A New Model-free Prediction Method: GA-NoVaS

TL;DR

This paper introduces GA-NoVaS, a novel model-free volatility prediction method based on the NoVaS approach and GARCH structure, demonstrating improved accuracy over existing methods especially in volatile and small-sample scenarios.

Contribution

The paper develops a new NoVaS-type method that exploits GARCH model structure, enhancing robustness and prediction accuracy in volatile financial data.

Findings

GA-NoVaS outperforms current NoVaS methods in volatile data forecasting

The method is more stable with small sample sizes

It offers a new avenue for exploring NoVaS structures

Abstract

Volatility forecasting plays an important role in the financial econometrics. Previous works in this regime are mainly based on applying various GARCH-type models. However, it is hard for people to choose a specific GARCH model which works for general cases and such traditional methods are unstable for dealing with high-volatile period or using small sample size. The newly proposed normalizing and variance stabilizing (NoVaS) method is a more robust and accurate prediction technique. This Model-free method is built by taking advantage of an inverse transformation which is based on the ARCH model. Inspired by the historic development of the ARCH to GARCH model, we propose a novel NoVaS-type method which exploits the GARCH model structure. By performing extensive data analysis, we find our model has better time-aggregated prediction performance than the current state-of-the-art NoVaS method on forecasting short and volatile data. The victory of our new method corroborates that and also opens up avenues where one can explore other NoVaS structures to improve on the existing ones or solve specific prediction problems.