LucasTrenzado/Predicting_Volatility_GARCH_vs_XGBoost
In this project, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a XGBoost Machine Learning model have been used to predict the volatility of Repsol's stock.
This project helps financial analysts and quantitative traders forecast stock volatility using historical stock price data. It takes past stock performance as input and generates predictions for future monthly volatility. The primary users are financial professionals who need accurate volatility forecasts for risk management and trading strategies.
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Use this if you need to accurately predict the short-term, rolling monthly volatility of a specific stock, especially if traditional GARCH models aren't meeting your performance needs.
Not ideal if you need to predict volatility for a broad market index or a highly illiquid asset, or if you're not interested in stock-specific volatility.
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Jan 02, 2023
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