Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/111746
|
Title: | 報酬率、連續波動度與跳躍項之因果關係-美國與歐洲期貨市場之實證研究 Causality Effect of Returns, Continuous Volatility and Jumps: Evidence from the U.S. and European Index Futures Markets |
Authors: | 廖志偉 Liao, Chih Wei |
Contributors: | 廖四郎 Liao, Szu Lang 廖志偉 Liao, Chih Wei |
Keywords: | 高頻資料 因果關係 槓桿效果 波動度回饋效果 跳躍 High-frequency data Causality Leverage effect Volatility feedback effect Jumps |
Date: | 2017 |
Issue Date: | 2017-08-10 09:47:03 (UTC+8) |
Abstract: | 本研究旨在探討金融危機期間,美國與歐洲金融市場之日內報酬率、實質波動度、連續波動度與跳躍風險行為之日內因果關係,並採用美國三大指數期貨(S&P 500, Dow Jones, Nasdaq)及歐洲期數期貨(FTSE, DAX, CAC)之高頻資料,檢定是否具有顯著槓桿效果(Leverage Effect)與波動度回饋效果(Volatility Effect)、在報酬率與跳躍風險之間具有相互影響效果。探討在金融危機發生前、後期間其日內報酬率、實質波動度、連續波動度與跳躍項間在1分鐘、5分鐘及60分鐘之抽樣頻率下之日內行為。因此,實證研究包含金融市場之上升及下降趨勢,顯示在金融危機發生後,日內波動度與跳躍項之槓桿效果(Leverage Effect)與波動度回饋效果(Volatility Effect)受到叢聚(Clustering)現象影響且顯著增加。不同抽樣頻率下之因果關係效果在金融危機發生前、中、後期間,特別在5分鐘及60分鐘之抽樣頻率方式,跳躍風險受到波動度回饋效果影響呈顯著增加,此實證結果對政策制定者及投資人具有重要之意涵。 This study examines the intraday causality between returns, volatility and jumps in the U.S. and European markets during the financial crisis. examine whether during the financial crisis, the S&P 500, Dow Jones, Nasdaq, FTSE, DAX and CAC index futures markets have a significant impact on the leverage and volatility feedback effects, as well as whether these interactions also occur between returns and jumps. The intraday behavior of 1-min, 5-min and 60-min sampling of returns, volatility and jumps is examined by employing data from the period between financial crisis. The study covers the major upward and downward trends in the market. Our empirical data indicate the main leverage and volatility feedback effects caused by intraday volatility and jump clustering significantly increased after the financial crisis. The causality effects with different sampling frequencies before, during and after the financial crisis show that jumps have increased the volatility feedback effect, especially when in a 5-min and 60-min sampling frequency is used. These findings have important implications for both policymakers and investors. |
Reference: | 1. Aı̈t-Sahalia, Y., (2004), “Disentangling Diffusion from Jumps,” Journal of Financial Economics, Vol. 74, p. 487-528. 2. Aı̈t-Sahalia, Y., (2009), “Estimating and Testing Continuous-Time Models in Finance: The Role of Transition Densities,” Annual Review of Financial Economics, Vol. 1, p. 341-359. 3. Aït-Sahalia, Y. and J. Jacod, (2009a), “Estimating the Degree of Activity of Jumps in High Frequency Data,” The Annals of Statistics, Vol. 37, p. 2202-2244. 4. Aït-Sahalia, Y. and J. Jacod, (2009b), “Testing for Jumps in A Discretely Oobserved Process,” The Annals of Statistics, Vol. 37, p.184-222. 5. Aït-Sahalia, Y. and J. Jacod, (2010), “Is Brownian Motion Necessary to Model Hhigh-Frequency Data?” The Annals of Statistics, Vol. 38, p.3093-3128. 6. Aït-Sahalia, Y., and J. Jacod, (2012), “Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data,” Journal of Economic Literature, Vol.50, Issue 4, p. 1007-1050. 7. Aït -Sahalia Y., J. Fan, and Y. Li, (2013) “The Leverage Effect Puzzle: Disentangling Sources of Biasat High Frequency,” Journal of Financial Economics, Vol. 109, Issue 1, p. 224-249. 8. Aït-Sahalia, Y., C. D., Julio and R. J. A. Laeven, (2015), “Modeling Financial Contagion Using Mutually Exciting Jump Processes,” Journal of Financial Economics,Vol. 117, Issue 3, p.585-606. 9. Aït -Sahalia Y., J. Fan, R. J.A. Laeven, D. C. Wang, and X. Yang, (2016) “Estimation of Continuous and Discontinuous Leverage Effects,” Journal of the American Statistical Association,forthcoming. 10. Andersen,T.G., T. Bollerslev, F. X. Diebold, and H. Ebens. (2001), “The Distribution of Stock Return Volatility,” Journal of Financial Economics, Vol. 61, p. 43-76. 