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題名 長記憶分析
Long Memory Analysis: An Empirical Investigation
作者 蔡易群
貢獻者 山本竜市
Ryuichi Yamamoto
蔡易群
關鍵詞 長記憶
日期 2012
上傳時間 2-Sep-2013 15:17:57 (UTC+8)
摘要 本研究欲藉由結合ARFIMA及FIGARCH兩模型來試圖去捕捉長記憶現象。首先，本研究將簡單回顧兩模型之相關理論與應用，再介紹在實務上廣泛被運用之長記憶現象之相關檢定。ARFIMA及FIGARCH模型中詮釋長記憶現在之分型係數d，分別使得模型在描述與預估金融時間序列之報酬及波動性的表現上相當突出。
      本研究所研究之對象為日本股市，所使用的資料為日經225指數。實證結果顯示，日報酬中含條件異質性變異數之現象。在模型的選擇上，本研究預估及比較了許多不同的模型，並且確認了長記憶確實存在於報酬及波動，以及最適之模型為ARFIMA-FIGARCH。
參考文獻 5. References
     Baillie, R.T., Bollerslev, T., Mikkenlsen, H.O. 1996. Fractionally Intergrated Generalized Auroregressive Conditional Heteroskedaticity. Journal of Econometrics 74, PP. 3-30.
     Barkoulas, J.T., Baum, C.F., Travlos, N. 2000. Long Memory in the Greek Stock Market. Applied Financial Economics, 10, 177-184.
     Burton G.M. 1987. Efficient Market Hypothesis: The New Palgrave. A dictionary of Economics 2, PP. 120-23.
     Chordia, T., Roll, R., Subrahmanyam, A. 2008. Liquidity and Market Efficiency. Journal of Econometrics 87, PP.249-268.
     Chuang, W.I., Liu, H.H., Susmel, R. 2012. The bivirate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility. Global Finance Journal, In Press, Available Online 13, Elsevier.
     Conrad, C. 2010. Non-Negativity Conditions for the Hyperbolic GARCH Model. Journal of Econometrics 157, PP. 441_457.
     Ding, Z., Granger, C.W.J., Engle, R.F., 1993. A Long Memory Property of Stock Market Returns amd a New Model. Journal of Empirical Finance, PP83-106.
     Gourdazi, H. 2010. Modeling Long Memory in the Indian Stock Market using Fractional Integrated Egarch Model. International Journal of Trade, Economic and Finance, Vol. 1, No. 3, PP. 231-237
     Gewek, J., Porter-Hudak, S. 1983. The Estimation and Application of Long Memory Time Series Models. Journal of Times Series Analysis 4, PP. 221-238.
     Granger, C. W. J. 1980. Long Memory Relationships and the Aggregation of Dynamic Models. Journal of Econometrics, 14, PP. 227-238.
     Granger, C. W. J., Joyeux, R. 1980. An introduction to long memory time series model and fractional differencing. Journal of Time Series Analysis, No 1, PP. 15-29.
     Hosking, J. R. M. 1981. Fractional Differencing. Biometrica, 68, 165-176.
     Lee, J., Kim, T.S., Lee, H.K. 2000. Long Memory in Volatility of Korean Stock Market Returns. Available Online.
     Maheu, J.M. 2002. Can GARCH Models Capture the Long-range Dependence in Financial Market Volatility. Studies in Nonlinear Dynamics and Econometrics, 9(4), PP. 1-41.
     Mun, M., Brooks, R. 2012. The roles of news and volatility in stock market correlations during the global financial crisis. Emerging Markets Review 13(1), PP. 1-7.
     Nazarian, R., Naderi, E., Alikhani N.G., Amiri, A. 2013. Long Memory Analysis: An Empirical Investigation. MPRA, No. 45605.
     Poon, H., Granger, C.W.J. 2003. Forecasting Volatility in Financial Markets: A Review Journal of Economic Literature XLI, PP. 478-539.
     Vacha, L., Vosvrda, M.S. 2005. Dynamical Agent’s Strategies and the Fractal Market Hypothesis. Prague Economic Papers, No 2, PP. 163-170.
     Xiu, J., Jin, Y. 2007. Empirical Study of ARFIMA Model Based on Fractional Differencing. Physia: A 377, PP. 137-184.
