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題名 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
作者 廖志偉
Liao, Chih Wei
貢獻者 廖四郎
Liao, Szu Lang
廖志偉
Liao, Chih Wei
關鍵詞 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
日期 2017
上傳時間 10-Aug-2017 09:47:03 (UTC+8)
摘要 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.
參考文獻 1. Aı̈t-Sahalia, Y., (2004), “Disentangling Diffusion from Jumps,” Journal of Financial Economics, Vol. 74, p. 487-528.\n2. 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.\n3. 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.\n4. Aït-Sahalia, Y. and J. Jacod, (2009b), “Testing for Jumps in A Discretely Oobserved Process,” The Annals of Statistics, Vol. 37, p.184-222.\n5. 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.\n6. 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.\n7. 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. \n8. 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.\n9. 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.\n10. 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.\n11. 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.\n12. 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.\n13. 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.\n14. Bandi, F. M.,and R. Renò (2012), “Time-varying leverage effects,” Journal of Econometrics, Vol. 169, Issue 1, p. 94-113. \n15. Barndorff-Nielsen, O. E., (2004), “Power and Bipower Variation with Stochastic Volatility and Jumps,” Journal of Financial Econometrics, Vol. 2, p. 1-37.\n16. 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.\n17. Bekaert, G., and G. Wu, (2000) “Asymmetric Volatility and Risk in Equity Markets,” Review of Financial Studies, Vol. 13, p. 1-42.\n18. 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\n19. 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.\n20. 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. \n21. Bollerslev, T., Sizova, N., Tauchen, G., (2012) “Volatility in equilibrium: asymmetries and dynamic dependencies,” Review of Finance 16, p. 31-80.\n22. 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.\n23. 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.\n24. Granger C. W. J. and J. L. Lin (1995), “Causality in the Long Run,” Econometrica Theory, Vol. 11, p. 530-536. \n25. 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. \n26. 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.\n27. 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.\n28. 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.\n29. 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\n30. Pindyck, R. S. (1984), “Risk, Inflation, and the Stock Market,” American Economic Review, Vol. 74, p.334-351.
描述 博士
國立政治大學
金融學系
99352503
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0993525033
資料類型 thesis
dc.contributor.advisor 廖四郎zh_TW
dc.contributor.advisor Liao, Szu Langen_US
dc.contributor.author (Authors) 廖志偉zh_TW
dc.contributor.author (Authors) Liao, Chih Weien_US
dc.creator (作者) 廖志偉zh_TW
dc.creator (作者) Liao, Chih Weien_US
dc.date (日期) 2017en_US
dc.date.accessioned 10-Aug-2017 09:47:03 (UTC+8)-
dc.date.available 10-Aug-2017 09:47:03 (UTC+8)-
dc.date.issued (上傳時間) 10-Aug-2017 09:47:03 (UTC+8)-
dc.identifier (Other Identifiers) G0993525033en_US
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=128604-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 99352503zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents Chapter 1 Introduction 1\nChapter 2 Literature Review 3\nChapter 3 Methodology 7\nChapter 4 Empirical Results and Analysis 13\n4.1 Data 13\n4.2 The empirical results regarding causality 18\nChapter 5 Conclusion 31\nBibliography 34zh_TW
dc.format.extent 11308419 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0993525033en_US
dc.subject (關鍵詞) 高頻資料zh_TW
dc.subject (關鍵詞) 因果關係zh_TW
dc.subject (關鍵詞) 槓桿效果zh_TW
dc.subject (關鍵詞) 波動度回饋效果zh_TW
dc.subject (關鍵詞) 跳躍zh_TW
dc.subject (關鍵詞) High-frequency dataen_US
dc.subject (關鍵詞) Causalityen_US
dc.subject (關鍵詞) Leverage effecten_US
dc.subject (關鍵詞) Volatility feedback effecten_US
dc.subject (關鍵詞) Jumpsen_US
dc.title (題名) 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zh_TW
dc.title (題名) Causality Effect of Returns, Continuous Volatility and Jumps: Evidence from the U.S. and European Index Futures Marketsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Aı̈t-Sahalia, Y., (2004), “Disentangling Diffusion from Jumps,” Journal of Financial Economics, Vol. 74, p. 487-528.\n2. 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.\n3. 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.\n4. Aït-Sahalia, Y. and J. Jacod, (2009b), “Testing for Jumps in A Discretely Oobserved Process,” The Annals of Statistics, Vol. 37, p.184-222.\n5. 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.\n6. 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.\n7. 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. \n8. 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.\n9. 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.\n10. 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.\n11. 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.\n12. 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.\n13. 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.\n14. Bandi, F. M.,and R. Renò (2012), “Time-varying leverage effects,” Journal of Econometrics, Vol. 169, Issue 1, p. 94-113. \n15. Barndorff-Nielsen, O. E., (2004), “Power and Bipower Variation with Stochastic Volatility and Jumps,” Journal of Financial Econometrics, Vol. 2, p. 1-37.\n16. 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.\n17. Bekaert, G., and G. Wu, (2000) “Asymmetric Volatility and Risk in Equity Markets,” Review of Financial Studies, Vol. 13, p. 1-42.\n18. 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\n19. 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.\n20. 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. \n21. Bollerslev, T., Sizova, N., Tauchen, G., (2012) “Volatility in equilibrium: asymmetries and dynamic dependencies,” Review of Finance 16, p. 31-80.\n22. 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.\n23. 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.\n24. Granger C. W. J. and J. L. Lin (1995), “Causality in the Long Run,” Econometrica Theory, Vol. 11, p. 530-536. \n25. 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. \n26. 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.\n27. 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.\n28. 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.\n29. 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\n30. Pindyck, R. S. (1984), “Risk, Inflation, and the Stock Market,” American Economic Review, Vol. 74, p.334-351.zh_TW