Transformative advances in volatility prediction: unveiling an innovative model selection method using exponentially weighted information criteria

Youyuan Wu, Wei Chong Choo, Bolaji Tunde Matemilola, Wan Cheong Kin and Zhe Zhang

International Journal of Business and Systems Research, 2024, vol. 18, issue 6, 569-590

Abstract: Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

Keywords: exponential weighted information criteria; volatility forecasting; decision making. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=142085 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbsre:v:18:y:2024:i:6:p:569-590

Access Statistics for this article

More articles in International Journal of Business and Systems Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().