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.

FRE6123 (Financial Risk Management) Group Project: Volatility Forecast Using GARCH and Temporal Convolutional Networks

A modular Python toolkit for advanced options pricing, volatility modeling, Greeks computation, and risk analysis. Includes Monte Carlo and Black-Scholes models, machine learning volatility surfaces, and interactive visualizations via Streamlit.

This project aims to model different Time Series data (mostly Stock data) by carrying out detailed analysis and fitting appropriate models.

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

The Analysis gives broad insight on Descriptive Analysis, Trend Analysis, Correlation Matrix, Covariance Matrix, Time Series Analysis, Volatility and Portfolio Optimization of stocks. With full insight given, Investors and Traders could determine the best stocks to invest in, the interpretation from the analysis clearly show stocks with high risk

Black–Scholes powered Python framework for options trading — featuring volatility forecasting, market microstructure analysis, and backtesting tools for building and deploying advanced trading strategies.