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Article
Using Arc Length to Cluster Financial Time Series According to Risk
Communications in Statistics: Case Studies, Data Analysis and Applications
  • Tharanga D. Wickramarachchi, Georgia Southern University
  • Ferebee Tunno, Arkansas State University
Document Type
Article
Publication Date
1-1-2015
DOI
10.1080/23737484.2016.1206456
Disciplines
Abstract

This article investigates how arc length can be used to partition financial time series according to variability (risk). This technique is predicated on the idea that arc length is an index of volatility, and thus the end result is that safer stocks can be sorted from more risky ones. Performance of arc length is compared with squared returns and absolute returns, two commonly used measures for quantifying the variability of prices. An application involving 30 popular stocks is presented using Maharaj, k-means ++, and correlation-based clustering techniques.

Citation Information
Tharanga D. Wickramarachchi and Ferebee Tunno. "Using Arc Length to Cluster Financial Time Series According to Risk" Communications in Statistics: Case Studies, Data Analysis and Applications Vol. 1 Iss. 4 (2015) p. 217 - 225 ISSN: 2373-7484
Available at: http://works.bepress.com/tharanga_wichramarchchi/13/