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題名 隱含波動率指數的分析及預測 - Mixed Causal-Noncausal Model 的應用
Modeling and Predicting The CBOE Volatility Index - Application of Mixed Causal-Noncausal Model作者 王姸之 貢獻者 徐士勛
王姸之關鍵詞 非因果模型
混合模型
隱含波動率指數
可拆解性質
Noncausal
Mixed causal-noncausal model
VIX
Filter日期 2017 上傳時間 11-Jul-2017 12:06:48 (UTC+8) 摘要 本研究主要針對 Breidt et al.(1991) 等多位學者所建構的 Mixed causal-noncausal model,探討其假設與可拆解特性,並仔細討論相關資料模擬估計及預測的方法,最後將其實際應用於隱含波動率指數 (Volatility Index)的估計及預測上。根據本研究的實證結果,我們發現隱含波動率指數確實包含非因果的特性,並可進一步對其拆解及預測。另外 , 我們也以移動窗格的方式觀察係數估計結果的變化,發現 Mixed Causal-Noncausal Model 的確能夠捕捉到泡沫或危機正在生成的過程。
This paper first focuses on Mixed causal-noncausal model constructed by Breidt et al.(1991) and then conducts empirical research on the CBOE Volatility Index. The assumptions, simulation, estimation and prediction methods of Mixed causal-noncausal model are introduced in great detail. Our empirical results show that the CBOE Volatility Index really contains non-causal parts, such that we can filter this part from the index and then further predict it. Moreover, by employing the rolling window estimation scheme the resulting coefficients of Mixed causal-noncausal model really could detect a bubble or a crisis which is going to happen.參考文獻 王維安 (2010), “VIX 指數之 Levy 模型最適化估計與預測及 VIX 衍生性商品之定價” 國立高雄應用科技大學金融資訊研究所學位論文。佟劭文 (2014), “以 VIX 指數作為擇時指標-探討七大工業國股票市場” 義守大學財務金融學系學位論文。周聖淵 (2012), “恐慌指數交易策略在股市之實證研究” 暨南大學經營管理碩士在職專班學位論文。陳志杰 (2012), “台灣大型權值股股價報酬與 VIX 指數, 黃金報酬之關聯性分析” 臺北大學國際財務金融碩士在職專班學位論文。張永杰 (2015), “VIX 指數, S&P500 指數與黃金價格之關聯性研究” 臺北大學國際財務金融碩士在職專班學位論文。黃冠甄 (2016), “VIX 指數, 美元指數及石油期貨價格對黃豆期貨價格及對咖啡期貨價格之影響” 中原大學企業管理研究所學位論文。Ahoniemi, K. (2008). Modeling and forecasting the VIX index. Working paper.Andrews, B., Calder, M., and Davis, R. A. (2009). Maximum likelihood estimation for α -stable autoregressive processes. The Annals of Statis-tics, 37(4), 1946–1982.Andrews, B., and Davis, R. A. (2013). Model identification for infinite variance autoregressive processes. Journal of Econometrics, 172(2), 222–234.Berger, J. M., and Mandelbrot, B. (1963). A new model for error clustering in telephone circuits. IBM Journal of Research and Development, 7(3), 224–236.Breidt, F. J., Davis, R. A., Lh, K. S., and Rosenblatt, M. (1991). Maximum likelihood estimation for noncausal autoregressive processes. Journal of Multivariate Analysis, 36(2), 175–198.Breidt, F. J., and Davis, R. A. (1992). Time-reversibility, identifiability and independence of innovations for stationary time series. Journal of Time Series Analysis, 13(5), 377–390.Brockwell, P. J., and Davis, R. A. (1991). Time series: theory and methods.Springer. New York.Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427–431.Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34–105.Fernandes, M., Medeiros, M. C., and Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking and Finance, 40, 1–10.Gourieroux, C., and Zakoian, J. M. (2013). Explosive bubble modelling by noncausal process. Working paper.Gourieroux, C., and Jasiak, J. (2015). Filtering, prediction and simulation methods for noncausal processes. Journal of Time Series Analysis, 37, 405–430.Gourieroux, C., and Zakoian, J. M. (2017). Local explosion modelling by non-causal process. