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 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.


Multivariate fractional Brownian motion Hurst exponent Continuous wavelet transform Meyer’s wavelets Short memory Long memory Gaussian noise

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How to Cite
Hmood, M. Y., & Hibatallah, A. (2022). Continuous wavelet estimation for multivariate fractional Brownian motion. Pakistan Journal of Statistics and Operation Research, 18(3), 633-641.


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