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研究生:李尚霖
研究生(外文):Shang-Lin Li
論文名稱:應用關聯結構理論於期貨避險之研究
論文名稱(外文):A Study on Hedge Performance of Futures―The Application of Copula Theory
指導教授:黃明祥黃明祥引用關係王 甡
指導教授(外文):Ming-Hsiang HuangArgus Shen Wang
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:42
中文關鍵詞:多變量GARCH期貨避險關聯結構理論非對稱波動
外文關鍵詞:Multivariate GARCHFutures HedgeCopulaAsymmetric Volatility
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有鑑於指數股票型基金的興起,使用股票指數期貨規避指數股票型基金投資風險之績效成為現代投資實證研究上備受關注的議題。前人著作多採用波動不動稱族群的GARCH模型用以評估兩個市場的投資風險,用以計算避險比率,據以擬訂投資策略。惟,前述兩個市場報酬波動之間往往存在著相關性(Dependence),怠於考量此一現象,可能產生偏誤的結果。有別於前人著作,本研究導入Copulas函數捕捉兩市場間報酬波動的相關結構現象,檢視Copulas-GJR-GARCH替代模型(Alternative model)是否能更為精確估算避險比率,產生較佳的避險績效。研究結果顯示Gumbel-Copula函數較能刻畫變數間的相關結構,而達到較佳的避險效果。但就整體避險績效而言靜態的OLS表現優於大多數的Copula避險模型以及DCC、GJR-DCC模型。證明OLS的表現也有凌駕動態避險策略的趨勢,但透過適當的Copula函數選擇,兩標的資產相關結構能否確實的被描述會導致避險績效差異。
Due to the rise of Exchange Traded Fund, using Stock Index Futures to avoid the risk of investing Exchange Traded Fund has become a more and more popular issue on study of contemporary investments. Former papers on this subject introduced the GARCH model of asymmetric volatility to estimate the risk of investing in two different markets, and to calculate hedge ratio in order to make an investing strategy. However, in former descriptions, there would be certain dependence between the two markets, there could be a biased result when this fact of the existence of the dependence is ignored. Aside from former studies, this study introduces Copulas to capture the structure of returns volatility between the two markets so that we can check if this Copula-GJR-GARCH alternative model can provide a more precise hedge ratio and generate more effective hedge performance. According to this study, adopting Gumbel-Copula is proved to more effectively show the structures between variables, as a result, it is proved to be a better method for risk hedge. However, the OLS method performs more effective than most Copula hedge models, including DCC and GJR-DCC models. Thus, it is proved that OLS tends to overtop time-vary hedge model; however, via proper Copula function choosing, whether two targeted assets' structures can be precisely described or not could differentiate their hedge performances.
目錄
頁次
目錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 2
第三節 論文架構 4
第二章 文獻探討 5
第一節 指數股票型基金 5
第二節 波動與相關模型 8
第三節 關聯結構理論 10
第三章 研究方法 13
第一節 檢定分析 13
第二節 非對稱一般化自我迴歸條件異質變異模型 15
第三節 DCC-GARCH模型 16
第四節 關聯結構理論 18
第五節 避險績效之衡量 21
第四章 實證分析 24
第一節 資料來源與期間 24
第二節 樣本特徵統計分析 25
第五章 結論 33
第一節 結論 33
第二節 未來研究方向 34
參考文獻 35
圖目錄
圖1.1 全球ETF資產規模 1
圖1.2 研究架構 4
圖2.1 ETF交易過程 7
圖4.1 ETF與期貨價格及報酬走勢圖。 24
表目錄
表2.1 ETF與共同基金、指數期貨、指數選擇權之比較 8
表4.1 ETF與期貨報酬率之基本統計量 25
表4.2 雙變量 GARCH (1,1)-DCC 模型 26
表4.3 雙變量 GJR-GARCH (1,1)-DCC模型 26
表4.4 邊際函數模型(GARCH)參數估計結果 27
表4.5 邊際函數模型(GJR-GARCH)參數估計結果 28
表4.6 Copula函數估計(殘差項符合常態分配)參數估計結果 29
表4.7 Copula函數估計(殘差項符合T分配)參數估計結果 30
表4.8 Copula函數估計(殘差項符合GED分配) 參數估計結果 31
表4.9 投資組合避險效果(投資組合變異數) 32


