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題名 選擇權價格對期貨報酬的資訊內涵
The information content of option prices on futures returns: Evidence from Taiwan Derivatives Market作者 施冠宇
Shih, Guan-Yu貢獻者 陳威光<br>羅秉政
Chen, Wei‑Kuang<br>Kendro Vincent
施冠宇
Shih, Guan-Yu關鍵詞 期貨報酬
具有方向性台灣VIX指標
選擇權策略
LASSO
Ridge
Futures Returns
Directional Taiwan VIX
Option Strategies
LASSO Regression
Ridge Regression日期 2024 上傳時間 1-Mar-2024 12:34:35 (UTC+8) 摘要 本研究採用了包括回歸分析、時間序列分析以及機器學習方法，深入探討了台灣衍生性商品市場中選擇權價格與期貨報酬之間的資訊內涵。首先就預測因子而言，研究結果顯示，具有方向性台灣VIX指標因子在預測能力上優於傳統的VIX恐慌指數和大多數選擇權策略因子。再來，雖然大部分的選擇權策略因子對於期貨報酬的預測能力不顯著，但是當它們與具方向性台灣VIX指標因子同時考量時，可以在多變量回歸模型中發揮綜效。 此外就多變量回歸模型而言，Ridge的整體預測能力表現最為出色，因為透過降低模型複雜度，使模型能夠適應各個主要的多頭市場。LASSO雖然在整體的預測能力表現略遜一籌，但是在特定的極端市場情況，如空頭市場中的表現相對較佳。最後，在2012至2023年的樣本期間可以發現，使用部分的選擇權策略因子來對台灣股價指數期貨建構均值-變異數最佳化的投資組合，可以獲得較高的夏普值。
This study employed various methods including regression analysis, time series analysis, and machine learning to deeply explore the information content of option prices on futures returns in the Taiwanese derivatives market. Firstly, regarding predictors, the results show that the Directional Taiwan VIX predictors has superior predictability compared to the traditional VIX and most option strategies predictors. Furthermore, although the majority of the option strategies predictors are not significantly predictive of futures returns, they demonstrate a synergistic effect in multivariate regression models when considered alongside the Directional Taiwan VIX predictors. In terms of multivariate regression models, Ridge regression demonstrates superior overall predictive capabilities, as it reduces model complexity, enabling the model to adapt to various major bull markets. Although LASSO regression slightly lags in overall predictive ability, it performs relatively better in certain extreme market conditions, such as bear markets. Finally, during the sample period from 2012 to 2023, it is observed that using certain option strategy predictors to construct a mean-variance optimized investment portfolio for Taiwan stock index futures can achieve a higher Sharpe ratio.參考文獻 林俊良 (Lin, C.-L., Lin, C.-L., & 廖四郎. (2023). 具有方向性台灣VIX指標之建構與實證 = Construction and Empirical Testing of Directional Taiwan Volatility Index / 林俊良撰. 林俊良. Aggarwal, N., & Gupta, M. (2013). Portfolio hedging through options: Covered call versus protective put. Journal of Management Research, 13(2), 118-126. Alexandridis, A. K., Apergis, I., Panopoulou, E., & Voukelatos, N. (2023). Futures return prediction: The role of information from the options market. Journal of Financial Markets, 64, 100801. Aljasimee, M., & Alhamzawi, R. (2023). The lasso and rlasso: A Comparative study. Utilitas Mathematica, 120, 113-129. Alpsten, G., & Samanci, S. (2018). Portfolio Protection Strategies: A study on the protective put and its extensions. Amaya, D., Christoffersen, P., Jacobs, K., & Vasquez, A. (2015). Does realized skewness predict the cross-section of equity returns?. Journal of Financial Economics, 118(1), 135-167. Atilgan, Y., Bali, T. G., & Demirtas, K. O. (2015). Implied volatility spreads and expected market returns. Journal of Business & Economic Statistics, 33(1), 87-101. Avanijaa, J. (2021). Prediction of house price using xgboost regression algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(2), 2151-2155. Bali, T. G., & Hovakimian, A. (2009). Volatility spreads and expected stock returns. Management Science, 55(11), 1797-1812. Bevilacqua, M., Morelli, D., & Tunaru, R. (2019). The determinants of the model-free positive and negative volatilities. Journal of International Money and Finance, 92, 1-24. Bevilacqua, M., Morelli, D., & Uzan, P. S. R. (2020). Asymmetric implied market volatility and terrorist attacks. International Review of Financial Analysis, 67, 101417. Bollerslev, T., Marrone, J., Xu, L., & Zhou, H. (2014). Stock return predictability and variance risk premia: Statistical inference and international evidence. Journal of Financial and Quantitative Analysis, 49(3), 633-661. Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018). Risk everywhere: Modeling and managing volatility. The Review of Financial Studies, 31(7), 2729-2773. Bondarenko, O. (2014). Why are put options so expensive?. The Quarterly Journal of Finance, 4(03), 1450015. Buss, A., Schönleber, L., & Vilkov, G. (2016). Option-implied correlations, factor models, and market risk. Buss, A., Schönleber, L., & Vilkov, G. (2019). Expected correlation and future market returns. Available at SSRN 3114063. Brooks, R., Chance, D., & Hemler, M. (2019). The “Superior Performance” of Covered Calls on the S&P 500: Rethinking an Anomaly. The Journal of Derivatives, 27(2), 50-61. Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average?. The Review of Financial Studies, 21(4), 1509-1531. Cao, C., Simin, T., & Xiao, H. (2020). Predicting the futures return with the implied volatility spread. Journal of Financial Markets, 51, 100531. Carr, P. (2017). Why is VIX a Fear Gauge? Risk and Decision Analysis 6(2), 179-185. CBOE, (2009). The CBOE Volatility Index- VIX, White Paper. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). Chordia, T., Lin, T. C., & Xiang, V. (2021). Risk-neutral skewness, informed trading, and the cross section of stock returns. Journal of Financial and Quantitative Analysis, 56(5), 1713-1737. Christoffersen, P., Jacobs, K., & Chang, B. Y. (2013). Forecasting with option-implied information. Handbook of economic forecasting, 2, 581-656. Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of econometrics, 138(1), 291-311. Colacito, R., Ghysels, E., Meng, J., & Siwasarit, W. (2016). Skewness in expected macro fundamentals and the predictability of equity returns: Evidence and theory. The Review of Financial Studies, 29(8), 2069-2109. De Giuli, M. E., Montagna, D., Naldi, F., Tanda, A., Generali, T., & Tanda, A. (2019). Enhance and Protect Portfolio Returns: A Dynamic Put Spread Optimization. International Journal of Economics and Finance, 11(12), 1-66. DeLisle, R. J., Diavatopoulos, D., Fodor, A., & Kassa, H. (2022). Variation in option implied volatility spread and future stock returns. The Quarterly Review of Economics and Finance, 83, 152-160. Dichtl, H., Drobetz, W., Neuhierl, A., & Wendt, V. S. (2021). Data snooping in futures return prediction. International Journal of Forecasting, 37(1), 72-94. Doane, D. P., & Seward, L. E. (2011). Measuring skewness: a forgotten statistic?. Journal of statistics education, 19(2). Dong, X., Li, Y., Rapach, D. E., & Zhou, G. (2022). Anomalies and the expected market return. The Journal of Finance, 77(1), 639-681. Doran, J. S., Fodor, A., & Jiang, D. (2013). Call-put implied volatility spreads and option returns. Review of Asset Pricing Studies, 3(2), 258-290. Figelman, I. (2008). Expected return and risk of covered call strategies. Journal of Portfolio Management, 34(4), 81. Figlewski, S., Chidambaran, N. K., & Kaplan, S. (1993). Evaluating the performance of the protective put strategy. Financial Analysts Journal, 46-69. Filippou, I., Rapach, D., Taylor, M. P., & Zhou, G. (2023). Out-of-sample exchange rate prediction: A machine learning perspective. Available at SSRN 3455713. Finta, M. A., & Ornelas, J. R. H. (2022). Commodity return predictability: Evidence from implied variance, skewness, and their risk premia☆☆. Journal of International Financial Markets, Institutions and Money, 79, 101569. Fu, X., Arisoy, Y. E., Shackleton, M. B., & Umutlu, M. (2016). Option-implied volatility measures and stock return predictability. The Journal of Derivatives, 24(1), 58-78. Fuertes, A. M., Liu, Z., & Tang, W. (2022). Risk‐neutral skewness and commodity futures pricing. Journal of Futures Markets, 42(4), 751-785. Gao, X., Wang, X., & Yan, Z. (2020). Attention: Implied volatility spreads and stock returns. Journal of Behavioral Finance, 21(4), 385-398. Graefe, A. (2015). Improving forecasts using equally weighted predictors. Journal of Business Research, 68(8), 1792-1799. Han, B., & Li, G. (2021). Information content of aggregate implied volatility spread. Management Science, 67(2), 1249-1269. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: applications to nonorthogonal problems. Technometrics, 69-82. 