On fitting and forecasting the log-returns of Bitcoin and Ethereum exchange rates via a new sine-based logistic model and robust regression methods-Reference-Cited by-同舟云学术

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On fitting and forecasting the log-returns of Bitcoin and Ethereum exchange rates via a new sine-based logistic model and robust regression methods

Published:2024-06 Issue: Volume:96 Page:225-236
ISSN:1110-0168
Container-title:Alexandria Engineering Journal
language:en
Short-container-title:Alexandria Engineering Journal

Author:

Zhao Yiming,Salem Sultan,AL-Zaydi Areej M.,Seong Jin-Taek,Alghamdi Fatimah M.,Yusuf M.^ORCID

Publisher

Elsevier BV

Reference32 articles.

1. Compound distributions for financial returns;Afuecheta;PLoS ONE,2020

2. On fitting and forecasting the log-returns of cryptocurrency exchange rates using a new logistic model and machine learning algorithms;Ahmad;AIMS Math.,2022

3. A superior extension for the Lomax distribution with application to Covid-19 infections real data;Alsuhabi;Alex. Eng. J.,2022

4. A new family of heavy tailed distributions with an application to the heavy tailed insurance loss data;Ahmad;Commun. Stat., Simul. Comput.,2022

5. New methods to define heavy-tailed distributions with applications to insurance data;Ahmad;J. Taibah Univ. Sci.,2020

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