Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery
Anna Louise Schröder () and
MPRA Paper from University Library of Munich, Germany
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)
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Published in Statistics and Its Interface 6.4(2013): pp. 449-461
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Working Paper: Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:52379
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