COMPARATIVE ANALYSIS OF HAAR AND DAUBECHIES STATISTICAL WAVELETS FOR FINANCIAL TIME SERIES ANALYSIS

Abstract

This study presents a comparative analysis of Haar and Daubechies statistical wavelets for analyzing financial time series data. We examine the application of these wavelets in extracting insights from financial data, including trend, fluctuations, energy distribution, and complexity. Our results show that both Haar and Daubechies wavelets can effectively capture the characteristics of financial data, but they differ in their methodological approaches and estimates. The Haar wavelet analysis reveals a gradual increase in the trend of the S&P GREEN BND SELECT INDEX - PRICE INDEX data, with a moderate level of complexity or uncertainty. The energy distribution of the Haar wavelet coefficients shows that the majority of the energy is concentrated in the low-frequency components. Our analysis demonstrates the effectiveness of statistical wavelets in extracting insights from financial data, which can be used to inform investment decisions, risk management strategies, and other financial applications.

Keywords: Statistical Wavelets, Haar Wavelet, Daubechies Wavelet, Financial Time Series Analysis, Signal Processing.