利用DCC-CARR及DCC-GARCH模型求算商品期貨最適避險比率

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姓名

陳昱宏(Nash Chen) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

利用DCC-CARR及DCC-GARCH模型求算商品期貨最適避險比率
(Optimal Hedge Ratio of Commodity Futures Using Bivariate DCC-CARR and DCC-GARCH Models)

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摘要(中)

對於期貨及現貨市場的參與者而言，他們會利用相對的避險策略來反應他們風險的態度及各自的目標，同時，他們若要讓個自的投資組合有著良好的績效，其中不僅需要利用市場上所謂的基本分析及技術分析來極大化他們各自的報酬，更必須要利用多角化的技術來分散他們的風險。然而，如同我們所知，系統風險能夠有效地被期貨契約所抵減掉。因此，在本篇論文，主要是針對商品期貨及現貨來進行分析，並著重於將投資組合的風險極小化。
在本篇文章使用的模型主要為 Chou et. al., (2005) 所提出的DCC-CARR為主體來計算最適避險比率並比較各個模型的避險有效性，其它模型分別為最小平方法(OLS)、CCC-GARCH、CCC-CARR及DCC-GARCH。
最後，我們利用與DCC-CARR來比較的風險抵減程度判定各個模型的優劣成敗，其中在樣本內的避險而言，除了黃金商品以外，我們所提出的DCC-CARR模型大都比其它模型要來的佳。然而，在樣本外五期及每期五十個值來做避險有效性的分析，結果發現與樣本內的結果大致相同，就一期而言，所有商品的避險效果均支持DCC-CARR為一較好的避險模型。整體而言，不論就樣本內及樣本外而言，DCC-CARR相對於其它模型表現均較為優異，日後投資人應可應用於實務上，以利找到最小風險的投資組合。

摘要(英)

When traders participate in both cash and futures markets they must choose a hedging strategy that reflects their individual goals and attitudes towards risk. At the same time, optimal portfolio management depends not only on the fundamental and technological analysis in maximizing returns, but it also encompasses diversification techniques in (un)systematic risk. Nevertheless, systematic risk can be effectively eliminated by futures contracts.
In this thesis, we focus on diversification to minimize the portfolio variance and will consider the minimum-variance hedge strategy because the benefits of sophisticated estimation techniques of the hedge ratio are small (Lence, 1995b). At first, we take the commodity prices, and then compute the Optimal Hedge Ratios (OHRs) between spot and futures using different methods. Here, the hedge ratios are used to hedge the spot price risk in simulations of investment.
In analysis, we use the Dynamic Conditional Correlation - Conditional Autoregressive Range (DCC-CARR) model proposed by Chou et. al. (2005) to compute the OHRs. Other alternative methods used for comparison include the ordinary least squares (OLS) estimator which provides an estimate for the minimum-variance hedge ratio, Constant Conditional Correlation –Generalized Autoregressive Conditional Heteroskedasticity and CARR (CCC-GARCH and CCC-CARR) models, and DCC-GARCH model.
Different methods used to compute hedge ratios are compared with each other in their performance of variance-reduction. While the spot price risk is hedged by their corresponding futures, within-sample hedge, the results show that the DCC-CARR model performs better than the other hedge models for the selected commodities with the exception of gold. For an out-sample hedge in one-period it supports that the DCC-CARR model is the best model for any commodity. But, in other period, the results are mixed because of the trading noises. In conclusion, we suggest that the DCC-CARR model is the better model for investors to find the minimum-variance of a portfolio.

關鍵字(中)

★ 避險
★ 期貨
★ 最小風險避險
★ 最適避險比率
★ 風險管理

關鍵字(英)

★ minimum-variance hedge
★ hedge
★ futures
★ optimal hedge ratio
★ risk management

論文目次

Abstract.......................................................................................................................II
Table of Contents......................................................................................................IV
1. Introduction..............................................................................................................1
2. Literature Review....................................................................................................3
3. Methodology.........................................................................................................5
3.1 Hedging With Futures Contracts.................................................................5
3.2 Estimation of Optimal Hedge Ratios............................................................7
3.3 The GARCH model......................................................................................10
3.4 The CARR model.........................................................................................11
3.5 The DCC model............................................................................................13
4. Data Analysis..........................................................................................................17
4.1 Data Source...................................................................................................17
4.2 Univariate GARCH and CARR model......................................................21
4.3 Unit root test.................................................................................................24
5. Empirical Analysis.................................................................................................26
5.1 Within-Sample hedge...................................................................................26
5.2 Out-of-sample hedge....................................................................................28
6. Conclusion..............................................................................................................33

