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題名 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作者 林俊良
Lin, Chun-Liang貢獻者 廖 四 郎
Liao, Szu-Lang
林俊良
Lin, Chun-Liang關鍵詞 波動指數
觀測者效應
市場共識
情緒指標
八卦(易經)
VIX
Observer effect
Market Consensus
Sentiment Index
Eight Trigrams (I Ching)日期 2023 上傳時間 2-六月-2023 11:46:30 (UTC+8) 摘要 本論文分為兩部分：第一部分是DTVIX之建構理論，第二部分是DTVIX之台灣市場實證。第一部分DTVIX建構理論是利用圖形分析發現，當其他條件不變，只有波動率變化時，買權和賣權線的移動可以間接推論選擇權波動率變化與期貨價格變化方向的關係。然後，根據《說卦傳》的內容，利用易經八卦的變化方向建立起期貨價格的變化方向。最後，本文成功地將選擇權波動的八種組合與易經八卦相結合，使選擇權市場的波動變化清晰地呈現在三元組變化中。第一部分DTVIX之建構理論後,依據此理論框架，我們進一步在第二部份利用台灣週選擇權市場的數據進行實證測試，並有四點具體的發現：第一，本文建構了一個方向性波動指數DTVIX，它與期貨價格變化方向密切相關。這使市場交易者能夠更好地了解選擇權波動變化與期貨價格變化之間的互動關係,並以八種顏色繪製K棒，使期貨價格變化能夠同時反映情緒和選擇權波動組合。第二，DTVIX不僅與選擇權波動組合密切相關，而且還成功地將市場情緒轉化為市場共識。這與易經八卦卦理完全一致，其中正卦顯示多空波動之間沒有衝突，而隅卦顯示多空波動之間存在衝突。同時使得市場交易者能夠通過市場共識進一步凝聚多重情緒。第三，台灣週選擇權市場正常交易時段和盤後交易時段的市場共識程度是不同的，類似於量子力學中的觀測者效應。因為在盤後交易時段沒有觀測交易者的虧損（即不需要維持保證金追繳），提供了更多的額外資訊。第四，通過多空波動樣本的配對學習，DTVIX預測能力大為提高，符合《說卦傳》第三章“天地定位”論點。主要原因是不僅能夠在訓練樣本中平衡多空期貨價，而且訓練模型中相對波動組合更強烈地平衡多空波動變化的影響。總結，在易經八卦原理的框架下，DTVIX不僅具有方向性，而且具有良好的預測能力。尤其是在將訓練數據依據多空波動配對學習後，發現市場交易者更願意在選擇權交易行為中表達他們的內心想法，尤其在盤後交易時段沒有觀測交易者的虧損（即不需要維持保證金追繳），且波動急劇增加時，它會立即反映在Conditional VIX的變化中。本文利用這些特徵來預測期貨收盤價方向和走勢型態，實證結果顯示了所提出的測量方法具有良好的預測能力正確率可高達66%。
This thesis consists of two parts: the first part is the construction theory of DTVIX, and the second part is the Taiwan market empirical testing of DTVIX.The construction theory of DTVIX is to use graphical analysis to discover that, when all other things being equal, the shift of calls and puts line can indirectly infer the relationship between options volatility changes and the direction of futures price changes as volatility changes. Then, based on the content of the “Shuo Gua Zhuan”, using the changing directions of the Eight Trigrams (I Ching) to establish the changing directions of futures prices. Therefore, this paper successfully combines the eight combinations of options volatility with the Eight Trigrams (I Ching) , making the changes in the options market clearly presented in trigram.After the construction theory of DTVIX in the first part, based on this theoretical framework of the Eight Trigrams (I Ching), we further use the data of Taiwan weekly options market in the second part to conduct empirical tests, and have four specific findings:Firstly, this paper constructs a directional volatility index, DTVIX, which is closely related to the direction changes of futures price. This supports market traders in better understanding the interaction between changes in options volatility and changes in futures prices. In addition, through drawing candlestick charts with eight colors, futures price changes can reflect the combination of sentiment and options volatility at the same time.Secondly, DTVIX is not only closely related to the combinations of options volatility, but also successfully transforms market sentiment into market consensus. This is completely consistent with the principles of the Eight Trigrams (I Ching), where the border trigrams show no conflict between long-short volatility and the corner trigrams show conflict between long-short volatility. This allows market traders to further crystallize multiple sentiments through market consensus.Thirdly, the market consensus of the regular trading session and the after-hours trading session of the Taiwan weekly options market is different, similar to the observer effect in quantum mechanics. It suggests that there is no observed trader’s loss (that is, no maintenance margin call is required) during the after-hours trading session, providing more additional information.Fourthly, the predictive power is greatly enhanced by learning through the pairing of long-short volatility samples. This inspiration comes from the third chapter of the “Shuo Gua Zhuan” which mentions “the positioning of heaven and earth”, allowing us not only to balance the long and short futures prices in the training samples, but also to balance the impact of volatility changes more strongly through the content of opposing volatility combinations in the training model.In summary, under the framework of the Eight Trigrams (I Ching) principles, DTVIX not only has directionality but also has good predictive