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  • 學位論文

以copula-based GARCH模型探討原油價格與匯率共移性的經濟價值

The economic value of co-movement between oil price and exchange rate using copula-based GARCH models

指導教授 : 鍾惠民 吳志強

摘要


由於美元為國際原油交易的主要貨幣。近幾年來,美元的大幅貶值導致了原油價格的飆升。本研究採用關連結構GARCH模型試圖更有彈性的去探討原油與匯率之間的依賴結構。而實證結果也表示,對稱的關連結構GARCH模型具有較好的解釋能力。此外我們使用動態資產配置策略去評估模型的經濟價值及其實際的效率性。在樣本外的預測中,使用Frank關連結構GARCH模型要比其他靜態及動態模型具有較高的經濟價值。而較為保守的投資者也願意付出較高的費用將靜態的投資策略轉為關連結構GARCH模型的動態策略。

關鍵字

原油 匯率 共移性 關連結構 經濟價值

並列摘要


The US dollar is used as the major currency of international crude oil trading, and thus the substantial depreciation of US dollar results in the soar of crude oil prices in recent years. In addition, the oil and exchange rate returns have been shown to be skew and leptokurtic and exhibit asymmetric or tail dependence structure. Therefore, this study uses the dynamic copula-based GARCH models to flexibly explore the dependence structure between the oil and US dollar exchange rate, and the empirical results demonstrate that the GARCH model with symmetric copulas has better explanatory ability. Furthermore, an asset allocation strategy is implemented to evaluate economic value and confirm the efficiency of the copula-based GARCH models. In terms of out-of-sample forecasting performance, a dynamic strategy based on the GARCH model with Frank copula exhibits larger economic benefits than static and other dynamic strategies. An investor with a higher risk aversion attitude also generates higher fee for switching from a static strategy to a dynamic strategy based on copula-based GARCH models.

並列關鍵字

Oil Exchange rate Co-movement Copula Economic value

參考文獻


[1] Akram, Q.F., 2004. Oil prices and exchange rates: Norwegian evidence, Econometrics Journal, 7, 476-504.
[2] Bartram, S.M., S.J Taylor, Y.H Wang, 2007. The Euro and European financial market integration. Journal of Banking and Finance, 31, 1461-1481.
[3] Cifarelli, G., G. Paladino, 2010. Oil Price Dynamics and Speculation, A Multivariate Financial Approach. Energy Economics, 32, 363-372.
[4] Engle, R.F., and G. Lee, 1999, A Long-run and Short-run Component Model of Stock Return Volatility, in Engle R.F. and White H. (eds.), Cointegration, Causality and Forecasting: A Festschrift in Honour of Clive W. J. Granger, Oxford, Oxford University Press, 475-497.
[5] Fleming, J., C. Kirby, B. Ostdiek, 2001. The economic value of volatility timing. Journal of Finance, 56, 329-352.

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