Xie, Yingfu
- Department of Forest Economics, Swedish University of Agricultural Sciences
Abstract
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.
Keywords
GARCH; locally stationary wavelet processes; non-decimated wavelets; sensitivity analysis; volatility forecasting
Published in
Journal of Statistical Computation and Simulation
2009, volume: 79, number: 9, pages: 1067-1082
Publisher: Taylor & Francis
SLU Authors
Yu, Jun
- Department of Forest Economics, Swedish University of Agricultural Sciences
- Department of Forest Economics, Swedish University of Agricultural Sciences
Ranneby, Bo
- Department of Forest Economics, Swedish University of Agricultural Sciences
- Department of Forest Economics, Swedish University of Agricultural Sciences
UKÄ Subject classification
Probability Theory and Statistics
Publication identifier
- DOI: https://doi.org/10.1080/00949650802087003
Permanent link to this page (URI)
https://res.slu.se/id/publ/26284