參考文獻

Baillie, R. T., and R. P. DeGennaro (1990), “Stock Returns and Volatility,” Journal of Finance and Quantitative analysis, 25, 203-214.
Baillie, R. T., and R. J. Myers (1991), “Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge,” Journal of Applied Econometrics, 6, 109-124.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,”Journal of Econometrics, 31, 307-327.
Bollerslev, T., R. Chou, and K. Kroner (1992), “ARCH Modeling In Finance: A Review of the Theory and Empirical Evidence,” Journal of Econometrics, 52, 5-59.
Bollerslev, T., and J. Wooldridge (1992), “Quasi maximum likelihood estimation and inference in dynamic models with time varying covariances,” Econometric Reviews, 11, 143-172.
Brenner, R., and K. F. Kroner (1993), “Arbitrage, Cointegration and Testing for Simple Efficiency in Financial Markets,” Unpubl. Manuscript, Univ. of Arizona.
Cassuto, A.E. (1995), “Non-Normal Error Patterns: How to Handle Them,” The Journal of Business Forecasting: Methods and Systems, 14, 15-16.
Chen, S. S., C. F. Lee and K. Shrestha (2003), “Futures Hedge Ratios: A Review,” The Quarterly Review of Economics and Finance, 43, 433-465.
Chou, R. Y. (1988), “Volatility Persistence and Stock Valuations-Some Empirical Evidence Using GARCH,” Journal of Applied Econometrics, 3, 279-294.
Chou, R. Y. (2005), “Forecasting financial volatilities with extreme values: the Conditional AutoRegressive Range (CARR) Model,” Journal of Money Credit and Banking, Vol. 37, No. 3 (June 2005), 561-582.
Chou, R. Y., C. C. Wu and N. Liu (2004), “A comparison and empirical study in forecasting abilities of dynamic volatility models,” Journal of Financial Studies, Vol. 12, No 1, 1-25.
Chou, R. Y., N. Liu and C. C. Wu (2005), “Forecasting Correlation and Covariance with A Range-Based Dynamic Conditional Correlation Model,” working paper.
Edrington, L. H. (1979) “The Hedging Performance of the New Futures Markets.” Journal of Finance, 34, 157-170.
Engle, R. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation,” Econometrica, 50, 987-1008.
Engle, R. (2002) “Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models, Journal of Business and Economic Statistics, 20, pp339-350.
Engle, R. and C. W. J. Granger (1987), “Cointegration and Error Correction: Representation, Estimation and Testing.” Econometrica, 55, 251-276.
Engle, R., and J. Russell (1998) “Autoregressive conditional duration: a new model for irregular spaced transaction data,” Econometrica, 66, 1127-1162.
Fama, E. F. (1965), “The Behavior of Stock Market Prices,” Journal of Business, 38, 34-105.
French, K., W. Schwert, and R. Stambaugh (1987), “Expected stock returns and volatility,” Journal of Financial Economics, 19, 3-29.
Ghosh, A. (1993), “Hedging with Stock Index Futures: Estimation and Forecasting with Error Correction Model,” The Journal of Futures Markets (Oct. 1993), 743-751.
Hill, J., and T. Schneeweis, “The Hedging Effectiveness of Foreign Currency Futures,” Journal of Financial Research, 5 (Spring 1982), 95-104.
Kroner K. F., and J. Sultan (1993), “Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures,” Journal of Finance and Quantitative analysis, Vol. 28, No.4, 535-551.
Lence, S. H. (1995b), “The Economic Value of Minimum-Variance Hedges,” American Journal of Agricultural Economics, 77, 353–364.
Lien, D., and Y. K. Tse (2002), “Some Recent Developments in Futures Hedging,” Journal of Economic Surveys, Vol. 16, No. 3, 357-396.
Lien, D., Y. K. Tse and A. Tsui (2002), “Evaluating the Hedging Performance of the Constant-Correlation GARCH Models,” Applied Financial Econometrics, 12, 791-798.
Ljung, G.M., and G.E.P. Box (1978), “On a Measure of Lack of Fit in Time Series
Models,” Biometrika, 65, 297-303.
Mandelbrot, B. (1963), “The Variation of Certain Speculative Prices,” Journal of Business, 36, 294-419.
McCurdy, T., and I. G. Morgan (1987), “Tests of the martingale hypothesis for foreign currency futures with time varying volatility”, International Journal of Forecasting, 3, 131-148.
Parkinson, M. (1980), “The extreme value method for estimating the variance of the rate of return,” Journal of Business, 53, 61-65.

指導教授

史綱、周雨田
(Gang Shyy、Ray Chou)

審核日期

2005-7-2

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