Risk reduction using wavelets for denoising principal‐components regression models

Abstract

Purpose

In this paper, it is set out a hybrid data analysis method based on the combination of wavelet techniques and principal‐components regression (PCR). The purpose of this paper is to study the dynamics of the stock returns within the French stock market.

Design/methodology/approach

Wavelet‐based thresholding techniques are applied to the stock price series in order to obtain a set of explanatory variables that are practically noise‐free. The PCR is then carried out on the new set of regressors.

Findings

The empirical results show that the suggested method allows extraction and interpretation of the factors that influence the stock price changes. Moreover, the wavelet‐PCR improves the explanatory power of the regression model as well as its forecasting quality.

Practical implications

The proposed technique offers investors a better understanding of the mechanisms that explain the stock return dynamics as it removes the noise that affects financial time series.

Originality/value

The paper uses a new denoising framework for financial assets. The paper thinks that this framework might be of great value for academics as well as for financial investors.

Citation

Ben Ammou, S., Kacem, Z. and Haouas, N. (2010), "Risk reduction using wavelets for denoising principal‐components regression models", Journal of Risk Finance, Vol. 11 No. 2, pp. 180-203. https://doi.org/10.1108/15265941011025198