Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery

Anna Louise Schröder and Piotr Fryzlewicz

MPRA Paper from University Library of Munich, Germany

Abstract: Low-frequency financial returns can be modelled as centered around piecewise-constant trend functions which change at certain points in time. We propose a new stochastic time series framework which captures this feature. The main ingredient of our model is a hierarchically-ordered oscillatory basis of simple piecewise-constant functions. It differs from the Fourier-like bases traditionally used in time series analysis in that it is determined by change-points, and hence needs to be estimated from the data before it can be used. The resulting model enables easy simulation and provides interpretable decomposition of nonstationarity into short- and long-term components. The model permits consistent estimation of the multiscale change-point-induced basis via binary segmentation, which results in a variable-span moving-average estimator of the current trend, and allows for short-term forecasting of the average return.

Keywords: Financial time series; Adaptive trend estimation; Change-point detection; Binary segmentation; Unbalanced Haar wavelets; Frequency-domain modelling (search for similar items in EconPapers)
JEL-codes: C1 C13 C22 C51 C58 G17 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Statistics and Its Interface 6.4(2013): pp. 449-461

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/52379/1/MPRA_paper_52379.pdf original version (application/pdf)

Related works:
Working Paper: Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery (2013)
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:pra:mprapa:52379

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().