APPLICATION OF WAVELET REGRESSION WITH LOCAL LINEAR QUANTILE REGRESSION IN FINANCIAL TIME SERIES FORECASTING.

Ma Ghazal,Gh Alshreef,W Alabeid

doi:10.21474/ijar01/4534

Abstract

21Jun 2017 APPLICATION OF WAVELET REGRESSION WITH LOCAL LINEAR QUANTILE REGRESSION IN FINANCIAL TIME SERIES FORECASTING. M. A. Ghazal , W. Alabeid and Gh. Alshreef. Department of Mathematics, Faculty of Science, Damietta University, Damietta, Egypt.

Highlights

The classical wavelet methods suffering from boundary problems caused by the application of the wavelet transformations to a finite
Wavelet regression is a new non parametric method characterized by the ability to detect unusual appearances, which might be observed in noisy data
Collapse points, and discontinuities can be taken into consideration by wavelet methods, but when performing wavelet regression it is usual to consider some boundary assumptions, such as periodicity or symmetry

Summary

Introduction

The classical wavelet methods suffering from boundary problems caused by the application of the wavelet transformations to a finite. Collapse points, and discontinuities can be taken into consideration by wavelet methods, but when performing wavelet regression it is usual to consider some boundary assumptions, such as periodicity or symmetry Such assumptions may not always be logical to treat this problem, it is suggested by Oh, Naveau, and Lee (2001) to split as the sum of a set of wavelet basis functions, , plus a low-order polynomial,. Oh and Lee (2005) proposed a method for correcting the boundary bias, they join wavelet shrinkage with local polynomial regression, where the latter regression technique known of a perfect boundary properties. Simulation results from both the univariate and bivariate settings provide strong evidence that the proposed method is very successful in terms of rectify boundary bias. The aims of this research are to study these new estimators that are combinations of local linear quantile regression

Objectives

Methods