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3), 737–756.Hecq, A., Lieb, L., and Telg, S. (2015a). Forecasting inflation in Europe with Mixed Causal-Noncausal Models. Working paper. Hecq, A., Lieb, L., and Telg, S. (2015b). Identification of Mixed Causal-Noncausal Models: How fat should we go? Working paper.Hencic, A., and Gourieroux, C. (2015). Noncausal autoregressive model in application to Bitcoin/USD exchange rates. Econometrics of Risk, 583, 17–40.Lanne, M., Luoto, J., and Saikkonen, P. (2012). Optimal forecasting of noncausal autoregressive time series. International Journal of Fore-casting, 28(3), 623–631.Lanne, M. and Saikkonen, P. (2011). Noncausal autoregressions for economic time series. Journal of Time Series Econometrics, 3(3), Article 2.Said, S. E., and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. 描述 碩士
國立政治大學
經濟學系
104258028資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104258028 資料類型 thesis dc.contributor.advisor 徐士勛 zh_TW dc.contributor.author (Authors) 王姸之 zh_TW dc.creator (作者) 王姸之 zh_TW dc.date (日期) 2017 en_US dc.date.accessioned 11-Jul-2017 12:06:48 (UTC+8) - dc.date.available 11-Jul-2017 12:06:48 (UTC+8) - dc.date.issued (上傳時間) 11-Jul-2017 12:06:48 (UTC+8) - dc.identifier (Other Identifiers) G0104258028 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110853 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 104258028 zh_TW dc.description.abstract (摘要) 本研究主要針對 Breidt et al.(1991) 等多位學者所建構的 Mixed causal-noncausal model,探討其假設與可拆解特性,並仔細討論相關資料模擬估計及預測的方法,最後將其實際應用於隱含波動率指數 (Volatility Index)的估計及預測上。根據本研究的實證結果,我們發現隱含波動率指數確實包含非因果的特性,並可進一步對其拆解及預測。另外 , 我們也以移動窗格的方式觀察係數估計結果的變化,發現 Mixed Causal-Noncausal Model 的確能夠捕捉到泡沫或危機正在生成的過程。 zh_TW dc.description.abstract (摘要) This paper first focuses on Mixed causal-noncausal model constructed by Breidt et al.(1991) and then conducts empirical research on the CBOE Volatility Index. The assumptions, simulation, estimation and prediction methods of Mixed causal-noncausal model are introduced in great detail. Our empirical results show that the CBOE Volatility Index really contains non-causal parts, such that we can filter this part from the index and then further predict it. Moreover, by employing the rolling window estimation scheme the resulting coefficients of Mixed causal-noncausal model really could detect a bubble or a crisis which is going to happen. en_US dc.description.tableofcontents 1 緒論 12 文獻回顧 42.1 計量模型的相關文獻探討 42.2 隱含波動率指數的相關文獻探討 63 實證模型 83.1 Mixed causal-noncausal model 介紹 83.2 Mixed causal-noncausal model 的拆解 103.3 Mixed causal-noncausal model 的模擬 103.4 Mixed causal-noncausal model 的估計 133.5 Mixed causal-noncausal model 的預測 144 實證方法 174.1 資料說明 174.2 資料敘述統計 184.3 研究方法 195 實證結果 215.1 ADF單根檢定 215.2 選取期數p 215.3 選取最適模型 225.4 資料的拆解 265.5 資料的預測 285.6 不同資料期間的估計結果 306 結論 34 zh_TW dc.format.extent 2295052 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104258028 en_US dc.subject (關鍵詞) 非因果模型 zh_TW dc.subject (關鍵詞) 混合模型 zh_TW dc.subject (關鍵詞) 隱含波動率指數 zh_TW dc.subject (關鍵詞) 可拆解性質 zh_TW dc.subject (關鍵詞) Noncausal en_US dc.subject (關鍵詞) Mixed causal-noncausal model en_US dc.subject (關鍵詞) VIX en_US dc.subject (關鍵詞) Filter en_US dc.title (題名) 隱含波動率指數的分析及預測 - Mixed Causal-Noncausal Model 的應用 zh_TW dc.title (題名) Modeling and Predicting The CBOE Volatility Index - Application of Mixed Causal-Noncausal Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 王維安 (2010), “VIX 指數之 Levy 模型最適化估計與預測及 VIX 衍生性商品之定價” 國立高雄應用科技大學金融資訊研究所學位論文。