Open AccessDiscrete wavelet based estimator for the Hurst parameter of multivariate fractional Brownian motion
Author(s) -
Munaf Yousif Hmood,
Amjad Hibtallah Hamza
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1879/3/032033
Subject(s) - fractional brownian motion , hurst exponent , estimator , mathematics , wavelet , multivariate statistics , statistics , brownian motion , computer science , artificial intelligence
In this paper, wavelets were used to study the multivariate fractional Brownian motion through the deviations of the random process to find an efficient estimation of Hurst exponent. The results of simulations experiments were shown that the performance of the proposed estimator was efficient. The estimation process was made by taking advantage of the detail coefficients stationarity from the wavelet transform, as the variance of this coefficient showed the power-low behavior. We use two wavelet filters (Haar and db5) to manage minimizing the mean square error of the model.