本研究旨在探討VIX指數（恐慌指數）對美國道瓊工業平均指數 成分股個股報酬率的影響。鑑於重大金融事件如金融危機、戰爭、恐怖事件等發生時，VIX指數會急遽上升，引發投資者的關注，但現有文獻對VIX指數對個股報酬率影響的研究仍有限。我們採用2014年至2023年的道瓊工業平均指數成分股日資料，基於Fama and French的三因子及五因子模型，並引入VIX指數的變動量和平方差於五因子模型中，以個股超額報酬為被解釋變數進行多元迴歸分析。實證結果顯示，五因子加入VIX指數平方差的模型有顯著負相關，其整體解釋能力優於原本的五因子模型，對個股報酬率有影響。VIX指數可作為個股投資決策的重要參考變數，並為投資者提供有用的資訊。

This study aims to investigate the impact of the VIX (Volatility Index) on the individual stock returns of the components of the Dow Jones Industrial Average (DJIA) in the United States. Given that significant financial events such as financial crises, wars, and terrorist attacks often lead to a rapid increase in the VIX, attracting widespread attention from investors, existing literature on the influence of the VIX on individual stock returns remains limited. We utilize daily data of the 30 components of the Dow Jones Industrial Average from 2014 to 2023. Employing Fama and French's three-factor and five-factor models, we incorporate the VIX into the models and conduct multiple regression analysis with individual stock excess returns as the dependent variable. The empirical results indicate that the model incorporating the VIX variance as part of the five-factor model not only exhibits a significant negative correlation but also outperforms the original five-factor model in explanatory power. This suggests that the VIX has an impact on individual stock returns and can serve as an important reference variable for stock investment decisions, providing valuable information for investors.

第一章　緒論 1
第一節　研究背景與動機 1
第二節　研究目的 1
第三節　研究架構 2
第二章　文獻探討 3
第一節　資本資產定價模型與多因子模型 3
第二節　VIX指數 6
第三節　VIX指數與股價報酬率的關係 9
第三章　研究方法 12
第一節　資料來源與研究期間 12
第二節　多元迴歸分析 14
第三節　實證模型 14
第四章　研究結果 17
第一節　敘述統計 17
第二節　迴歸分析實證結果 19
第三節　前一期VIX指數變化與股票報酬關係 24
第五章　結論與建議 26
參考文獻 28
附錄 30

1. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, 61(1), 259-299.
2. Bégin, J.-F., Dorion, C., & Gauthier, G. (2020). Idiosyncratic jump risk matters: Evidence from equity returns and options. The Review of Financial Studies, 33(1), 155-211.
3. Bakshi, G., & Kapadia, N. (2003). Delta-hedged gains and the negative market volatility risk premium. The Review of Financial Studies, 16(2), 527-566.
4. Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of financial Economics, 9(1), 3-18.
5. Black, F. (1972). Capital market equilibrium with restricted borrowing. The Journal of business, 45(3), 444-455.
6. Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1), 57-82.
7. Doran, J. S. (2020). Volatility as an asset class: Holding VIX in a portfolio. Journal of Futures Markets, 40(6), 841-859.
8. Durand, R. B., Lim, D., & Zumwalt, J. K. (2011). Fear and the Fama‐French factors. Financial Management, 40(2), 409-426.
9. Fama, E. F. (1968). Risk, return and equilibrium: some clarifying comments. The Journal of Finance, 23(1), 29-40.
10. Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. The Journal of Finance, 47(2), 427-465.
11. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial Economics, 33(1), 3-56.
12. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of financial Economics, 116(1), 1-22.
13. Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: A new measure. Journal of Futures Markets, 15(3), 265-302.
14. Jagannathan, R., & Wang, Z. (1996). The conditional CAPM and the cross‐section of expected returns. The Journal of Finance, 51(1), 3-53.
15. Jensen, M. C., Black, F., & Scholes, M. S. (1972). The capital asset pricing model: Some empirical tests.
16. Kanas, A. (2012). Modelling the risk–return relation for the S&P 100: The role of VIX. Economic Modelling, 29(3), 795-809.
17. Kanas, A. (2013). The risk-return relation and VIX: evidence from the S&P 500. Empirical Economics, 44, 1291-1314.
18. Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 13-37.
19. Lintner, J. (1975). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. In Stochastic optimization models in finance (pp. 131-155). Elsevier.
20. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
21. Merton, R. C. (1973). An Intertemporal Capital Asset Pricing Model. Econometrica, 41(5), 867-887.
22. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the econometric society, 768-783.
23. Neffelli, M., & Resta, M. (2018). Is VIX still the investor fear gauge? Evidence for the US and BRIC markets. arXiv preprint arXiv:1806.07556.
24. Reinganum, M. R. (1981). A new empirical perspective on the CAPM. Journal of financial and quantitative analysis, 16(4), 439-462.
25. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360.
26. Ross, S. A. (1978). The current status of the capital asset pricing model (CAPM). The Journal of Finance, 33(3), 885-901.
27. Sarwar, G. (2012). Intertemporal relations between the market volatility index and stock index returns. Applied Financial Economics, 22(11), 899-909.
28. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
29. Whaley, R. E. (1993). Derivatives on market volatility. The journal of Derivatives, 1(1), 71-84.
30. Whaley, R. E. (2000). The investor fear gauge. Journal of portfolio management, 26(3), 12.
31. Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.