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研究生:李佳怡
研究生(外文):Chia-yi Li
論文名稱:股價指數期貨避險:考慮動態條件相關、基差與波動不對稱性
論文名稱(外文):Hedging With Stock Index Futures: Considering Dynamic Conditional Correlations, Asymmetric Effects of the Basis and Volatility
指導教授:林楚雄林楚雄引用關係
指導教授(外文):Chu-Hsiung Lin
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:風險管理與保險所
學門:商業及管理學門
學類:風險管理學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:61
中文關鍵詞:動態條件相關避險績效不對稱基差不對稱GARCH模型誤差修正模型股價指數期貨
外文關鍵詞:Hedging performancesStock index futuresAsymmetric effects of the basis
相關次數:
  • 被引用被引用:1
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本文檢視基差對於股價指數的現貨與期貨報酬與風險結構的影響,並以不加入基差、加入對稱基差與不對稱基差的動態條件相關雙變量GJR-GARCH(1,1)模型之最適避險比率,進一步評估三者模型的期貨避險績效優劣。另外,本研究亦考慮現貨與期貨的共整合,由此建構了誤差修正報酬模型。研究對象包括台灣加權股價指數、摩根台灣股價指數、那斯達克100指數、日經225指數與S&P 500指數,共五種股價指數。實證結果發現所有樣本指數現貨與期貨的變動關係支持持有成本理論的成立,且現貨與期貨報酬與波動皆存在不對稱基差效果。因此,本文檢視以GJR-GARCH模型為基礎所建構的不對稱基差避險模型是否優於其他避險策略。整體而言,有四個樣本指數在考慮基差效果模型的避險績效最佳,不對稱基差DCC GJR-GARCH模型提供NASDAQ 100指數與台灣加權股價指數最佳樣本外績效,而摩根台股指數與日經225指數採用對稱基差DCC GJR-GARCH模型時的避險績效最佳。
This paper investigates effects of the basis on the return and risk structure of stock index spot and futures, and uses optimal hedge ratios with no basis, symmetric effects, and asymmetric effects of the basis for estimating futures hedging performances. Besides, this paper considers the co-integration between spot and futures and hence establishes the error-correction model. Five samples which include TAIEX, MSCI Taiwan Stock Index, NASDAQ 100 Stock Index, Nikkei 225 Stock Index, and S&P 500 Stock Index are studied. Empirical results show that spot and futures in all index markets hold the prediction of the cost-of-carry theory, and the asymmetric effects of spread on spot and futures returns and volatilities are found in all markets. Therefore, this paper examines whether hedging performances of asymmetric effects of the basis GJR-GARCH model outperforms other hedging strategies. As a whole, four samples which consider the effect of the basis on the model provides the best performance. The asymmetric effect model provides the best out-sample performance for NASDAQ 100 index and TAIEX, and the symmetric effect model provides the best out-sample performance for MSCI Taiwan Stock Index and Nikkei 225 index.
摘要...................................................i
ABSTRACT...............................................ii
誌謝...................................................iii
目錄...................................................iv
表目錄.................................................v
第一章 緒論...........................................1
第一節 研究動機.......................................1
第二節 研究內容.......................................2
第三節 研究架構.......................................3
第二章 文獻回顧.......................................4
第一節 股價指數期貨...................................4
第二節 避險理論.......................................5
第三節 國外文獻探討...................................9
第四節 國內避險績效實證文獻..........................11
第三章 研究方法......................................12
第一節 研究流程......................................12
第二節 檢定方法......................................13
第三節 避險模型......................................16
第四節 避險績效的衡量................................20
第五節 研究限制......................................21
第四章 實證結果......................................22
第一節 資料來源與處理................................22
第二節 基本統計量....................................23
第三節 檢定結果......................................24
第四節 各模型的估計結果..............................28
第五節 各模型的避險績效..............................46
第五章 結論與建議....................................48
第一節 結論..........................................48
第二節 建議..........................................49
參考文獻..............................................50
一、 中文部分:
1.王毓蓉,「股價指數期貨動態避險之應用」,國立高雄第一科技大學金融營運所碩士論文,民國94年。
2.沈育展、洪瑞成、邱建良、李命志,「日經225指數期貨之避險績效與最適避險策略之探討」,輔仁管理評論,第十一卷,第一期,第153-180頁,民國93年。
3.邱建良、魏志良、吳佩珊、邱哲修,「TAIFEX與MSCI台股指數期貨與現貨直接避險策略之研究」,商管科技季刊,第五卷,第二期,第169-184頁,民國九十三年。
4.徐清俊、張加民,「臺灣股價指數期貨最適避險比率探討」,遠東學報,第20卷,第3期,第531-542頁,民國九十二年。
5.蔡政哲,「期貨持有成本評價模型—台灣的實證研究」,中原大學碩士論文,民國九十年。
6.吳羽容,「期貨避險比率之估計—波動不對稱性與動態相關係數」,國立高雄第一科技大學財務管理所碩士論文,民國九十三年。


二、 英文部分:
1.Black, F. (1976), “Studies of Stock, Price Volatility Changes”, Proceedings of the American Statistical Association: Business and Economic Statistics Section, 177-181.
