Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the m...

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Bibliographic Details
Main Authors: Sneha S. Bagalkot, Dinesha H. A, Nagaraj Naik
Format: Article
Language:English
Published: PeerJ Inc. 2024-01-01
Series:PeerJ Computer Science
Subjects:
ARIMA
GARCH
GWO
Stock price
Parameter selection
Online Access:https://peerj.com/articles/cs-1735.pdf

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