A Comparative Analysis of Traditional GARCH Models and Modern Machine Learning Approaches
Keywords:
Volatility forecasting, GARCH models, Machine learning, Time series, Data leakageAbstract
In this work, I will provide a general comparison of the conventional financial economic approaches and the contemporary machine learning techniques in forecasting stock market volatility. On a sample of 505 S&P 500 stocks between 2013 and 2018, we apply several GARCH models, as well as a random forest and LSTM neural networks. According to our analysis, 1/1/ Student-t GARCH(1,1) is the best model in comparison with other traditional models due to its performance on a variety of volatility regimes. The machine learning exploration shows the strong limitations of data leakage and autocorrelation in financial time series, and it has very important methodological implications. Findings indicate that GARCH models attained realistic out of sample RMSE values of 0.96-4.82 whereas optimally implemented ML models result in more modest but candid performance indicators. The study will add to the knowledge about the shortcomings of volatility models and offer a strict framework of comparing econometric and machine learning methods in financial forecasting.
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