动态避险模型之建立—以远期外汇与一篮子货币避险策略为例
The Dynamic Hedging Model —A Combination of Delivery Forward and Currency Basket Hedge

作者: 余尚武 , 黄泓玮 :;

关键词: 动态避险一篮子货币避险支撑向量机Dynamic Hedge Currency Basket Hedge Smooth Vector Regression

摘要: 本研究建构一动态避险模型,由传统避险工具与一篮子货币避险策略所构成。由于传统避险工具的好处在于风险的完全规避,而一篮子货币避险之主要目的则在于降低避险成本,因此本研究之动态避险模型,系以一篮子货币避险策略为主,于每期期初比较两种策略之避险成本,择其低者作为该期之避险策略。另外,为建立更为精确的一篮子货币避险部位,本研究亦导入人工智能工具于模型中,试图利用人工智能于汇率预测上的优秀表现,优化篮子内货币之权重,进而达到更佳的避险绩效。经实证,本研究之动态避险模型,确能显着地优于仅执行单一传统避险工具或者一篮子货币避险策略。其中,以两年作为权重估计期间并纳入预测技术之模型,于实验期间内表现最佳,是为本研究所推荐使用之模型。

Abstract: This paper proposes a “dynamic hedging model”, which adjusts the hedging strategies by the time, to increase the hedging performance. The dynamic hedging model is combined with the traditional hedging strategy (e.g. Delivery Forward) and the basket currency hedging. The traditional hedging strategy covers the whole risk, in the mean time, the basket currency reduce the hedging cost. Therefore, we establish the position of basket currency first, and execute this strategy when its hedging cost is lower than the traditional hedging strategy; otherwise, we execute the traditional hedging strategy. Besides, for establishing a more precise position of basket currency, we also use artificial intelligence to forecast the exchange rate, which is expected to estimate the currency weight in a basket more precisely. Empirically, the dynamic hedging model we propose performs much better than either the traditional hedging strategy or basket currency hedging strategy. In addition, due to the way of using two-year estimation period and adding forecast technology to correct the estimation got the best performance, we recommend this model to be a reference of a company’s hedging behavior.

文章引用: 余尚武 , 黄泓玮 (2012) 动态避险模型之建立—以远期外汇与一篮子货币避险策略为例。 管理科学与工程, 1, 9-18. doi: 10.12677/MSE.2012.12002

参考文献

[1] 叶慧心. 国寿海外投资避险成本减半[N]. 经济日报, 2006-2-14.

[2] 陈美君. 寿险一篮子货币避险冲三成[N]. 工商时报, 2007-10-31。

[3] 张宗载. 一篮子货币避险[D]. 台湾大学财务金融研究学所, 2005.

[4] P.-F. Pai, C.-S. Lin, W.-C. Hong and C.-T. Chen. A hybrid support vector machine regression for exchange rate prediction. Information and Management Sciences, 2006, 17(2): 19-32.

[5] H. Ince, T. B. Trafalis. A hybrid model for exchange rate prediction. Decision Support Systems, 2006, 42(2): 1054-1062.

[6] 吴志远. 衍生性金融商品的应用[URL], 2006. http://www.centerforpbbefr.rutgers.edu/2006/ppt%202006/ps14-wu.ppt

[7] 林萍珍. 投资分析: 含Matlab应用、类神经网络与遗传算法模型[M]. 台北: 新陆书局, 2008.

[8] Y.-J. Lee, W.-F. Hsieh and C.-M. Huang. ε-SSVR: A smooth support vector machine for ε-insensitive regression. IEEE Transactions on Knowledge and Data Engineering, 2006, 17: 1041-1347.

[9] 黄建铭. 支撑向量机的自动參數选择[D]. 台湾科技大学资讯工程系, 2005.

[10] C. N. W. Tan. Applying artifi-cial neural networks in finance: A foreign exchange market trading system example with transaction costs. PhD Conference in Economics and Finance, Perth, 1995: 79-117.

[11] 许诚洲. 财务工程: 衍生性商品交易理论、实务与个案研究[M]. 台北: 双叶书廊, 2006.

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