匯率的劇烈波動常導致投資人承受巨額損失，因而外匯期貨已成為避險的金融商品之一。本文以芝加哥商業交易所1987年2月2日至2017年1月31日的美元兌日圓、英鎊、澳幣、加幣及瑞士法郎期貨為研究對象，利用移動視窗的架構，探討普通最小平方(OLS)模型、動態條件相關自我迴歸異質變異數(DCC-GARCH)模型與不對稱動態條件相關自我迴歸異質變異數(ADCC-GARCH)模型的動態避險績效。在考量最小變異數避險組合下，除瑞士法郎的OLS模型外，其他模型的動態避險績效皆顯著的大於靜態避險績效。此外，所有外匯商品在DCC-GARCH(1,1)與ADCC-GARCH(1,1)模型皆有顯著的動態避險績效，而兩種模型的避險績效並無顯著差異。本文的研究結論可提供給投資人參考。

Fluctuations in exchange rates often cause huge losses to investors, and thus  foreign exchange rate futures become one of the hedge financial products. This study examined USD/JPY, USD/GBP, USD/AUD, USD/CAD and USD/CHF spots and futures in Chicago Mercantile Exchange (CME) from February 2, 1987 to January 31, 2017. The window-rolling framework is used to investigate dynamic hedging effectiveness of OLS, DCC-GARCH and ADCC-GARCH model. Based on the minimum variance hedging portfolio, the dynamic hedging effectiveness is significantly better than static hedging effectiveness in the researched models other than USD/CHF of OLS model. Although there exists significant dynamic hedging effectiveness of DCC-GARCH(1,1) and ADCC-GARCH(1,1) model for the researched foreign exchange rate products, there is no significant difference in hedging effectiveness of these two models. The findings in this study can be used as a reference for investors.

目錄
中文摘要………………………………...……………………………………...I
ABSTRACT…………………………………………………….…..………….II
目錄………………………………………..………………………….………..III
表目錄……………………………………………………………………………IV
圖目錄………..…………………………………………..……….…………….V
第一章　緒論…………………………………………………………………….1
	 1.1  研究背景與動機.………..…..…………………………………………...1
     1.2  研究目的….…………………..………..…………………………………...4
     1.3  研究範圍與限制.……………..…………………………………..………...5
     1.4  研究架構….…………………..…………………………………………….5
第二章　資料與方法…………………………………………………………...7
     2.1  樣本資料與來源.……………..………………..…………………………...7
     2.2  實證模型….…………………..…………………………………..………...8
     2.3  最小變異數避險組合的避險績效……..…………………………………11
第三章　實證結果分析……………………………………………………….13
     3.1  基本敘述統計量……..……………………………………………………13
     3.2  單根檢定分析……………..……………………………..………………..17
     3.3  模型參數估計……………..……………………………………………....20
     3.4  避險組合的避險比率……..…………………..…………………………..24
     3.5  靜態與動態避險績效的比較……..…………………………..…………..25
第四章　結論與建議………………………………………………………….30
     4.1  結論…….…..……………..…………………………………………….....30
     4.2  建議……….……………..…..…………………………………………….31
參考文獻………...………….………………………….........…...…………. 32

表目錄
表3-1	研究變數的基本敘述統計量分析………………………………………….16
表3-2	日價格序列ADF、PP、KPSS檢定...………………………………….18
表3-3	日報酬序列ADF、PP、KPSS檢定...………………………………….19
表3-4	殘差自我相關檢定…………....…………………………………………….21
表3-5	DCC-GARCH(1,1)模型參數估計值(全部樣本).……...…….………….22
表3-6	ADCC-GARCH(1,1)模型參數估計值(全部樣本).….…...………….….23
表3-7	波動性持續性檢定(全部樣本).…………………………………………….24
表3-8	最適避險比率……………………………………………………………….25
表3-9	靜態與動態避險績效的統計……………………………………………….26
表3-10	靜態與動態的避險績效比較……………………………………………….26
表3-11	靜態避險績效的比較…………………………………………………………….28
表3-12	動態避險績效的比較…………………………………………………………….29 

