TensorFlow implementation of the HARNet model for realized volatility forecasting.

Daily Volatility trading strategies on Index Equity Options

IBOVESPA volatility forecasting

A comprehensive analysis and forecasting project for Samsung stock data, utilizing historical data to build predictive models and analyze volatility.

Official code - M2VN(Multi-Modal Learning Network for Volatility Forecasting)

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.

Advanced stock forecasting system using LSTM neural networks with real-time sentiment analysis. Predicts price movements and volatility by combining technical indicators, news sentiment from Finnhub API, and multivariate analysis. Features dual LSTM models, intelligent alerts, and comprehensive risk assessment for informed trading decisions.

An autonomous risk-overlay system simulating a hedge fund Investment Committee. Uses Multi-Agent Architecture (LangGraph) to validate algorithmic signals by combining deep-learning volatility forecasts (VolSense) with fundamental semantic reasoning and CVaR constraints.

📈 Forecast stock prices and volatility using LSTM neural networks and sentiment analysis for informed trading decisions and risk assessment.

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

Forecasting daily realized volatility using 5-minute microstructure data (ARIMAX, SARIMAX, State-Space models)

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

High-frequency IV forecasting for ETH using LightGBM and cross-asset order book features

This model predicts future market volatility.Portfolio managers, traders and financial institutions use this to manage risk, price options, and decide how much exposure to take in financial markets.

📈 Analyze A-share market trends with AI-driven insights, simulating a top fund's decision-making process through advanced multi-agent collaboration.

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