政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146341

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146341

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政大典藏 > College of Commerce > Department of International Business > Theses > Item 140.119/146341

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146341

Title:	條件目標波動度策略 — 波動度指數之運用與多國比較分析 Conditional Target Volatility Strategy - Use of Volatility Index and Multi-Country Comparative Analysis
Authors:	文莛橞 Wen, Ting-Hui
Contributors:	林信助文莛橞 Wen, Ting-Hui
Keywords:	目標波動度策略波動度指數已實現波動度投資組合多國比較 Volatility Targeting Strategy Volatility Index Realized Volatility Portfolio Multi-Country Comparisons
Date:	2023
Issue Date:	2023-08-02 13:13:18 (UTC+8)
Abstract:	本研究延伸 Bongaerts et al. (2020) 之條件目標波動度策略，探討波動度指數的運用及模型設定，並透過不同市場的比較分析，來驗證波動度指數在傳統與條件目標波動度策略中對提升投資組合報酬及降低下檔風險的有效性。考慮投資人風險偏好之差異，我們同時在傳統與條件目標波動度策略中加入最大槓桿程度的設定，不僅使兩種策略能在同一基準進行比較，並容許投資人可以根據自身風險態度選擇合適之槓桿程度。在實證方面，我們針對美國、臺灣、中國、日本和歐洲等五大市場進行分析，採用S&P 500指數、臺灣加權股價指數、香港恆生指數、日經225指數和歐洲50指數等股價指數及其相對應的波動度指數。實證結果顯示條件目標波動度策略相較於傳統目標波動度策略保守，能更有效的降低下檔風險且周轉率較低，報酬表現則不一。且當最大槓桿程度為1時，降低下檔風險的效果較好，周轉率也較低；而當最大槓桿程度為2，報酬表現較佳。本研究的貢獻在於，闡明條件目標波動度策略在不同國家的應用，並驗證採用波動度指數能更有效的降低下檔風險，周轉率也較低，可以節省投資人的交易成本。 This thesis extends the conditional target volatility strategy of Bongaerts et al. (2020), and explores the usefulness of volatility indices. Through a comparative analysis of different markets, we demonstrate the effectiveness of volatility indices in enhancing portfolio returns and reducing downside risk in both conventional and conditional target volatility strategies. Considering the difference in investors` risk preferences, we also impose the same leverage ratio in both the conventional and the conditional target volatility strategies, which not only enables the two strategies to be compared on the same benchmark, but also allows investors to choose the appropriate degree of leverage according to their own risk attitude. For the empirical investigation, we analyze five major markets from the United States, Taiwan, China, Japan and Europe. Data examined in this thesis include S&P 500 Index, Taiwan Weighted Stock Index, Hong Kong Hang Seng Index, Nikkei 225 Index and Europe 50 Index and their corresponding volatility indices. The empirical results show that the conditional target volatility strategy is more conservative than the conventional target volatility strategy, which can more effectively reduce the downside risk, and the turnover rate is also lower, but results on return performance are mixed. When the maximum leverage level is set to 1, the effect of reducing downside risk is better, and the turnover rate is also lower; while, when the maximum leverage level is set to 2, the return performance is better. This thesis contributes to clarify the application of the conditional target volatility strategy in different countries. In addition, we verify that using volatility indices has a better effect on downside risk control, and entails lower turnover rates which imply lower transaction costs for investors.
Reference:	Bongaerts, D., Kang, X., & van Dijk, M. (2020). Conditional volatility targeting. Financial Analysts Journal, 76(4), 54-71. Dachraoui, K. (2018). On the Optimality of Target Volatility Strategies. Journal of Portfolio Management, 44(5):58–67. 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. Cirelli, S., Lozza, S. O., & Moriggia, V. (2017). A conservative discontinuous target volatility strategy. Investment Management & Financial Innovations, 14(2), 176. Giese, G. (2010). On the risk-return profile of leveraged and inverse ETFs. Journal of Asset Management, 11(4), 219-228. Harvey, C. R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M., & Van Hemert, O. (2018). The impact of volatility targeting. Available at SSRN 3175538. Hallerbach, W. G. (2012). A proof of the optimality of volatility weighting over time. Available at SSRN 2008176. Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20(7), 873-889. Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: a joint model for returns and realized measures of volatility. Journal of Applied Econometrics, 27(6), 877-906. Henkel, S. J., Martin, J. S., & Nardari, F. (2011). Time-varying short-horizon predictability. Journal of financial economics, 99(3), 560-580. Kongsilp, W., & Mateus, C. (2017). Volatility risk and stock return predictability on global financial crises. China Finance Review International, 7, 1, 33-66. Kritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22-39. Liu, F., Tang, X., & Zhou, G. (2019). Volatility-managed portfolio: Does it really work?. The Journal of Portfolio Management, 46 (1), 38-51. Moreira, A., & Muir, T. (2017). Volatility‐managed portfolios. The Journal of Finance, 72(4), 1611-1644. Mylnikov, G. (2021). Volatility Targeting: It’s Complicated!. The Journal of Portfolio Management, 47(8), 57-74. Nadarajah, S., Zhang, B., & Chan, S. (2014). Estimation methods for expected shortfall. Quantitative Finance, 14(2), 271-291. Pong, S., Shackleton, M. B., Taylor, S. J., & Xu, X. (2004). Forecasting currency volatility: A comparison of implied volatilities and AR (FI) MA models. Journal of Banking & Finance, 28(10), 2541-2563. Whaley, R. E. (2009). Understanding the VIX. Journal of Portfolio Management, 35(3), 98-105. Qian, E., Sorensen, E. H., & Hua, R. (2007). Information horizon, portfolio turnover, and optimal alpha models. The Journal of Portfolio Management, 34(1), 27-40.
Description:	碩士國立政治大學國際經營與貿易學系 110351031
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110351031
Data Type:	thesis
Appears in Collections:	[Department of International Business] Theses

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