power. Especially after pairing the training data with buyer and seller force, the traders are more willing to express their inner thoughts in the options trading behavior where there is no observed trader`s loss (that is, no maintenance margin call is required) during the after-hours trading session, and the volatility increases sharply, so it will be immediately reflected in the changes of Conditional VIX. This paper uses these features to predict the direction of the futures close price and movement pattern, and the empirical results have shown good predictive power of the proposed measurements, and the correct rate can be as high as 66%.參考文獻 Atilgan, Y., Bali, T.G., & Demirtas, K. O. (2015). Implied Volatility Spreads and Expected Market Returns. Journal of Business and Economic Statistics, 33, 1.87-101.Baynes, C.F. (1950). The I Ching or Book of Changes. New York: Pantheon Books / Bollingen Series XIX.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.Cao, C., Simin, T., & Xiao, H. (2019). Predicting the Equity Premium 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, J., Jiang, G.J., Yuan, C., & Zhu, D. (2021). Breaking VIX at Open: Evidence of Uncertainty Creation and Resolution. Journal of Banking & Finance, 124, 106060.Fama, E.F., & French, K.R. (1993). Common Risk Factors in the Returns Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.Griffin, J.M., & Shams, A. (2018). Manipulation in the VIX? Review of Financial Studies, Society for Financial Studies, 31(4), 1377-1417.Gao, X., Wang, X., & Yan, Z. (2020). Attention: Implied Volatility Spreads and Stock Returns. Journal of Behavioral Finance, 21(4), 385-398.Han, B. (2008). Investor Sentiment and Option Prices. The Review of Financial Studies, 21(1), 387-414.Han, B., & Li, G. (2021). Aggregate Implied Volatility Spread and Stock Market Returns. Management Science, 67(2), 1249-1269.Hancock, G.D. (2012). VIX and VIX Futures Pricing Algorithms: Cultivating Understanding. Modern Economy, 3, 284-294.Kownatzki, K. (2016). How Good is the VIX as a Predictor of Market Risk? Journal of Accounting and Finance, 16(6), 39-60.Osterrieder, J., Roschli, K., & Vetter, L. (2019). The VIX Volatility Index—A Very Thorough Look at It. Working Paper, Zurich University of Applied Sciences, Switzerland.Prasad, A., & Bakhshi, P. (2022). Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning. Journal of Risk and Financial Management, 15(12), 552.Rosillo, R., Giner, J., & Fuente, D. (2014). The Effectiveness of the Combined Use of VIX and Support Vector Machines on the Prediction of S&P 500. Neural Computing and Applications 25(2), 321–332.Saha, A., Malkiel, B.G., & Rinaudo, A. (2019). Has the VIX Index been Manipulated? Journal of Asset Management, 20, 1-14.Serur, J.A., Dapena, J.P., & Siri, J.R. (2021). Decomposing the VIX Index into Greed and Fear, CEMA Working Papers: Serie Documentos de Trabajo. 780, Universidad del CEMA.Taiwan Futures Exchange, (2007). Annual Report.Young, T. (1802). The Bakerian Lecture: On the Theory of Light and Colours. Philosophical Transactions of the Royal Society of London, 92, 12–48. 描述 博士
國立政治大學
金融學系
105352504資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105352504 資料類型 thesis dc.contributor.advisor 廖 四 郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (作者) 林俊良 zh_TW dc.contributor.author (作者) Lin, Chun-Liang en_US dc.creator (作者) 林俊良 zh_TW dc.creator (作者) Lin, Chun-Liang en_US dc.date (日期) 2023 en_US dc.date.accessioned 2-六月-2023 11:46:30 (UTC+8) - dc.date.available 2-六月-2023 11:46:30 (UTC+8) - dc.date.issued (上傳時間) 2-六月-2023 11:46:30 (UTC+8) - dc.identifier (其他 識別碼) G0105352504 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145087 - dc.description (描述) 博士 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) 105352504 zh_TW dc.description.abstract (摘要) 本論文分為兩部分：第一部分是DTVIX之建構理論，第二部分是DTVIX之台灣市場實證。第一部分DTVIX建構理論是利用圖形分析發現，當其他條件不變，只有波動率變化時，買權和賣權線的移動可以間接推論選擇權波動率變化與期貨價格變化方向的關係。然後，根據《說卦傳》的內容，利用易經八卦的變化方向建立起期貨價格的變化方向。最後，本文成功地將選擇權波動的八種組合與易經八卦相結合，使選擇權市場的波動變化清晰地呈現在三元組變化中。第一部分DTVIX之建構理論後,依據此理論框架，我們進一步在第二部份利用台灣週選擇權市場的數據進行實證測試，並有四點具體的發現：第一，本文建構了一個方向性波動指數DTVIX，它與期貨價格變化方向密切相關。這使市場交易者能夠更好地了解選擇權波動變化與期貨價格變化之間的互動關係,並以八種顏色繪製K棒，使期貨價格變化能夠同時反映情緒和選擇權波動組合。第二，DTVIX不僅與選擇權波動組合密切相關，而且還成功地將市場情緒轉化為市場共識。