參考文獻
一、中文部分
邵彥彬 (1993),「臺指50ETF簡介與市場概況」, 華南金控,第十期,1-5頁。
林華德與王甡 (1995),「台灣股市成交量對股價波動的影響」,企銀季刊,第十九卷第二期,40-58頁。
徐樹滋、葛思惠與陳正斌 (2001),「ETF在台灣發行交易之可行性研究」,台灣證券交易所。
曾毅豪 (2007),「關聯結構在金融市場風險管理之研究」,國立成功大學統計學研究所碩士論文。
劉美纓、王甡與蔡美華 (2001),「臺股指數現貨與期貨日內報酬波動不對稱關聯性之研究」,貨幣市場,第五卷第四期,17-40頁。
徐清俊與張加民 (2003),「台灣股價指數期貨最適避險比率探討」,遠東學報,第二十卷第三期,531-542頁
二、英文部分
Ackert, L. F. and Y. S. Tian (2000), “Arbitrage and Valuation in the Market for Standard and Poor's Depositary Receipts,” Financial Management, 29 (3), pp. 71-88.

Ang, A. and Chen J. (2002), “Asymmetric Correlations of Equity Portfolios”, Journal of Financial Economics, 63 (3), pp. 443-494.

Antoniou, A. and P. Holmes (1995), “Futures Trading Information and Spot Price Volatility: Evidence for the FTSE-100 Stock Index Futures Contact Using GARCH,” Journal of Banking and Finance, 19 (1), pp. 117-129.

Backus, D.K. and A.W. Gregory (1993), “Theoretical relations between risk and premiums and conditional variances,” Journal of Business and Economic Statistics, 11 (2), pp. 177-185.

Bartram, S., Taylor, S., and Wang, Y. (2007), “The Euro and European Financial Market Dependence,” Journal of Banking and Finance, 31, pp. 1461-81.

Black, F. (1976), “Studies of Stock Price Volatility Changes,” Proceedings of the 1976 Meeting of Business and Economic Statistics Section, American Statistical Association, pp. 177-181.
BlackRock (2011), ETF Landscape Industry Review, March 2011 edition.

Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasicity,” Journal of Econometrics, 31, pp. 307-327.

Bollerslev, T. (1987), “A Conditional heteroskedasitic time series for speculative prices and rate of return,” Review of Economics and Statistics 69, pp. 542-547.

Bollerslev, T. (1990), “Modeling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model,” Review of Economics and Statistics, 72 (3), pp. 498-505.

Bollerslev, T., R. F. Engle, and J. M. Wooldridge (1988), “A Capital Asset Pricing Model with Time Varying Covariances,” Journal of Political Economy, 96 (1), pp. 116-131.

Bouye, E., V. Durrleman, A. Nikeghbali, G. Riboulet, and T. Roncalli (2000), “Copula for Finance-A Reading Guide and Some Applications,” Groupe de Recherche Operationnelle, Credit Lyonnais, Working Paper.

Boyer, B. H., M. S. Gibson, and M. Loretan (1999), “Pitfalls in Test for Changes in Correlation,” International Finance Discussion Paper .

Campbell, J.Y. and L. Hentschel (1992), “No news is good news: an asymmetric model of changing volatility in stock returns,” Journal of Financial Economics, 31 (3), pp. 281-381.

Cappiello, L., R. F. Engle, and K. Sheppard (2006), “Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns,” Journal of Finance Econometrics, 4 (4), pp. 537-572.

Cherubini, U., and E. Luciano (2001), “Value-At-Risk Trade-off and Capital Allocation with Copulas,” Economic Notes, 30 (2), pp. 235-256.

Cherubini, U., and E. Luciano,(2002), “Bivariate Option Pricing with Copulas,” Applied Mathematical Finance, 9 (2), pp. 69-85.

Chou, R. Y. (1988), “Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH,” Journal of Applied Econometrics, 3 (4), pp. 279-294.

Clayton, D.,G. (1978), “A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence, ” Biometirka, 65, pp. 141–151.

Clemente D., Annalisa, and C. Romano (2003), “Measuring Portfolio Value-at-Risk by a Copula-EVT Based Approach,” Working Paper.

Costinot, A., Roncalli T. and Teiletche J. (2000), “Revisiting the Dependence between Financial Markets with Copula, ” Paris Credit Lyonnais Working Paper.

Dickey D. A. and W. A. Fuller (1979), “Distribution of Estimators for Autoregressive Time Series With a Unit Root,” Journal of the American Statistical Association, 74 (366), pp. 427-431.

Ederington L. H. (1979), “The Hedging Performance of the New Futures Markets,” The Journal of Finance, 34 (1), pp. 157-170.

