本研究詳細探討波動度指數如何應用於Cirelli et al.（2017）所提出的非連續目標波動度策略，並檢測其是否能夠更有效地提高投資組合之績效。除了沿用Cirelli et al.（2017）原先的設定之外，我們也提出使用統計分位數跟極端值的定義來設定策略中的目標波動度與警戒值；不僅在參數設定上較不任意，投資人也可以依據不同的股票市場，設定不同的參數。我們針對三個美國大盤市場進行研究，分別是S&P 500、道瓊工業指數、納斯達克100指數及其對應的波動度指數，資料期間皆由該資料之起始日至2023年4月30日。實證結果發現，非連續目標波動度策略確實能夠較大盤提高績效，但並沒有明顯比傳統目標波動度策略帶來更低的下檔風險；但若將金融危機時期樣本切割出來，則可發現該策略能有效降低下檔風險，顯示非連續目標波動度策略能改善傳統目標波動度策略在高波動時期仍投資一定比例在風險性資產的缺點。本研究之貢獻在於闡明非連續目標波動度策略機制之操作與波動度指數之應用，同時，本研究也研究了S&P 500以外的股票市場，驗證目標波動度策略與波動度指數在其他美國大型成熟市場也都能夠帶來更佳的績效。

This thesis examines in detail how volatility indices can be applied to the discontinuous target volatility strategy proposed by Cirelli et al. (2017), and investigates whether it can improve portfolio performance more effectively. In addition to following the original setting of Cirelli et al. (2017), we also propose to set the target volatility and warning values required by the strategy according to statistical quantiles and the definition of extreme values. Not only is such parameter setting less ad hoc, investors can also set parameters according to situations in different stock markets. We conduct empirical studies on three large U.S. markets, namely the S&P 500 index, the Dow Jones Industrial Index, the Nasdaq 100 Index and their corresponding volatility indices. The data period is from the starting dates of volatility indices to April 30, 2023. The results show that the discontinuous target volatility strategy can indeed improve the performance of the market, but it does not bring significantly lower downside risks than the traditional standard volatility strategy. However, based on the sub-sample of the financial crisis period, it can be found that the strategy can effectively reduce the downside risk, showing that the discontinuous target volatility strategy can indeed improve the shortcomings of traditional target volatility strategies during highly volatility market period. This thesis contributes to related literature by demonstrating how volatility indices can be properly applied and improve performance of target volatility strategies. In addition, we also demonstrate the effectiveness of the discontinuous target volatility strategies when applied to other large and mature markets in the United States.

1. 周雨田、陳唯帆、殷正華（2011），VIX對崩盤風險之避險功能分析。Journal of Futures and Options Vol.4 No.2。
2. 黃韋中（2021），利用VIX指數和ARMA-GARCH模型預測波動度之目標波動度策略績效分析。國立政治大學金融學系研究所未出版碩士論文，臺灣臺北。
3. Auinger, F. (2015). The Causal Relationship between the S&P 500 and the VIX Index: Critical Analysis of Financial Market Volatility and Its Predictability. Springer, 37-41.
4. Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finance, 55(2), 773-806.
5. Bongaerts, D., Kang, X., Dijk, M.V. (2020). Conditional Volatility Targeting, Financial Analysts Journal, 76:4, 54-71.
6. Cirelli, S., Vitali, S., Ortobelli Lozza, S., Moriggia, V. (2017). A conservative discontinuous target volatility strategy. Investment Management and Financial Innovations, 14(2-1), 176-190.
7. Dachraoui, K. (2018). On the Optimality of Target Volatility Strategies. Journal of Portfolio Management 44(5): 58-67.
8. Harvey, C. R., E. Hoyle, R. Korgaonkar, S. Rattray, M. Sargaison, and O. Van Hemert. (2018). The Impact of Volatility Targeting. The Journal of Portfolio Management August 2021, 47 (8) 57-74.
9. Liu, F., Tang, X., & Zhou, G. (2019). Volatility-Managed Porfolio: Does It Really Work? The Journal of Portfolio Management.
10. Markowitz, H. M. (1952). Portfolio Selection. Journal of Finance, 7, 77-91.
11. Moreira, A., & Muir, T. (2017). Volatility‐Managed Portfolios. The Journal of Finance, 72(4): 1611-1644.
12. Mylnikov, G. (2021). Volatility Targeting: It's Complicated! The Journal of Portfolio Management August 2021, 47 (8) 57-74.
13. Tukey, J. W. (1977). Exploratory data analysis.
14. Wang, H. (2019). VIX and volatility forecasting: A new insight. Physica A: Statistical Mechanics and its Applications, 533, 121951.