Clustering time series data by SOM for the optimal hedge ratio estimation

Yu Chia Hsu*, An-Pin Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The fat-tailed and leptokurtic properties observed in most financial asset return series would cause the inaccuracy of hedge ratio estimation because most traditional statistics approaches are based on the assumption of normal distribution. In this study, a novel approach is proposed using self-organizing map (SOM, also called Kohonen 's Self-Organizing Feature Map) for time series data clustering and similar pattern recognition to improve the optimal hedge ratio (OHR) estimation. Five SOM-based models (considering the weight for averaging and the interval for data sampling) and two traditional models (ordinary least square method and naive hedge) were compared in Taiwan stock market hedging. The experiment demonstrates the feasibility of applying SOM, and the empirical results show that SOM approach provides a useful alternative to the OHR estimation.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Pages1164-1169
Number of pages6
DOIs
StatePublished - 29 Dec 2008
Event3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 - Busan, Korea, Republic of
Duration: 11 Nov 200813 Nov 2008

Publication series

NameProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Volume2

Conference

Conference3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
CountryKorea, Republic of
CityBusan
Period11/11/0813/11/08

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    Hsu, Y. C., & Chen, A-P. (2008). Clustering time series data by SOM for the optimal hedge ratio estimation. In Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 (pp. 1164-1169). [4682405] (Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008; Vol. 2). https://doi.org/10.1109/ICCIT.2008.408