Embrechts, P., A.J. McNeil, and D. Straumann (2002), “Correlation and dependency in risk management: properties and pitfalls,” In Risk Management: Value at Risk and Beyond (ed. M. Dempster), pp. 176–223. Cambridge University Press, Cambridge.

Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50 (4), pp. 987-1008.

Engle, R. F. (2002), “Dynamic Conditional Correlation—A Simple Class of Multivariate GARCH Models,” Journal of Business and Economic Statistics, 20 (3), pp. 339-350.

Engle, R. F., D. M. Lilien, and Robins R. P. (1987), “Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model,” Econometrica, 55 (2), pp. 391-407.
Engle, R. F. and González-Rivera (1991), “Semiparametric ARCH models,” Journal of Business and Economic Statistics, 9 (4), pp. 345-359.

Engle, R. F. and C.W.J. Grange (1987), “Cointegration and error-correction: representation, estimation and testing,” Econometrica, 55 (2), pp. 251-276.

Engle, R.F. and F. K. Kroner (1995), “Multivariate Simultaneous Generalized ARCH,” Econometric Theory, 11 (1), pp. 122-150.

Engle R. F and V. K. Ng (1993), “Measuring and testing the impact of news on volatility,” Journal of Finance 48 (5), pp. 1749-1778.

Fama, F. Eugene (1965), “The Behavior of Stock-Market Prices,” Journal of Business, 38 (1), pp. 34-105.

Figlewski, S. (1984), “Hedging performance and basis risk in stock index futures,” Journal of Finance, 39 (3), pp. 657-669.

Frees E. W., J. Carriere, and E. Valdez (1996), “Annuity Valuation with Dependent Mortality,” The Journal of Risk and Insurance, 63 (2), pp. 229-261.

Frees, E.W., and E.A. Valdez (1998), “Understanding relationships using Copulas,” North American Actuarial Journal, 2 (1), pp. 1-25.

French, K. R., G. W. Schwert, and R. E. Stambaugh (1987), “Expected Stock Returns and Volatility,” Journal of Financial Economics, 19 (1), pp. 3-29.

Frey, R. and A. J. McNeil (2001), “Modeling dependent defaults,” Working Paper, Department of Mathematics, ETH Zurich.

Genest C. and L. Rivest (1993), “Statistical Inference Procedures for Bivariate Archimedean Copulas,” Journal of American Statistical Association, 88 (423), pp. 1034-1043.


Genest C. B. ,Remillard and Beaudoin D. (2009), “Goodness-of-fit tests for copulas: A review and a power study,” Insurance: Mathematics and Economics, 44, pp. 199-214.

Glosten, L.R., R. Jagannathan, and D.E. Runkle, (1993), “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48 (5), pp. 1779-1801.

Granger C.W.J. and P. Newbold (1974), “Spurious regressions in econometrics,” Journal of Econometrics, 2 (2), pp. 111–120.

Gumbel,E.,J. (1960), “Bivariate exponential distributions,” Journal of the American Statistical Association 55, pp. 698–707.

Harvey, C.R. (1989), “Time-varying conditional covariance in tests of asset pricing models,” Journal of Financial Economics, 24 (2), pp. 289-317.

Hegde S. P. and J. B. McDermott (2004), “The market liquidity of DIAMONDS, Q's, and their underlying stocks,” Journal of Banking & Finance, 28 (5), pp. 1043-1067.

Jarque, C. M. and A. K. Bera (1980), “Efficient tests for normality, homoscedasticity and serial independence of regression residuals,” Economics Letters, 6 (3), pp. 255-259.

Joe, H. and J. J. Xu, (1996), “The Estimation Method of Inference Functions for Margins for Multivariate Models,” Technical Report, No. 166, Department of Statistics, University of British Columbia.

Johnson, L. L. (1960), “The Theory of Hedging and Speculation in Commodity Futures,” The Review of Economic Studies, 27 (3), pp. 139-15

Jorion, P. (2005). Value at Risk: the new benchmark for managing financial risk, Second Edition, New York: McGraw-Hill

Kostovetsky L.,(2003). “Index Mutual Funds and Exchange-Traded Funds,” The Journal of Portfolio Management, 29 (4), pp. 80-92

Ku, Y., Chen, H., and Chen, K., (2007), “On the Application of the Dynamic Conditional Correlation Model in Estimating Optimal Time-Varying Hedge Ratios,” Applied Economics Letters, 14, pp. 503-09.