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Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129, 103827. Singh, A. K., Gewali, L. P., & Khatiwada, J. (2019). New measures of skewness of a probability distribution. Open Journal of Statistics, 9(5), 601-621. SP, S., E, G., Acharya, R., & Matha, R. (2022). Are Options Trading Strategies Really Effective for Hedging in the Indian Derivatives Market?. Cogent Economics & Finance, 10(1), 2111783. Taiwan Futures Exchange, (2007). Annual Report. Ungar, J., & Moran, M. T. (2009). The cash-secured putwrite strategy and performance of related benchmark indexes. The Journal of Alternative Investments, 11(4), 43-56. Vanden, J. M. (2008). Information quality and options. The Review of Financial Studies, 21(6), 2635-2676. Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of futures return prediction. The Review of Financial Studies, 21(4), 1455-1508. Whaley, R. E. (2002). Return and risk of CBOE buy write monthly index. The Journal of Derivatives, 10(2), 35-42. Yang, Z. G. (2011). Buy-write or put-write: an active index writing portfolio to strike it right. Available at SSRN 1827363. 描述 碩士
國立政治大學
金融學系
111352019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352019 資料類型 thesis dc.contributor.advisor 陳威光<br>羅秉政 zh_TW dc.contributor.advisor Chen, Wei‑Kuang<br>Kendro Vincent en_US dc.contributor.author (Authors) 施冠宇 zh_TW dc.contributor.author (Authors) Shih, Guan-Yu en_US dc.creator (作者) 施冠宇 zh_TW dc.creator (作者) Shih, Guan-Yu en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-Mar-2024 12:34:35 (UTC+8) - dc.date.available 1-Mar-2024 12:34:35 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2024 12:34:35 (UTC+8) - dc.identifier (Other Identifiers) G0111352019 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150122 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 111352019 zh_TW dc.description.abstract (摘要) 本研究採用了包括回歸分析、時間序列分析以及機器學習方法，深入探討了台灣衍生性商品市場中選擇權價格與期貨報酬之間的資訊內涵。首先就預測因子而言，研究結果顯示，具有方向性台灣VIX指標因子在預測能力上優於傳統的VIX恐慌指數和大多數選擇權策略因子。再來，雖然大部分的選擇權策略因子對於期貨報酬的預測能力不顯著，但是當它們與具方向性台灣VIX指標因子同時考量時，可以在多變量回歸模型中發揮綜效。 此外就多變量回歸模型而言，Ridge的整體預測能力表現最為出色，因為透過降低模型複雜度，使模型能夠適應各個主要的多頭市場。LASSO雖然在整體的預測能力表現略遜一籌，但是在特定的極端市場情況，如空頭市場中的表現相對較佳。最後，在2012至2023年的樣本期間可以發現，使用部分的選擇權策略因子來對台灣股價指數期貨建構均值-變異數最佳化的投資組合，可以獲得較高的夏普值。 zh_TW dc.description.abstract (摘要) This study employed various methods including regression analysis, time series analysis, and machine learning to deeply explore the information content of option prices on futures returns in the Taiwanese derivatives market. Firstly, regarding predictors, the results show that the Directional Taiwan VIX predictors has superior predictability compared to the traditional VIX and most option strategies predictors. Furthermore, although the majority of the option strategies predictors are not significantly predictive of futures returns, they demonstrate a synergistic effect in multivariate regression models when considered alongside the Directional Taiwan VIX predictors. In terms of multivariate regression models, Ridge regression demonstrates superior overall predictive capabilities, as it reduces model complexity, enabling the model to adapt to various major bull markets. Although LASSO regression slightly lags in overall predictive ability, it performs relatively better in certain extreme market conditions, such as bear markets. Finally, during the sample period from 2012 to 2023, it is observed that using certain option strategy predictors to construct a mean-variance optimized investment portfolio for Taiwan stock index futures can achieve a higher Sharpe ratio. en_US dc.description.tableofcontents 摘要 ⅰ Abstract ii Contents iii List of Tables iv List of Figures v Chapter 1 Introduction 1 Chapter 2 Literature review 3 Chapter 3 Methodology 15 3.1 Forecasting approaches 15 3.2 Forecast evaluation criteria 22 Chapter 4 Data and main variables 25 4.1 Skewness of historical futures returns 26 4.2 Option strategy predictors 26 4.3 VIX-related predictors 32 Chapter 5 Results 40 5.1 In-sample predictability 40 5.2 Out-sample predictability 42 5.3 Economic evaluation 51 Chapter 6 Conclusion 63 Reference 65 zh_TW dc.format.extent 1110794 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352019 en_US dc.subject (關鍵詞) 期貨報酬 zh_TW dc.subject (關鍵詞) 具有方向性台灣VIX指標 zh_TW dc.subject (關鍵詞) 選擇權策略 zh_TW dc.subject (關鍵詞) LASSO zh_TW dc.subject (關鍵詞) Ridge zh_TW dc.subject (關鍵詞) Futures Returns en_US dc.subject (關鍵詞) Directional Taiwan VIX en_US dc.subject (關鍵詞) Option Strategies en_US dc.subject (關鍵詞) LASSO Regression en_US dc.subject (關鍵詞) Ridge Regression en_US dc.title (題名) 選擇權價格對期貨報酬的資訊內涵 zh_TW dc.title (題名) The information content of option prices on futures returns: Evidence from Taiwan Derivatives Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 林俊良 (Lin, C.