2.Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, 31, 307-327.
3.Brooks, C. (2002), Introductory Econometrics for finance, Cambridge University Press.
4.Brooks, C., Henry, O. T. and Persand, G. (2002), “The Effect of Asymmetries on Optimal Hedge Ratios”, Journal of Business, 75, 333-352.
5.Christie, A. (1982), “The Stochastic Behavior of Common Stock Variance: Value, Leverage and Interest Rate Effects”, Journal of Financial Economics, 10, 407-432.
6.Ederington, L. H. (1979), “The Hedging Performance of the New Futures Markets”, Journal of Finance, 34, 157-170.
7.Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of Untied Kingdom Inflation”, Econometrica, 50, 987-1007.
8.Engle, R. F. (2002), “Dynamic Conditional Correlation: A New Simple Class of Multivariate GARCH Models”, Journal of Business and Economic Statistics, 20, 339-350.
9.Engle, R. F., Granger, C. W. J. (1987), “Cointegration and Error-Correction: Representation, Estimation, and Testing”, Econometrica, 55, 251-276.
10.Engle, R. F., Ng, V. K. (1993), “Measuring and Testing the Impact of News on Volatility”, Journal of Finance, 48, 1749-1778.
11.French, R. K., Schwert, G. W., Stambaugh, R. F. (1987), “Expected Stock Returns and Volatility”, Journal of Financial Economics, 19, 3-29.
12.Howard, C. T., D’Antonio, L. J. (1984), “A Risk-Return Measure of Hedging Effectiveness”, Journal of Financial and Quantitative Analysis, 19, 101-112.
13.Johnson, L. L. (1960), “The Theory of Hedging and Speculation in Commodity Futures”, Review of Economic Studies, 27, 139-151.
14.Kogan, L., Livdan, D., and Yaron, A. (2008), “Oil Futures Prices in a Production Economy With Investment Constraints (Working Paper), Cambridge, MA: MIT.
15.Kroner, K. F., Sultan, J. (1993), “Time Varying Distribution and Dynamic Hedging With Foreign Currency Futures”, Journal of Financial and Quantitative Analysis, 28, 535-551.
16.Lanza, A., Manera, M., McAleer, M. (2006), “Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns”, Finance Research Letters, 3, 114-132.
17.Lee, T. H. (1994), “Spread and Volatility in Spot and Forward Exchange Rates”, Journal of International Money and Finance, 13, 375-383.
18.Ljung, G., Box, G. (1978), “On a Measure of Lack of Fit in Time Series Models”, Biometrica, 65, 297-303.
19.Lien, D., Yang, L. (2006), “Spot-Futures Spread, Time-Varying Correlation, and Hedging With Currency Futures”, Journal of Futures Markets, 26, 1019-1038.
20.Myers, R. J. (1991), “Estimating Time-Varying Optimal Hedge Ratio on Futures Markets”, Journal of Futures Markets, 6, 39-53.
21.Ng, V., Pirrong, S. C. (1994), “Fundamentals and Volatility: Storage, Spreads, and the Dynamics of Metals Prices”, Journal of Business, 67, 203-330.
22.Park, T. H., Switzer, L. N. (1995), “Bivariate GARCH Estimation of the Optimal Hedge Ratio for Stock Index Futures: A Note”, Journal of Futures Markets, 15, 61-67.
23.Poterba, J., Summers, L. (1986), “The Persistence of Volatility and Stock Market Fluctuations”, American Economic Review, 76, 1142-1151.
24.Schwert, G. W. (1990), “Stock Volatility and the Crash of 87”, Review of Financial Studies, 3, 77-102.
25.Tse, Y. K., Tsui, A. K. C. (2002), “A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations”, Journal of Business and Economic Statistics, 20, 351-362.
26.Working, H. (1953), “Futures Trading and Hedging”, American Economic Review, 43, 314-343.
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