Volatility & Risk Forecasting for PRY.MI, G.MI, and REC.MI. Implements GARCH & GJR-GARCH (Gaussian/t) models in R to estimate 5% VaR. Validated via Hit Tests and Diebold-Mariano (HAC) against RiskMetrics. Results highlight the superior performance of Student’s t-models in capturing tail risk, leverage effects, and distributional asymmetries.

A quantitative finance project that builds a web-based platform for monitoring market volatility and portfolio risk metrics. The system uses market data, machine learning models, and quantitative finance methods to forecast volatility and analyze financial risk for portfolio managers, risk analysts, and quantitative researchers.