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Forecasting using locally stationary wavelet processes
SLU, Centre of Biostochastics.
SLU, Centre of Biostochastics.
SLU, Centre of Biostochastics.
2009 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 79, no 9, 1067-1082 p.Article in journal (Refereed) Published
Abstract [en]

Locally stationary wavelet (LSW) processes, built on non-decimated wavelets, can be used to analyse and forecast non-stationary time series. They have been proved useful in the analysis of financial data. In this paper, we first carry out a sensitivity analysis, then propose some practical guidelines for choosing the wavelet bases for these processes. The existing forecasting algorithm is found to be vulnerable to outliers, and a new algorithm is proposed to overcome the weakness. The new algorithm is shown to be stable and outperforms the existing algorithm when applied to real financial data. The volatility forecasting ability of LSW modelling based on our new algorithm is then discussed and shown to be competitive with traditional GARCH models.

Place, publisher, year, edition, pages
2009. Vol. 79, no 9, 1067-1082 p.
Keyword [en]
GARCH, locally stationary wavelet processes, non-decimated wavelets, sensitivity analysis, volatility forecasting
National Category
Probability Theory and Statistics Computer Science
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-63681DOI: 10.1080/00949650802087003ISI: 000270155600001OAI: oai:DiVA.org:umu-63681DiVA: diva2:582306
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2017-12-06Bibliographically approved

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Yu, Jun
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  • asciidoc
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