Copula-based GARCH模型於期貨避險之應用

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姓名

曾至苹(Chih-Ping Tseng) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

Copula-based GARCH模型於期貨避險之應用
(Futures Hedging with a Copula-based Multivariate GARCH Model)

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摘要(中)

過去的研究指出，固定的避險比率並不能達到最佳的避險效果，而建議最適避險比率應隨時間變動調整。本篇論文提出以Copula-based GARCH模型估計避險比率，並將其避險績效與傳統避險模型 (OLS)，CCC GARCH模型，以及DCC GARCH模型相比較。Copula-based GARCH模型不受限於常態分配的假設，使資產邊際分配的選擇更有彈性，更能貼近其真實的分配。實證結果指出，不論是在樣本內或樣本外，以Copula-based GARCH模型估計避險比率所形成的投資組合變異數皆為最小，有最好的避險績效。

摘要(英)

Many recent studies have demonstrated that using the constant hedge ratio obtained by the ordinary least squares method is inappropriate and hence different dynamic hedging strategies are suggested. In this paper we propose a new copula-based GARCH model to estimate the optimal hedge ratio, and compare its hedging effectiveness with different hedge models, including the constant conditional correlation GARCH model and the dynamic conditional correlation GARCH model. The advantage of the proposed model is that it allows for a more flexible distribution specification; Namely, marginal distributions or the dependence structure can be considered separately and simultaneously without the multivariate normality assumption. Hedging performance, in terms of variance reduction of portfolio returns, is evaluated for alternative models. Based on in-sample and out-of-sample comparisons, we find that the proposed model provides best hedging effectiveness.

論文目次

Contents
1. Introduction 1
2. Methodology 6
2.1 Hedging Theories 6
2.2 Bivariate Constant Correlation GARCH 9
2.3 Dynamic Conditional Correlation Multivariate GARCH 10
3. Copula-based Multivariate GARCH 12
3.1 Copula 12
3.2 Estimation of Parameters 14
3.3 Empirical Methodology 15
4. Data 19
5. Empirical Analysis 20
5.1 Parameters Estimation 20
5.2 In-sample Comparison of Hedging Performance 21
5.3 Out-of-sample Comparison of Hedging Performance 23
6. Conclusion 25
Reference 26

參考文獻

Reference
Baillie, R. T., and R. J. Myers. “Bivariate GARCG Estimation of the Optimal Commodity Futures Hedge.” Journal of Applied Econometrics, 6 (April-June 1991), 109-124.
Bartram, S. M., S. J. Taylor, and Y. H. Wang. “The Euro and European Financial Market Integration.” (2005).
Bollerslev, T. "Generalized Autoregressive Conditional Heteroskedasticity." Journal of Econometrics, 31 (1986), 307-327.
Bollerslev, T. “Modeling the Coherence in Short-Run Nominal Exchang Rates: A Multivariate Generalized ARCH Approach.” Review of Economics and Statistics, 72 (1990), 498-505.
Cecchetti, S.G., R.E. Cumby, and S. Figlewski. “Estimation of Optimal Hedge.” Review of Economics and Statistics, 50 (1988), 623-630.
Chen N.Y., R.Y. Chou, N. Liu, and G. Shyy. “Optimal Hedge Ratio of Commodity Futures Using Bivarite DCC-CARR and DCC-GARCH Models.” (2005)
Ederington, L. H. “The Hedging Performance of the New Futures Markets.” Journal of Finance, 34 (March 1979), 157-170.
Engle, R.F. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50 (1982), 987-1007.
Engle, R.F. “Dynamic Conditional Correlation- a Simple Class of Multivariate GARCH Models.” Journal of Business and Economic Statistics, 20 (2002), 339-350.
Engle, R.F. and C. W. J. Granger. “Cointegration and Error Correction: Representation, Estimation and Testing.” Econometrica, 55 (1987), 251-276.
Engle, R.F. and K.F. Kroner. “Multivariate Simultaneous Generalized ARCH,” Econometric Theory, 11 (1995), 122-150.
Engle, R.F. and K. Sheppard. “Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH.” Stern Finance Working Paper Series (2001), FIN-01-027.
Fama, E. F. “The Behavior of Stock Market Prices.” Journal of Business, 38 (1965), 34-105.
Figlewski, S. “hedging with Stock Index Futures: Theory and Application in a New Market.” Jounal of Futures Markets, 5 (July 1984), 183-199.
Ghosh A., “Hedging with Stock Index Futures: Estimation and Forecasting with Error Correction Model.” Journal of Futures Markets, vol. 1 (1993), 743-752.
Hansen, Bruce, 1994, Autoregressive conditional density estimation, International
Economic Review 35, 705-730.
Joe, H. “Multivariate Models and Dependence Concepts.” London, Chapman & Hall(1997).
Kroner, K. F., and J. Sultan. “Exchange Rate Volatility and Time Varying Hedge Ratios.” Pacific-Basin Capital Markets Research, Volume II, S. G. Rhee and R. P. Chang, eds. Amsterdam: Elsevier Science Publishers, North-Holland (1991), 397-412
Kroner, K. F., and J. Sultan. “Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures.” Journal of Finance and Quantitative analysis, Vol. 28, No. 4 (1993), 535-551.
Lee, C. F., Ed Bubnys and Y. Lin. “Stock Index Futures Hedge Ratios: Tests on Horizon Effects abd Functional Form.” Advances in Futures and Options, Vol. 2 (1987).
Myers, R.J. “Estimating Time-Varying Optimal Hedge Ratios on Futures Markets.” Journal of Futures Markets, 11 (1991), 39-53.
Nelsen, R. B. “An Introduction to Copulas.” New York, Springer (1999).
Newey, W. K. and McFadden D. “Large Sample Estimation and Hypothesis Testing.” In Engle, R. F. and McFadden eds. Handbook of Econometrics 4, North-Holland, Amsterdam (1994).
Patton, A. J. “Estimation of Multivariate Models for Time Series of Possibly Different Lengths.” LSE Working Paper (2004).
Patton, A. J. “Modeling Asymmetric Exchange Rate Dependence.” International Economic Review, forthcoming (2005).
White, H. “Estimation, Inference and Specification Analysis.” New York, Cambridge University Press (1994).

指導教授

王耀輝、徐之強
(Yaw-Huei Wang、Chih-Chiang Hsu)

審核日期

2006-7-4

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