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

Author

Listed:
  • Youyuan Wu
  • Wei Chong Choo
  • Bolaji Tunde Matemilola
  • Wan Cheong Kin
  • Zhe Zhang

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).

Suggested Citation

  • Youyuan Wu & Wei Chong Choo & Bolaji Tunde Matemilola & Wan Cheong Kin & Zhe Zhang, 2024. "Transformative advances in volatility prediction: unveiling an innovative model selection method using exponentially weighted information criteria," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 18(6), pages 569-590.
    • Handle: RePEc:ids:ijbsre:v:18:y:2024:i:6:p:569-590
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