這與易經八卦卦理完全一致，其中正卦顯示多空波動之間沒有衝突，而隅卦顯示多空波動之間存在衝突。同時使得市場交易者能夠通過市場共識進一步凝聚多重情緒。第三，台灣週選擇權市場正常交易時段和盤後交易時段的市場共識程度是不同的，類似於量子力學中的觀測者效應。因為在盤後交易時段沒有觀測交易者的虧損（即不需要維持保證金追繳），提供了更多的額外資訊。第四，通過多空波動樣本的配對學習，DTVIX預測能力大為提高，符合《說卦傳》第三章“天地定位”論點。主要原因是不僅能夠在訓練樣本中平衡多空期貨價，而且訓練模型中相對波動組合更強烈地平衡多空波動變化的影響。總結，在易經八卦原理的框架下，DTVIX不僅具有方向性，而且具有良好的預測能力。尤其是在將訓練數據依據多空波動配對學習後，發現市場交易者更願意在選擇權交易行為中表達他們的內心想法，尤其在盤後交易時段沒有觀測交易者的虧損（即不需要維持保證金追繳），且波動急劇增加時，它會立即反映在Conditional VIX的變化中。本文利用這些特徵來預測期貨收盤價方向和走勢型態，實證結果顯示了所提出的測量方法具有良好的預測能力正確率可高達66%。 zh_TW dc.description.abstract (摘要) This thesis consists of two parts: the first part is the construction theory of DTVIX, and the second part is the Taiwan market empirical testing of DTVIX.The construction theory of DTVIX is to use graphical analysis to discover that, when all other things being equal, the shift of calls and puts line can indirectly infer the relationship between options volatility changes and the direction of futures price changes as volatility changes. Then, based on the content of the “Shuo Gua Zhuan”, using the changing directions of the Eight Trigrams (I Ching) to establish the changing directions of futures prices. Therefore, this paper successfully combines the eight combinations of options volatility with the Eight Trigrams (I Ching) , making the changes in the options market clearly presented in trigram.After the construction theory of DTVIX in the first part, based on this theoretical framework of the Eight Trigrams (I Ching), we further use the data of Taiwan weekly options market in the second part to conduct empirical tests, and have four specific findings:Firstly, this paper constructs a directional volatility index, DTVIX, which is closely related to the direction changes of futures price. This supports market traders in better understanding the interaction between changes in options volatility and changes in futures prices. In addition, through drawing candlestick charts with eight colors, futures price changes can reflect the combination of sentiment and options volatility at the same time.Secondly, DTVIX is not only closely related to the combinations of options volatility, but also successfully transforms market sentiment into market consensus. This is completely consistent with the principles of the Eight Trigrams (I Ching), where the border trigrams show no conflict between long-short volatility and the corner trigrams show conflict between long-short volatility. This allows market traders to further crystallize multiple sentiments through market consensus.Thirdly, the market consensus of the regular trading session and the after-hours trading session of the Taiwan weekly options market is different, similar to the observer effect in quantum mechanics. It suggests that there is no observed trader’s loss (that is, no maintenance margin call is required) during the after-hours trading session, providing more additional information.Fourthly, the predictive power is greatly enhanced by learning through the pairing of long-short volatility samples. This inspiration comes from the third chapter of the “Shuo Gua Zhuan” which mentions “the positioning of heaven and earth”, allowing us not only to balance the long and short futures prices in the training samples, but also to balance the impact of volatility changes more strongly through the content of opposing volatility combinations in the training model.In summary, under the framework of the Eight Trigrams (I Ching) principles, DTVIX not only has directionality but also has good predictive power. Especially after pairing the training data with buyer and seller force, the traders are more willing to express their inner thoughts in the options trading behavior where there is no observed trader`s loss (that is, no maintenance margin call is required) during the after-hours trading session, and the volatility increases sharply, so it will be immediately reflected in the changes of Conditional VIX. This paper uses these features to predict the direction of the futures close price and movement pattern, and the empirical results have shown good predictive power of the proposed measurements, and the correct rate can be as high as 66%. en_US dc.description.tableofcontents 摘要 ⅰAbstract ⅲContents