Non-linear Time Series and Artificial Neural Networks of Red Hat Volatility

TL;DR

This paper enhances understanding of Red Hat stock volatility by applying advanced machine learning and econometric models, including neural networks and smooth transition regression, to empirical financial data.

Contribution

It introduces novel application of neural networks and smooth transition models to analyze stock volatility, extending prior empirical research.

Findings

Neural networks effectively model volatility patterns.

Smooth transition regression captures regime changes.

Enhanced predictive accuracy over traditional methods.

Abstract

We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.