標題: A clustering time series model for the optimal hedge ratio decision making
作者: Hsu, Yu-Chia
Chen, An-Pin
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Optimal hedge ratio;Financial time series;GHSOM;Cluster analysis
公開日期: 22-八月-2014
摘要: 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 (FUSE 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 naive models. (C) 2014 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neucom.2014.01.026
http://hdl.handle.net/11536/24604
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.01.026
期刊: NEUROCOMPUTING
Volume: 138
Issue: 
起始頁: 358
結束頁: 370
顯示於類別:期刊論文


文件中的檔案:

  1. 000337261700037.pdf