描述 碩士
國立政治大學
國際經營與貿易研究所
100351021
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100351021
資料類型 thesis
dc.contributor.advisor 山本竜市zh_TW
dc.contributor.advisor Ryuichi Yamamotoen_US
dc.contributor.author (Authors) 蔡易群zh_TW
dc.creator (作者) 蔡易群zh_TW
dc.date (日期) 2012en_US
dc.date.accessioned 2-Sep-2013 15:17:57 (UTC+8)-
dc.date.available 2-Sep-2013 15:17:57 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2013 15:17:57 (UTC+8)-
dc.identifier (Other Identifiers) G0100351021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59234-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易研究所zh_TW
dc.description (描述) 100351021zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) 本研究欲藉由結合ARFIMA及FIGARCH兩模型來試圖去捕捉長記憶現象。首先，本研究將簡單回顧兩模型之相關理論與應用，再介紹在實務上廣泛被運用之長記憶現象之相關檢定。ARFIMA及FIGARCH模型中詮釋長記憶現在之分型係數d，分別使得模型在描述與預估金融時間序列之報酬及波動性的表現上相當突出。
      本研究所研究之對象為日本股市，所使用的資料為日經225指數。實證結果顯示，日報酬中含條件異質性變異數之現象。在模型的選擇上，本研究預估及比較了許多不同的模型，並且確認了長記憶確實存在於報酬及波動，以及最適之模型為ARFIMA-FIGARCH。		zh_TW
dc.description.tableofcontents Contents
     1. Introduction 1
     1.1. Literature Review 3
     1.2. Data description 5
     2. Methodology 7
     2.1. ARFIMA Model: Long Memory Model for Levels 7
     2.2. ARCH Models 8
     2.2.1. Linear ARCH Models 9
     2.2.2. The FIGARCH Model 10
     2.3. Tests Used for Identifying Long Memory Features 10
     2.3.1. ACF Test 10
     2.3.2. GPH Test(Spectral Density Method) 11
     3. Results Analysis 13
     3.1. Descriptive Analysis of the Data 13
     3.2. Stationary Test 17
     3.3. Specifying the Long Memory Parameter 19
     3.4. Estimating the ARIMA/ARFIMA Model 20
     3.5. Estimating the GARCH Models 22
     4. Conclusion 24
     5. References 25		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100351021en_US
dc.subject (關鍵詞) 長記憶zh_TW
dc.title (題名) 長記憶分析zh_TW
dc.title (題名) Long Memory Analysis: An Empirical Investigationen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 5. References
     Baillie, R.T., Bollerslev, T., Mikkenlsen, H.O. 1996. Fractionally Intergrated Generalized Auroregressive Conditional Heteroskedaticity. Journal of Econometrics 74, PP. 3-30.
     Barkoulas, J.T., Baum, C.F., Travlos, N. 2000. Long Memory in the Greek Stock Market. Applied Financial Economics, 10, 177-184.
     Burton G.M. 1987. Efficient Market Hypothesis: The New Palgrave. A dictionary of Economics 2, PP. 120-23.
     Chordia, T., Roll, R., Subrahmanyam, A. 2008. Liquidity and Market Efficiency. Journal of Econometrics 87, PP.249-268.
     Chuang, W.I., Liu, H.H., Susmel, R. 2012. The bivirate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility. Global Finance Journal, In Press, Available Online 13, Elsevier.
     Conrad, C. 2010. Non-Negativity Conditions for the Hyperbolic GARCH Model. Journal of Econometrics 157, PP. 441_457.
     Ding, Z., Granger, C.W.J., Engle, R.F., 1993. A Long Memory Property of Stock Market Returns amd a New Model. Journal of Empirical Finance, PP83-106.
     Gourdazi, H. 2010. Modeling Long Memory in the Indian Stock Market using Fractional Integrated Egarch Model. International Journal of Trade, Economic and Finance, Vol. 1, No. 3, PP. 231-237
     Gewek, J., Porter-Hudak, S. 1983. The Estimation and Application of Long Memory Time Series Models. Journal of Times Series Analysis 4, PP. 221-238.
     Granger, C. W. J. 1980. Long Memory Relationships and the Aggregation of Dynamic Models. Journal of Econometrics, 14, PP. 227-238.
     Granger, C. W. J., Joyeux, R. 1980. An introduction to long memory time series model and fractional differencing. Journal of Time Series Analysis, No 1, PP. 15-29.
     Hosking, J. R. M. 1981. Fractional Differencing. Biometrica, 68, 165-176.
     Lee, J., Kim, T.S., Lee, H.K. 2000. Long Memory in Volatility of Korean Stock Market Returns. Available Online.
     Maheu, J.M. 2002. Can GARCH Models Capture the Long-range Dependence in Financial Market Volatility. Studies in Nonlinear Dynamics and Econometrics, 9(4), PP. 1-41.
     Mun, M., Brooks, R. 2012. The roles of news and volatility in stock market correlations during the global financial crisis. Emerging Markets Review 13(1), PP. 1-7.
     Nazarian, R., Naderi, E., Alikhani N.G., Amiri, A. 2013. Long Memory Analysis: An Empirical Investigation. MPRA, No. 45605.
     Poon, H., Granger, C.W.J. 2003. Forecasting Volatility in Financial Markets: A Review Journal of Economic Literature XLI, PP. 478-539.
     Vacha, L., Vosvrda, M.S. 2005. Dynamical Agent’s Strategies and the Fractal Market Hypothesis. Prague Economic Papers, No 2, PP. 163-170.
     Xiu, J., Jin, Y. 2007. Empirical Study of ARFIMA Model Based on Fractional Differencing. Physia: A 377, PP. 137-184.		zh_TW