Lai, Y. H. Chen Cathy W.S. and Gerlach R., (2009) “Optimal dynamic hedging via copula-threshold-GARCH models, ”Mathematics and Computers in Simulation 79 (8), pp. 2609-2624

Lai, Y. H., (2009), “Copula-Based Dynamic Hedging Strategies in Stock Index Futures: International Evidence,” Review of Future Markets, 18, pp. 7-26

Lai, Y. H. and J. C. Tseng, (2010), “The Role of Chinese Stock Market in Stock Market: A Safe Haven or a Hedge?” International Review Economics and Finance, 19, pp. 211-218

Lien, D. (2008),“A further note on the optimality of the OLS hedge strategy,” Journal of Futures Markets, 28 (3), pp. 308-311.

Lien, D. (2009). “A note on the hedging effectiveness of GARCH models,” International Review of Economics and Finance, 18, pp. 110-112.

Li, D.Z.,(2000) “On Default Correlation: A Copula Function Approach,” Journal of Fixed Income, 9 (1), pp. 43-54

Mandelbrot, Benoit, (1963), “The variation of Certain Speculative Prices,” Journal of Business, 36 (4), pp. 394-419

Markowitz, H. M., (1952) “Portfolio selection,” Journal of Finance,7 (1), pp. 77-91

Mendes, B.V.M. (2005),” Asymmetric extreme interdependence in emerging equity markets,” Applied Stochastic Models in Business and Industry, 21 (6), pp. 483-498

Mendes, B. V. M. and Souza, R. M., (2004), “ Measuring financial risks with Copulas,” International Review of Financial Analysis, 13 (1), pp. 27−45.

Nelson, C. R. and P C. R. losser (1982), “Trends and random walks in macroeconmic time series : Some evidence and implications,” Journal of Monetary Economics, 10 (2), pp. 139-162.

Nelson, D.B. (1990), “Stationary and persistence in the GARCH (1,1) model,” Econometrica Theory, 6, pp. 318-334.

Nelson, D.B. (1991), “Conditional heteroskedasticity in asset returns: A new approach”, Econometrica, 59 (2), pp. 347-370.

Nelsen, R. B. (2006), An introduction to Copula, Second Edition, New York: Springer Verlag

Pagan A., and G.W. Schwert (1990), “Alternative models for conditional stock volatility,” Journal of Econometrics, 45 (1-2), pp. 267-290.

Pan M. S. and L. P. Hsueh (1998), “Transmission of Stock Returns and Volatility between the U.S. and Japan: Evidence from the Stock Index Futures Markets,” Asia-Pacific Financial Markets, 5 (3), pp. 211-225.

Patton A. J. (2006 ), “Modeling Asymmetric Exchange Rate Dependence,” International Economic Review, 47 (2), pp. 527-556.

Patton, A. J. (2006b). “Estimation of multivariate models for time series of possibly different lengths,” Journal of Applied Econometrics, 21, pp. 147-173.

Phillips P. C. B. and P. Perron (1988), ”Testing for a unit root in time series regression,” Biometrika, 75 (2), pp. 335-346.

Poterba J. M and J. B. Shoven (2002), ” Exchanged-traded funds: a new investment option for taxable investors,” American Economic Review, 92 (2), pp. 422-427.

Rosenberg J. V. and T. Schuermann (2006), “A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risks,” Journal of Financial Economics, 79 (3), pp. 569-614.

Said S. E. and D. A. Dickey (1984), ”Testing for unit roots in autoregressive-moving average models of unknown order,” Biometrika, 71 (3), pp. 599-607.

Schwert, G. W. (1989), “ Why does stock market volatility change over time,” Journal of Finance, 44 (5), pp. 1115-1153.

Sklar A. (1959), “Fonctions de répartition à n dimensions et leurs marges,” Publications de l'Institut de Statistique de L'Université de Paris, 8, pp. 229-231.

Tse Y.,(1999), “Price discovery and volatility spillovers in the DJIA index and futures markets,” Journal of Futures Markets, 19 (8), pp. 911-930.

Vladimir de Vassal (2001), “Risk diversification benefits of multiple-stock portfolio”, The Journal of Portfolio Management, 27 (2), pp. 32-39.

Sharpe W. F. (1976), “The Parable of the Money Managers,” Financial Analysts Journal, 32 (4), pp. 4.

Working H. (1962), ”New Concepts Concerning Futures Markets and Prices,” The American Economic Review, 52 (3), pp. 431-459.



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