| 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. |