圖目錄
圖1-1	研究流程圖…………………………………………………………………...6
圖2-1	移動視窗架構示意圖………………………………………………………...8
圖3-1	美元兌日圓現貨與期貨日價格及日報酬時間走勢圖…………………….13
圖3-2	美元兌英鎊現貨與期貨日價格及日報酬時間走勢圖…………………….14
圖3-3	美元兌澳幣現貨與期貨日價格及日報酬時間走勢圖…………………….14
圖3-4	美元兌加幣現貨與期貨日價格及日報酬時間走勢圖…………………….15
圖3-5	美元兌瑞士法郎現貨與期貨日價格及日報酬時間走勢圖……………….15

一、	中文部分
1.	王健聰(2011)。考慮匯率風險與高狹峰分配之最小變異數避險比率
         估計-新加坡摩根台股指數期貨之驗證。證券市場發展季刊,23(4),
         111-142。
2.	巫春洲、劉炳麟、楊奕農(2009)。農産品期貨動態避險策略的評
         價。農業與經濟, (42), 39-62。
3.	李命志、邱哲修、黃景明、陳君達(2003)。台灣股價指數期貨最
         適避險策略之研究。企業管理學報,(58),85-104。
4.	李勝彥(2016)。英鎊的興衰與展望。亞洲金融季報，秋季號，頁
         4-8。
5.	康信鴻與繆俊華(1998)。外匯期貨最適避險比例之實證研究。管
         理學報,15(3),419-453.
6.	張巧宜、賴靖宜、莊益源(2013)。期貨最適組合避險模型: 新興市
         場為例。管理與系統,20(2),355-383.
7.	黃仁德和林進煌(2007)。亞洲金融危機與國際貨幣基金的角色。
         問題與研究，第46卷第一期，頁101-145。
8.	楊明晶、董澍琦、劉孟宜(2010)。外匯風險管理-外匯期貨及遠期
         外匯契約之避險效益分析。證券市場發展季刊, 22(3), 105-136. 

