CHARTING THROUGH MODERN FINANCIAL MARKETS A CONTEMPORARY APPROACH TO FORECASTING STOCK VOLATILITY

Abstract

This study aims to navigate the critical role of financial volatility in sectors from risk management to behavioral economics by evaluating the predictive accuracy of traditional, machine learning, and deep learning models specifically for the Standard and Poor's 500 (S&P 500) Index, a cornerstone of the United States (U.S.) economy. The goal is to identify the most effective forecasting model, recognizing its substantial impact on investors and institutions. This study compares the efficacy of traditional models, such as the Heterogeneous Autoregressive-realized Volatility (HAR-RV) model and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process, with machine learning algorithms such as Random Forest, Regularization Methods, and Neural Networks in volatility prediction, aiming to discern the most robust approach by evaluating each method's strengths and weaknesses. Contrary to popular belief, our analysis showed traditional models, particularly HAR-RV and GARCH(1,1), held firm against most machine learning algorithms, such as Random Forest, Long-Short Term Memory (LSTM), and Multi-layered Perceptrons (MLPs) also demonstrated notable predictive prowess. We then extended the study by exploring forecast combination techniques and model evaluation methods. The findings highlight the strengths and limitations of different forecasting models, which are crucial not just academically but also for enhancing risk management, portfolio resilience, derivative pricing accuracy, and market stability assessment tools for policymakers. This study aims to navigate the critical role of financial volatility in sectors from risk management to behavioral economics by evaluating the predictive accuracy of traditional, machine learning, and deep learning models specifically for the Standard and Poor's 500 (S&P 500) Index, a cornerstone of the United States (U.S.) economy. The goal is to identify the most effective forecasting model, recognizing its substantial impact on investors and institutions. This study compares the efficacy of traditional models, such as the Heterogeneous Autoregressive-realized Volatility (HAR-RV) model and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process, with machine learning algorithms such as Random Forest, Regularization Methods, and Neural Networks in volatility prediction, aiming to discern the most robust approach by evaluating each method's strengths and weaknesses. Contrary to popular belief, our analysis showed traditional models, particularly HAR-RV and GARCH(1,1), held firm against most machine learning algorithms, such as Random Forest, Long-Short Term Memory (LSTM), and Multi-layered Perceptrons (MLPs) also demonstrated notable predictive prowess. We then extended the study by exploring forecast combination techniques and model evaluation methods. The findings highlight the strengths and limitations of different forecasting models, which are crucial not just academically but also for enhancing risk management, portfolio resilience, derivative pricing accuracy, and market stability assessment tools for policymakers.