Model-free time-aggregated predictions for econometric datasets
Methodology
2021-11-05 v3
Abstract
This article explores the existing normalizing and variance-stabilizing (NoVaS) method on predicting squared log-returns of financial data. First, we explore the robustness of the existing NoVaS method for long-term time-aggregated predictions. Then we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term time-aggregated predictions.
Keywords
Cite
@article{arxiv.2101.02273,
title = {Model-free time-aggregated predictions for econometric datasets},
author = {Kejin Wu and Sayar Karmakar},
journal= {arXiv preprint arXiv:2101.02273},
year = {2021}
}