (關鍵詞) time value en_US dc.title (題名) 台指選擇權之波動率-以馬可夫轉換模型分析 zh_TW dc.title (題名) Regime-switched Volatility of TAIEX Options Using Markov-switching variance model en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) I. Chinese References王祝三，莊益源，張鐘霖(2003)，「波動率模型預測能力的比較-以臺指選擇權為例」，臺灣金融財務季刊，4(2)，41-63。吳仰哲，廖四郎，林士貴(2009)，「Lévy與GARCH- Lévy過程之選擇權評價與實證分析：臺灣加權股價指數選擇權為例」，管理與系統，17(1)，49-74。徐正憲(2014)，「馬可夫轉換模型在黃金現貨、石油價格之實證研究」，政大統計系碩士論文。張志向(2006)，「台指選擇權推出對領先落後關係的影響：內含價值與權利類型」，亞太經濟管理評論，10(1)，1-26。郭玟秀，陳仁龍，邱永金(2010)，「台指選擇權隱含波動率指標對真實波動率與指數報酬的資訊內涵之研究」，創新與管理，7(2)，127-146。粘瑞益(1999/2006?)，建構臺灣股市之隱含波動度避險模型—以馬可夫轉換模型為例，第六屆證券暨期貨金椽獎，市場組佳作。郭維裕，陳鴻隆，陳威光(2013)，「選擇權市場效率性檢定；隱含波動率成對交易檢定法」，管理與系統，23(3)，425-458。詹錦宏，施介人 (2005)，「台股指數現貨、期貨與選擇權價格發現之研究」，臺灣金融財務季刊，6(1)，31-51。Ⅱ. English ReferencesBackus, D., Chernov, M., Martin, I. (2011), “Disasters Implied by Equity Index Options,” The Journal of Finance, 66(6), 1969-2012.Black, F., Scholes, M. (1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, 81, 637-659.Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, 307-327.Cai, J. (1994), “A Markov Model of Switching-Regime ARCH,” Journal of Business and Economic Statistics, 12(3), 309-316.Cont, R., Deguest, R. (2013), “Equity Correlations Implied by Index Options: Estimation and Model Uncertainty Analysis,” Mathematical Finance, 23(3), 496-530.Chan, K.C., Cheng, L.T.W., Lung, P.P. (2005), “Asymmetric Volatility and Trading Activity in Index Futures Options,” The Financial Review, 40, 381-407.Chen, J., Zou, W. (2013), “A Markov regime-switching model for crude-oil markets: Comparison of composite likelihood and full likelihood,” The Canadian Journal of Statistics, 41(2), 353-367.Daouk, H., Guo, J.Q. (2004), “Switching Asymmetric GARCH and Options on a Volatility Index,” The Journal of Futures Markets, 24(3), 251-282.Diebold, F.X. (1986), “Modelling the persistence of conditional variance: A comment,” Econometric Reviews, 5(1), 51-56.Dueker, M., Neely, C. (2007), “Can Markov switching models predict excess foreign exchange returns?” Journal of Banking and Finance, 31, 279–296.Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50(4), 987-1008.Figlewski, S. (1997), “Forecasting Volatility,” Financial Markets, Institutions & Instruments, 6(1).Hamilton, J.D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57(2), 357-384.Hamilton, J.D., Susmel, R. (1994), “Autoregressive conditional heteroskedasticity and changes in regime,” Journal of Econometrics, 64, 307-333.Huang, Y.L., Kuan, C.M., Lin, K.S. (1998), “Identifying the turning points and business cycles and forecasting real GNP growth rate in Taiwan,” Taiwan Economic Review, 26, 431–457.Kim, C.J., Nelson, C.R., Startz, R. (1998), “Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization,” Journal of Empirical Finances, 5, 131-154.Kim, C.J., Nelson, C.R. (1999), “State-space models with regime switching : classical and Gibbs-sampling approaches with applications,” 1st edition, 59-93, England, The MIT Press.Lamourex, C.G., Lastrapes, W.D. (1990), “Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,” The Journal of Finance, 45(1), 221-229.Lamourex, C.G., Lastrapes, W.D. (1993), “Forecasting Stock-Return Variance: Toward an understanding of Stochastic Implied Volatilities,” The Review of Financial Studies, 6(2), 293-326.Mayhew, S., Stivers, C. (2003), “Stock Return Dynamics, Option Volume, and the Information Content of Implied Volatility,” The Journal of Futures Markets, 23(7), 615-646.Poon, S.H., C.W.J. Granger (2003), “Forecasting Volatility in Financial Market,” Journal of Economic Literature, 41, 475-539.Ramchond, L., Susmel, R. (1998), “ Volatility and Cross Correlation Across Major Stock Markets,” Journal of Empirical Finance, 5(4), 397-416.Smith, D.R. (2002), “Markov-switching and stochastic volatility diffusion models of short-term interest rates,” Journal of Business and Economic Statistics, 20, 183–197.Su et al. (2006), “Pricing and Hedging performance of Taiwan Stock Index Options under two-state volatility condition,” Proceedings of the 11th annual conference of Asia Pacific Decision Sciences Institute, June 14-18, 707-711.Turner, C.M., Startz, R., Nelson, C.R. (1989), “A Markov Model of Hetereskedasticity, Risk, and Learning in the Stock Market,” Journal of Financial Economics, 25, 3-22.Wang, J. (2007), “Research on the Markov switching model,” Quantitative and Technical Economics, 3, 39–48. zh_TW
