This repository is meant to provide all Python code, data, calculations and results of the research Forecasting the Realized Volatility of S&P 500 by Financial Turbulence and Neural Networks in the journal Economics and Business Review.
this paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
Souto, Hugo Gobato, and Amir Moradi. 2023. “Forecasting Realized Volatility through Financial Turbulence and Neural Networks”. Economics and Business Review 9 (2). https://doi.org/10.18559/ebr.2023.2.737