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    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.
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    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.
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    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.
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    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.
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    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:[Department of Money and Banking] Theses

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