TensorFlow implementation of the HARNet model for realized volatility forecasting.
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Updated
Jul 16, 2023 - Python
TensorFlow implementation of the HARNet model for realized volatility forecasting.
IBOVESPA volatility forecasting
A comprehensive analysis and forecasting project for Samsung stock data, utilizing historical data to build predictive models and analyze volatility.
Comparing the performance of the GARCH(1,1) model and historical volatility, close-to-close volatility, Parkinson volatility, Garman-Klass volatility and Rogers-Satchell volatility in the rolling window method to forecast future volatility on the NASDAQ composite.
This project aims to model different Time Series data (mostly Stock data) by carrying out detailed analysis and fitting appropriate models.
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