佟劭文 (2014), “以 VIX 指數作為擇時指標-探討七大工業國股票市場” 義守大學財務金融學系學位論文。周聖淵 (2012), “恐慌指數交易策略在股市之實證研究” 暨南大學經營管理碩士在職專班學位論文。陳志杰 (2012), “台灣大型權值股股價報酬與 VIX 指數, 黃金報酬之關聯性分析” 臺北大學國際財務金融碩士在職專班學位論文。張永杰 (2015), “VIX 指數, S&P500 指數與黃金價格之關聯性研究” 臺北大學國際財務金融碩士在職專班學位論文。黃冠甄 (2016), “VIX 指數, 美元指數及石油期貨價格對黃豆期貨價格及對咖啡期貨價格之影響” 中原大學企業管理研究所學位論文。Ahoniemi, K. (2008). Modeling and forecasting the VIX index. Working paper.Andrews, B., Calder, M., and Davis, R. A. (2009). Maximum likelihood estimation for α -stable autoregressive processes. The Annals of Statis-tics, 37(4), 1946–1982.Andrews, B., and Davis, R. A. (2013). Model identification for infinite variance autoregressive processes. Journal of Econometrics, 172(2), 222–234.Berger, J. M., and Mandelbrot, B. (1963). A new model for error clustering in telephone circuits. IBM Journal of Research and Development, 7(3), 224–236.Breidt, F. J., Davis, R. A., Lh, K. S., and Rosenblatt, M. (1991). Maximum likelihood estimation for noncausal autoregressive processes. Journal of Multivariate Analysis, 36(2), 175–198.Breidt, F. J., and Davis, R. A. (1992). Time-reversibility, identifiability and independence of innovations for stationary time series. Journal of Time Series Analysis, 13(5), 377–390.Brockwell, P. J., and Davis, R. A. (1991). Time series: theory and methods.Springer. New York.Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427–431.Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34–105.Fernandes, M., Medeiros, M. C., and Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking and Finance, 40, 1–10.Gourieroux, C., and Zakoian, J. M. (2013). Explosive bubble modelling by noncausal process. Working paper.Gourieroux, C., and Jasiak, J. (2015). Filtering, prediction and simulation methods for noncausal processes. Journal of Time Series Analysis, 37, 405–430.Gourieroux, C., and Zakoian, J. M. (2017). Local explosion modelling by non-causal process. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3), 737–756.Hecq, A., Lieb, L., and Telg, S. (2015a). Forecasting inflation in Europe with Mixed Causal-Noncausal Models. Working paper. Hecq, A., Lieb, L., and Telg, S. (2015b). Identification of Mixed Causal-Noncausal Models: How fat should we go? Working paper.Hencic, A., and Gourieroux, C. (2015). Noncausal autoregressive model in application to Bitcoin/USD exchange rates. Econometrics of Risk, 583, 17–40.Lanne, M., Luoto, J., and Saikkonen, P. (2012). Optimal forecasting of noncausal autoregressive time series. International Journal of Fore-casting, 28(3), 623–631.Lanne, M. and Saikkonen, P. (2011). Noncausal autoregressions for economic time series. Journal of Time Series Econometrics, 3(3), Article 2.Said, S. E., and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. zh_TW