ⅴList of Tables ⅶList of Figures ⅸChapter 1 Introduction 1Chapter 2 Literature review 32.1 Literature review on volatility 32.2 preliminary study on the directional relationship between volatility and futures price changes 6Chapter 3 The Construction Theory of DTVIX 113.1 Introduction of the Eight Trigrams (I Ching) 133.2 The relationship between the Eight Trigrams (I Ching) and the direction of futures price changes 153.3 Conditional VIX 233.4 The relationship between options volatility combination and eight trigrams (I Ching) 273.5 Market Consensus (MC) 323.6 Observer effect 34Chapter 4 The Taiwan Market Empirical Testing of DTVIX 374.1 Data 374.2 Introduction of machine learning algorithms 384.3 Variable selection analysis 414.4 DTVIX model for forecasting futures price changes 534.5 The relationship between DTVIX and futures price changes 574.6 DTVIX predictive power analysis 664.6.1 The predictive power analysis of different trading sessions 664.6.2 Predictive power analysis of different options volatility combinations 67Chapter 5 Conclusions and Future Research 725.1 Conclusions 725.2 Future Research 74Reference 76 zh_TW dc.format.extent 5024193 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105352504 en_US dc.subject (關鍵詞) 波動指數 zh_TW dc.subject (關鍵詞) 觀測者效應 zh_TW dc.subject (關鍵詞) 市場共識 zh_TW dc.subject (關鍵詞) 情緒指標 zh_TW dc.subject (關鍵詞) 八卦(易經) zh_TW dc.subject (關鍵詞) VIX en_US dc.subject (關鍵詞) Observer effect en_US dc.subject (關鍵詞) Market Consensus en_US dc.subject (關鍵詞) Sentiment Index en_US dc.subject (關鍵詞) Eight Trigrams (I Ching) en_US dc.title (題名) 具有方向性台灣VIX指標之建構與實證 zh_TW dc.title (題名) Construction and Empirical Testing of Directional Taiwan Volatility Index en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Atilgan, Y., Bali, T.G., & Demirtas, K. O. (2015). Implied Volatility Spreads and Expected Market Returns. Journal of Business and Economic Statistics, 33, 1.87-101.Baynes, C.F. (1950). The I Ching or Book of Changes. New York: Pantheon Books / Bollingen Series XIX.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.Cao, C., Simin, T., & Xiao, H. (2019). Predicting the Equity Premium 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, J., Jiang, G.J., Yuan, C., & Zhu, D. (2021). Breaking VIX at Open: Evidence of Uncertainty Creation and Resolution. Journal of Banking & Finance, 124, 106060.Fama, E.F., & French, K.R. (1993). Common Risk Factors in the Returns Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.Griffin, J.M., & Shams, A. (2018). Manipulation in the VIX? Review of Financial Studies, Society for Financial Studies, 31(4), 1377-1417.Gao, X., Wang, X., & Yan, Z. (2020). Attention: Implied Volatility Spreads and Stock Returns. Journal of Behavioral Finance, 21(4), 385-398.Han, B. (2008). Investor Sentiment and Option Prices. The Review of Financial Studies, 21(1), 387-414.Han, B., & Li, G. (2021). Aggregate Implied Volatility Spread and Stock Market Returns. Management Science, 67(2), 1249-1269.Hancock, G.D. (2012). VIX and VIX Futures Pricing Algorithms: Cultivating Understanding. Modern Economy, 3, 284-294.Kownatzki, K. (2016). How Good is the VIX as a Predictor of Market Risk? Journal of Accounting and Finance, 16(6), 39-60.Osterrieder, J., Roschli, K., & Vetter, L. (2019). The VIX Volatility Index—A Very Thorough Look at It. Working Paper, Zurich University of Applied Sciences, Switzerland.Prasad, A., & Bakhshi, P. (2022). Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning. Journal of Risk and Financial Management, 15(12), 552.Rosillo, R., Giner, J., & Fuente, D. (2014). The Effectiveness of the Combined Use of VIX and Support Vector Machines on the Prediction of S&P 500. Neural Computing and Applications 25(2), 321–332.Saha, A., Malkiel, B.G., & Rinaudo, A. (2019). Has the VIX Index been Manipulated? Journal of Asset Management, 20, 1-14.Serur, J.A., Dapena, J.P., & Siri, J.R. (2021). Decomposing the VIX Index into Greed and Fear, CEMA Working Papers: Serie Documentos de Trabajo. 780, Universidad del CEMA.Taiwan Futures Exchange, (2007). Annual Report.Young, T. (1802). The Bakerian Lecture: On the Theory of Light and Colours. Philosophical Transactions of the Royal Society of London, 92, 12–48. zh_TW