二、	英文部分
1.	Acatrinei, M., Gorun, A., and Marcu, N. (2013). A DCC-GARCH model to estimate. Romanian Journal of Economic Forecasting, 16(1), 136-148.
2.	Basher, S. A., and Sadorsky, P. (2016). Hedging emerging market stock prices with Oil, Gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235-247.
3.	Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
4.	Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72, 498-505.
5.	Bonga-Bonga, L., and Nleya, L. (2016). Assessing portfolio market risk in the BRICS economies: Use of multivariate GARCH models. MPRA Paper, No. 75809.
6.	Cappiello, L., Engle, R. F., and Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537-572.
7.	Chang, C. L., Gonzalez-Serrano, L., and Jimenez-Martin, J. A. (2013). Currency hedging strategies using dynamic multivariate GARCH. Mathematics and Computers in Simulation, 94, 164-182.
8.	Chang, C. L., McAleer, M., and Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912-923.
9.	Chang, K. L. (2011). The optimal value-at-risk hedging strategy under bivariate regime switching ARCH framework. Applied Economics, 43(21), 2627-2640.
10.	Chen, P., Zhuo, Z., and Liu, J. (2016). Estimation and comparative of dynamic optimal hedge ratios of China gold futures based on ECM-GARCH. International Journal of Economics and Finance, 8(3), 236.
11.	Chen, S. W., and Shen, C. H. (2004). GARCH, jumps and permanent and transitory components of volatility: The case of the Taiwan exchange rate. Mathematics and Computers in Simulation, 67(3), 201-216.
12.	Choudhry, T. (2003). Short-run deviations and optimal hedge ratio: Evidence from stock futures. Journal of Multinational Financial Management, 13(2), 171-192.
13.	Choudhry, T. (2004). The hedging effectiveness of constant and time-varying hedge ratios using three pacific basin stock futures. International Review of Economics and Finance, 13(4), 371-385.
14.	Chuang, C. C., Wang, Y. H., Yeh, T. J., and Chuang, S. L. (2015). Hedging effectiveness of the hedged portfolio: The Expected utility maximization sbject to the value-at-risk approach. Applied Economics, 47(20), 2040-2052.
15.	Cremers, J. H., Kritzman, M., and Page, S. (2005). Optimal hedge fund allocations. Journal of Portfolio Management, 31(3), 70-81.
16.	Dickey, D. A., and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of Econometric Society, 49(4), 1057-1072.
17.	Ederington, L. H. (1979). The hedging performance of the new futures markets. Journal of Finance, 34(1), 157-170.
18.	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.
19.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of Econometric Society, 50(4), 987-1007.
20.	Fama, E. F. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34-105.
21.	Ghosh, A. (1993). Hedging with stock index futures: Estimation and forecasting with error correction model. Journal of Futures Markets, 13, 743-743.
22.	Hammoudeh, S., and Aleisa, E. (2004). Dynamic relationships among GCC stock markets and NYMEX oil futures. Contemporary Economic Policy, 22(2), 250-269. 
23.	Hansen, P., and Lunde, A. (2011). Forecasting volatility using high frequency data. In Michael P. Clements and David F. Hendry. (Eds.), Oxford Handbook of Economic Forecasting, 525-556, United Kingdom, Oxford: Oxford University Press.
24.	Henze, N., and Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics-Theory and Methods, 19(10), 3595-3617.
25.	Johnson, L. L. (1960). The theory of hedging and speculation in commodity futures. Review of Economic Studies, 27(3), 139-151.
26.	Kavussanos, M. G., and Visvikis, I. D. (2008). Hedging effectiveness of the Athens stock index futures contracts. European Journal of Finance, 14(3), 243-270.
27.	Kroner, K. F., and Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535-551.
28.	Ku, Y. H. H., Chen, H. C., and Chen, K. H. (2007). On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios. Applied Economics Letters, 14(7), 503-509.
29.	Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root. Journal of Econometrics, 54(1-3), 159-178.
30.	Lanza, A., Manera, M., and McAleer, M. (2006). Modeling dynamic conditional correlations in WTI oil forward and futures returns. Finance Research Letters, 3(2), 114-132.
31.	Lee, C. H., Doong, S. C., and Chou, P. I. (2011). Dynamic correlation between stock prices and exchange rates. Applied Financial Economics, 21(11), 789-800.
32.	Lien, D., and Tse, Y. K. (2002). Some recent developments in futures hedging. Journal of Economic Surveys, 16(3), 357-396.
33.	Ljung, G. M., and Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
34.	Mardia, K. V. (1980). Tests of univariate and multivariate normality. In P. R. Krishnaiah. (Eds.), Handbook of Statistics, 1, 279–320, North Holland, Amstredam.
35.	Morgan, I. G. (1976). Stock prices and heteroscedasticity. Journal of Business, 49(4), 496-508.
36.	Phillips, P. C., and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
37.	Teo, M., 2009, The geography of hedge funds, Review of Financial Studies, 22, 9, 3531-3561.
38.	Tong, W. H. (1996). An examination of dynamic hedging. Journal of International Money and Finance, 15(1), 19-35.
39.	Van Der Weide, R. (2002). GO‐GARCH: A multivariate generalized orthogonal GARCH model. Journal of Applied Econometrics, 17(5), 549-564.
40.	Yun, W. C., and Kim, H. J. (2010). Hedging strategy for crude oil trading and the factors influencing hedging effectiveness. Energy Policy, 38(5), 2404-2408.
41.	Zanotti, G., Gabbi, G., and Geranio, M. (2010). Hedging with futures: Efficacy of GARCH correlation models to European electricity markets. Journal of International Financial Markets, Institutions and Money, 20(2), 135-148.