Schröder, Anna Louise and Fryzlewicz, Piotr (2013): Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery. Published in: Statistics and Its Interface , Vol. 4, No. 6 (2013): pp. 449-461.
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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.
Item Type: | MPRA Paper |
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Original Title: | Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery |
English Title: | Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery |
Language: | English |
Keywords: | Financial time series, Adaptive trend estimation, Change-point detection, Binary segmentation, Unbalanced Haar wavelets, Frequency-domain modelling |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 52379 |
Depositing User: | Ms Anna Louise Schröder |
Date Deposited: | 26 Dec 2013 21:21 |
Last Modified: | 27 Sep 2019 22:48 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/52379 |