标题: 以计算智慧为基础之新的避险比例决定方法
A Novel Approach for Hedge Ratio Decision Based on Computational Intelligence
作者: 许育嘉
Hsu, Yu-Chia
陈安斌
Chen, An-Pin
资讯管理研究所
关键字: 最适避险比例;财务时间序列;成长阶层式自组织映射图;集群分析;optimal hedge ratio;financial time series;GHSOM;cluster analysis
公开日期: 2009
摘要: 本研究提出了一个整合计算智慧与统计方法学的最适避险比例决定方法,用来改善不同避险区间下最小变异避险比例之预测准确度。透过衡量金融市场现货及期货商品报酬时间序列之变异数、共变数、价差及其他们的一阶、二阶变量,市场波动的动态行为可以被撷取出来,之后以增长阶层式自我组织图进行阶层式的分群。经过分群,这些位在相同集群里具有相似行为的时间序列资料,经过给予不同的权重进行重新取样后,会被搜集起来用来取代原先估算最适避险比例的资料样本。我们将这个方法运用在台湾加权股价指数、标准普尔500指数、金融时报100指数、以及日经255指数之避险实证研究上,对于避险区间之长短与避险效果的关系进行研究。实验结果显示,这个方法所估算之避险比例,在中、长期避险区间下可以显着地得到优于传统最小平方法模型及天真避险模型之表现,决定出各种避险期间下之最适避险比例。
In this study, a novel procedure combining computational intelligence and statistical methodologies is proposed to improve the accuracy of minimum -variance optimal hedge ratio (OHR) estimation over various hedging horizons. The time series of financial asset returns are clustered hierarchically using a growing hierarchical self-organizing map (GHSOM) based on the dynamic behaviors of market fluctuation extracted by measurement of variances, covariance, price spread, and their first and second differences. Instead of using original observations, observations with similar patterns in the same cluster and weighted by a resample process are collected to estimate the OHR. Four stock market indexes and related futures contracts, including Taiwan Weighted Index (TWI), Standard & Poor's 500 Index (S&P 500), Financial Times Stock Exchange 100 Index (FTSE 100), and NIKKEI 255 Index, are adopted in empirical experiments to investigate the correlation between hedging horizon and performance. Results of the experiments demonstrate that the proposed approach can significantly improve OHR decisions for mid-term and long-term hedging compared with traditional ordinary least squares and naïve models.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079434808
http://hdl.handle.net/11536/40875
显示于类别:Thesis


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