政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152392

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152392

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政大典藏 > College of Commerce > Department of International Business > Theses > Item 140.119/152392

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152392

Title:	股債市場波動之動態相關：基於VIX和MOVE指數的分量迴歸研究 Dynamic Correlation of Stock/Bond Market Volatility: A Quantile Regression Analysis based on VIX and MOVE Indices
Authors:	吳尚橙 Wu, Shang-Cheng
Contributors:	林信助吳尚橙 Wu, Shang-Cheng
Keywords:	股債關係 VIX MOVE 分量迴歸動態相關係數 Stock-Bond Relationship VIX MOVE Quantile Regression Time Varying Correlation Coefficient
Date:	2024
Issue Date:	2024-08-05 11:55:59 (UTC+8)
Abstract:	本文使用VIX與MOVE指數分別代表美國股債兩市的波動率，以此計算動態相關係數，描述股債兩市波動率的相關性。在平穩時期，兩市場波動相關性呈現低度的正相關；當市場發生重大金融事件時，市場風險較高，此時動態相關係數多處於高度正相關或負相關。接著進一步以分量迴歸檢視在不同市場狀態下，總體變數對於股債波動相關性的邊際效果。實證結果發現，在代表平穩時期的中間分量時，解釋變數的係數通常顯著，並且有較強烈的效果；而總體變數在高風險時期的左尾、右尾分量時，係數效果相對平穩時期會減弱至不顯著差異於0，甚至有變號的現象發生。這表示在股債波動性具有低度正相關的平穩時期，此時市場較無風險，總體變數較能對相關係數產生影響，並能提供解釋；在高度動盪的高風險時期，因總體衝擊或政府政策，使得股債波動性產生較明顯的相關性，此時市場過度恐慌、風險較高，總體變數的解釋能力較為貧弱，甚至因過度反應而出現不符預期的效果。 This thesis uses the VIX and MOVE indices to represent the volatility of the American stock and bond markets, and calculates time-varying correlation coefficients between the two indices to explain the relationship between these markets. During stable periods, the correlation coefficients show a low positive relationship, while during turmoil periods, the correlation coefficients show a high positive or negative relationship. This thesis further employs a quantile regression model to investigate the marginal effects of macroeconomic variables on the correlation coefficients across different quantiles under various market conditions. Our empirical results indicate that during stable periods, represented by middle quantiles, the coefficients of macroeconomic variables have a significant impact on the dependent variable. Conversely, during turmoil periods, represented by the left and right quantiles, these effects weaken or become insignificant, and may even change signs. These results suggest that macroeconomic variables have greater explanatory power during stable periods. However, during turmoil periods, market conditions driven by panic and irrationality, caused by financial events and government policies, reduce the explanatory power of macroeconomic variables and may lead to unexpected effects due to investor overreaction.
Reference:	莊家彰、管中閔(2005)。台灣與美國股市價量關係的分量迴歸分析。經濟論文，33(4)，379-404。 Aslanidis, N., & Christiansen, C. (2012). Smooth transition patterns in the realized stock–bond correlation. Journal of Empirical Finance, 19(4), 454-464. Aslanidis, N., & Christiansen, C. (2014). Quantiles of the realized stock–bond correlation and links to the macroeconomy. Journal of Empirical Finance, 28, 321-331. Asgharian, H., Christiansen, C., & Hou, A. J. (2015). Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification. Journal of Financial Econometrics, 14(3), 617-642. Adrian, T., Crump, R.K., & Vogt, E. (2019). Nonlinearity and Flight-to-Safety in the Risk-Return Trade-Off for Stocks and Bonds. The Journal of Finance, 74(4), 1931-1973. Budd, B. (2017). Canaries in the coal mine. The tale of two signals: the VIX and the MOVE Indexes. Economics & Finance Conference. Choi, J.E.& Shin, D.W. (2021). Nonparametric estimation of time varying correlation coefficient. J. Korean Stat. Soc., 50, 333–353. Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20, 339-350. Fang, L., Yu, H., Huang, Y. (2018). The role of investor sentiment in the long-term correlation between U.S. stock and bond markets. International Review of Economics & Finance, (58), 127-139. Grane, A., Veiga, H., & Martin-Barragan, B. (2014). Additive level outliers in multivariate GARCH models. Topics in statistical simulation, 114, 247–255. Iqbal, N., Bouri, E., Liu, G., & Kumar, A. (2022). Volatility spillovers during normal and high volatility states and their driving factors: A cross-country and cross-asset analysis, International Journal of Finance & Economics,29(1), 1-21 Koenker, R. and Bassett, G. (1978). Regression Quantiles. Econometrica,40, 33-50. Lin, F. Yang, S., Marsh, T., & Chen, Y. (2018). Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics & Finance, 55, 285-294. Tu, A. H., Hsieh, W. L. G., Wu, W. S. (2016). Market uncertainty, expected volatility and the mispricing of S&P 500 index futures. Journal of Empirical Finance, 35, 78-98. Yang, Z., Zhou, Y., & Cheng, X. (2020). Systemic risk in global volatility spillover networks: Evidence from option-implied volatility indices. The Journal of Futures Markets, 40(3), 392-409. Zhou, Y. (2014). Modeling the joint dynamics of risk-neutral stock index and bond yield Volatilities. Journal of Banking & Finance, 38, 216-228.
Description:	碩士國立政治大學國際經營與貿易學系 111351011
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111351011
Data Type:	thesis
Appears in Collections:	[Department of International Business] Theses

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