A wavelet-based approach to the analysis and modelling of financial time series exhibiting strong long-range dependence: the case of Southeast Europe

DOI10.1080/02664763.2015.1077370OpenAlexW2511456326MaRDI QIDQ5138025

Boryana C. Bogdanova, Ivan Ganchev Ivanov

Publication date: 3 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2015.1077370

zbMATH Keywords

wavelet transformlong-range dependencemultiscale autoregressive (MAR) modelsoutheast European stock markets

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series
• Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
• Long memory processes and fractional integration in econometrics
• Testing for long-range dependence in world stock markets
• Wavelet-based prediction of oil prices
• Estimators of long-memory: Fourier versus wavelets
• THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
• A theory for multiresolution signal decomposition: the wavelet representation
• Modelling and forecasting by wavelets, and the application to exchange rates
• Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Long-Term Memory in Stock Market Prices
• Wavelet analysis of long-range-dependent traffic
• The contribution of wavelets to the analysis of economic and financial data
• Fractional Brownian Motions, Fractional Noises and Applications