Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network

TL;DR

This paper introduces a stacked machine learning model combining various algorithms to improve the accuracy of S&P 500 volatility forecasting, addressing a key challenge in market risk management.

Contribution

The paper presents a novel hybrid stacked model using multiple machine learning techniques to enhance volatility prediction accuracy over traditional models.

Findings

The stacked model outperforms traditional volatility forecasting models.

The hybrid approach improves market risk assessment accuracy.

Machine learning techniques effectively capture volatility dynamics.

Abstract

An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.