TensorFlow implementation of the HARNet model for realized volatility forecasting.

Systematic Volatility Research and Backtesting for equity options

Official code - M2VN(Multi-Modal Learning Network for 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.

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.

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.

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.

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

Independent R&D bridging classical financial econometrics and modern continuous-time deep learning. Projects on PINNs for Value-at-Risk and Neural SDEs for density forecasting. "Complexity must earn its place."

Forecasting 21-day realised volatility on the URA uranium ETF using a stacked LSTM network, benchmarked against a GARCH(1,1) baseline.

Forecast stock prices and volatility using ARIMA, SARIMA, Prophet, and LSTM. Includes technical indicators (RSI, MACD, Bollinger), evaluation metrics, a long/flat backtester, and an interactive Streamlit dashboard.

High-performance portfolio risk engine with C++ Monte Carlo core and hybrid GARCH-LSTM volatility modeling.

Algorithmic market structure framework for statistical mean reversion and predictive volatility anchoring.

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.

Transformer for FX realized-volatility forecasting. Each hourly block encodes the joint market state (10 forex pairs + 14 macros + events + HAR features) into a single context vector; 24-horizon output for one target symbol.

Regime-conditional volatility forecasting framework using HAR-RV as a baseline and XGBoost on either residual vol or directly on log(RV), implemented for Germany and France electricity markets. Metric: Spearman ranking. Model validation and market-neutral cross-country trading strategy.

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

Dual-head Transformer for volatility forecasting & regime classification, injected into Monte Carlo price path simulation. Streamlit dashboard included.

Bi-LSTM + Attention vs HAR — comparative study of deep learning and econometric models in realized volatility forecasting (SPY, EWG, EPOL, EEM)