-L., Lin, C.-L., & 廖四郎. (2023). 具有方向性台灣VIX指標之建構與實證 = Construction and Empirical Testing of Directional Taiwan Volatility Index / 林俊良撰. 林俊良. Aggarwal, N., & Gupta, M. (2013). Portfolio hedging through options: Covered call versus protective put. Journal of Management Research, 13(2), 118-126. Alexandridis, A. K., Apergis, I., Panopoulou, E., & Voukelatos, N. (2023). Futures return prediction: The role of information from the options market. Journal of Financial Markets, 64, 100801. Aljasimee, M., & Alhamzawi, R. (2023). The lasso and rlasso: A Comparative study. Utilitas Mathematica, 120, 113-129. Alpsten, G., & Samanci, S. (2018). Portfolio Protection Strategies: A study on the protective put and its extensions. Amaya, D., Christoffersen, P., Jacobs, K., & Vasquez, A. (2015). Does realized skewness predict the cross-section of equity returns?. Journal of Financial Economics, 118(1), 135-167. Atilgan, Y., Bali, T. G., & Demirtas, K. O. (2015). Implied volatility spreads and expected market returns. Journal of Business & Economic Statistics, 33(1), 87-101. Avanijaa, J. (2021). Prediction of house price using xgboost regression algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(2), 2151-2155. Bali, T. G., & Hovakimian, A. (2009). Volatility spreads and expected stock returns. Management Science, 55(11), 1797-1812. Bevilacqua, M., Morelli, D., & Tunaru, R. (2019). The determinants of the model-free positive and negative volatilities. Journal of International Money and Finance, 92, 1-24. Bevilacqua, M., Morelli, D., & Uzan, P. S. R. (2020). Asymmetric implied market volatility and terrorist attacks. International Review of Financial Analysis, 67, 101417. Bollerslev, T., Marrone, J., Xu, L., & Zhou, H. (2014). Stock return predictability and variance risk premia: Statistical inference and international evidence. Journal of Financial and Quantitative Analysis, 49(3), 633-661. Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018). Risk everywhere: Modeling and managing volatility. The Review of Financial Studies, 31(7), 2729-2773. Bondarenko, O. (2014). Why are put options so expensive?. The Quarterly Journal of Finance, 4(03), 1450015. Buss, A., Schönleber, L., & Vilkov, G. (2016). Option-implied correlations, factor models, and market risk. Buss, A., Schönleber, L., & Vilkov, G. (2019). Expected correlation and future market returns. Available at SSRN 3114063. Brooks, R., Chance, D., & Hemler, M. (2019). The “Superior Performance” of Covered Calls on the S&P 500: Rethinking an Anomaly. The Journal of Derivatives, 27(2), 50-61. Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average?. The Review of Financial Studies, 21(4), 1509-1531. Cao, C., Simin, T., & Xiao, H. (2020). Predicting the futures return with the implied volatility spread. Journal of Financial Markets, 51, 100531. Carr, P. (2017). Why is VIX a Fear Gauge? Risk and Decision Analysis 6(2), 179-185. CBOE, (2009). The CBOE Volatility Index- VIX, White Paper. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). Chordia, T., Lin, T. C., & Xiang, V. (2021). Risk-neutral skewness, informed trading, and the cross section of stock returns. Journal of Financial and Quantitative Analysis, 56(5), 1713-1737. Christoffersen, P., Jacobs, K., & Chang, B. Y. (2013). Forecasting with option-implied information. Handbook of economic forecasting, 2, 581-656. Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of econometrics, 138(1), 291-311. Colacito, R., Ghysels, E., Meng, J., & Siwasarit, W. (2016). Skewness in expected macro fundamentals and the predictability of equity returns: Evidence and theory. 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