11. Andersen,T.G., T. Bollerslev, and Dobrev, D., (2007a), “No-arbitrage Semi-martingale restrictions for Continuous-time Volatility Models Subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications,” Journal of Econometrics, Vol. 138, p. 125-180. 12. Andersen,T.G., T. Bollerslev, and F. X. Diebold, (2007b), “Roughing It Up: Including Jump Components in Measuring, Modeling and Forecasting Asset Return Volatility,” Review of Economics and Statistics,Vol.89, Issue 4, p. 701-720. 13. Andersen,T.G., T. Bollerslev, and N. Meddahi, (2011), “Realized Volatility Forecasting and Market Microstructure Noise,” Journal of Econometrics, Vol. 160, Issue 1, p.220-234. 14. Bandi, F. M.,and R. Renò (2012), “Time-varying leverage effects,” Journal of Econometrics, Vol. 169, Issue 1, p. 94-113. 15. Barndorff-Nielsen, O. E., (2004), “Power and Bipower Variation with Stochastic Volatility and Jumps,” Journal of Financial Econometrics, Vol. 2, p. 1-37. 16. Barndorff-Nielsen, O. E., (2005), “Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation,” Journal of Financial Econometrics, Vol. 4, p. 1-30. 17. Bekaert, G., and G. Wu, (2000) “Asymmetric Volatility and Risk in Equity Markets,” Review of Financial Studies, Vol. 13, p. 1-42. 18. Becker, R., A.E. Clements and A. McClelland (2009), “The jump component of S&P 500 volatility and the VIX index,” Journal of Banking & Finance, Vol. 33, Issue 6, p. 1033-1038 19. Black, F. (1976), “Studies of Stock Price Volatility Changes.” Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics,” American Statistical Association, Washington (D.C.), p. 177-181. 20. Bollerslev, T., J. Litvinova, and G. Tauchen, (2006) “Leverage and Volatility Feedback Effects in High-Frequency Data,” Journal of Financial Econometrics, Vol. 4, p. 353-384. 21. Bollerslev, T., Sizova, N., Tauchen, G., (2012) “Volatility in equilibrium: asymmetries and dynamic dependencies,” Review of Finance 16, p. 31-80. 22. Campbell, J., and L. Hentschel, (1992) “No News Is Good News: An Asymmetric Model of Changing Volatility in Stock Returns,” Journal of Financial Economics, Vol. 31, p. 281-331. 23. Christie, A. (1982), “The Stochastic Behavior of Common Stock Variances-Value, Leverage and Interest Rate Effects,” Journal of Financial Economics, Vol. 3, p. 145-166. 24. Granger C. W. J. and J. L. Lin (1995), “Causality in the Long Run,” Econometrica Theory, Vol. 11, p. 530-536. 25. Dufour, J. M. and A. Taamouti, (2010) “Short and Long Run Causality Measures: Theory and Inference,"Journal of Econometrics, Vol. 154, Issue 1, p. 42-58. 26. Dufour, J. M. R. Garcia and A. Taamouti, (2012) “Measuring High-Frequency Causality Between Returns, Realized Volatility, and Implied Volatility,” Journal of Financial Econometrics, Vol. 10, No. 1, p. 124-163. 27. French, K. R., G. W. Schwert, and R. F. Stambaugh, (1987) “Expected Stock Returns and Volatility,” Journal of Financial Economics, Vol. 19, p. 3-30. 28. Hasanhodzic, Jasmina and A. W. Lo, 2011. Black’s leverage effect is not due to leverage, Working paper, Boston University and Massachusetts Institute of Technology. 29. Huang, X. and G. Tauchen (2005) “The Relative Contribution of Jumps to Total Price Variance,” Journal of Financial Econometrics, Vol. 3, Issue. 4, p. 456-499 30. Pindyck, R. S. (1984), “Risk, Inflation, and the Stock Market,” American Economic Review, Vol. 74, p.334-351. |
Description: | 博士 國立政治大學 金融學系 99352503 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0993525033 |
Data Type: | thesis |
Appears in Collections: | [金融學系] 學位論文
|
Files in This Item:
File |
Description |
Size | Format | |
503301.pdf | | 11043Kb | Adobe PDF2 | 69 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|