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[BKH10] Risk reduction using wavelets for denoising principal-components regression models
Revue Internationale avec comité de lecture : Journal The Journal of Risk Finance, vol. 11(2), pp. 180-203, 2010
motcle:
Résumé:
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.
Keywords Stock markets, Stock returns, Data analysis, Stock exchanges, Regression analysis, France
Commentaires:
note
BibTeX
| @article { | |||
| BKH10, | |||
| title | = | "{Risk reduction using wavelets for denoising principal-components regression models}", | |
| author | = | "S. Benammou and Z. Kacem and N. Haouas", | |
| journal | = | "The Journal of Risk Finance", | |
| year | = | 2010, | |
| volume | = | 11, | |
| number | = | 2, | |
| pages | = | "180-203", | |
| note | = | "{note}", | |
| } | |||
