# Model-free time-aggregated predictions for econometric datasets

arXiv: https://arxiv.org/abs/2101.02273 · HTML: https://mlsanity.com/paper/2101.02273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Deep Limit Model-free Prediction in Regression](https://mlsanity.com/paper/2408.09532/) (2024)

## Abstract (arXiv)

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.