The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models

TL;DR

This paper compares traditional GARCH, LSTM, and hybrid models including VIX for forecasting S&P 500 volatility, demonstrating that hybrid models with VIX provide the most accurate predictions for better risk management.

Contribution

It introduces a hybrid LSTM-GARCH model incorporating VIX to improve volatility forecasting accuracy over traditional methods.

Findings

Hybrid LSTM-GARCH models outperform GARCH alone.

Including VIX enhances forecast accuracy.

Machine learning models significantly improve volatility predictions.

Abstract

Predicting the S&P 500 index volatility is crucial for investors and financial analysts as it helps assess market risk and make informed investment decisions. Volatility represents the level of uncertainty or risk related to the size of changes in a security's value, making it an essential indicator for financial planning. This study explores four methods to improve the accuracy of volatility forecasts for the S&P 500: the established GARCH model, known for capturing historical volatility patterns; an LSTM network that utilizes past volatility and log returns; a hybrid LSTM-GARCH model that combines the strengths of both approaches; and an advanced version of the hybrid model that also factors in the VIX index to gauge market sentiment. This analysis is based on a daily dataset that includes S&P 500 and VIX index data, covering the period from January 3, 2000, to December 21, 2023. Through rigorous testing and comparison, we found that machine learning approaches, particularly the hybrid LSTM models, significantly outperform the traditional GARCH model. Including the VIX index in the hybrid model further enhances its forecasting ability by incorporating real-time market sentiment. The results of this study offer valuable insights for achieving more accurate volatility predictions, enabling better risk management and strategic investment decisions in the volatile environment of the S&P 500.