Bivariate Functional Archetypoid Analysis: An Application to Financial Time Series

Jesús Moliner () and Irene Epifanio ()
Additional contact information
Jesús Moliner: Universitat Jaume I, Department of Mathematics
Irene Epifanio: Universitat Jaume I, Department of Mathematics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 473-476 from Springer

Abstract: Abstract Archetype Analysis (AA) is a statistical technique that describes individuals of a sample as a convex combination of certain number of elements called archetypes, which in turn, are convex combinations of the individuals in the sample. For it’s part, Archetypoid Analysis (ADA) tries to represent each individual as a convex combination of a certain number of extreme subjects called archetypoids. It is possible to extend these techniques to functional data. This work presents an application of Functional Archetypoids Analysis (FADA) to financial time series. At the best of our knowledge, this is the first time FADA is applied in this field. The starting time series consists of daily equity prices of the S&P 500 stocks. From it, measures of volatility and profitability are generated in order to characterize listed companies. These variables are converted into functional data through a Fourier basis expansion function and bivariate FADA is applied. By representing subjects through extreme cases, this analysis facilitates the understanding of both the composition and the relationships between listed companies. Finally, a cluster methodology based on a similarity parameter is presented. Therefore, the suitability of this technique for this kind of time series is shown, as well as the robustness of the conclusions drawn.

Keywords: Functional data analysis; Archetypal analysis; Stock time series (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_84

Ordering information: This item can be ordered from
http://www.springer.com/9783319898247

DOI: 10.1007/978-3-319-89824-7_84

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().