Title

Using Arc Length to Cluster Financial Time Series According to Risk

Document Type

Article

Publication Date

2015

Publication Title

Communications in Statistics: Case Studies, Data Analysis and Applications

DOI

10.1080/23737484.2016.1206456

ISSN

2373-7484

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.

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