Projects

A selection of data science projects showcasing statistical modeling, Bayesian inference, optimization, and interactive visualization.

1. Bayesian Volatility Modeling: GARCH vs Stochastic Volatility

Comparing two Bayesian volatility models on S&P 500 weekly data (2013 to 2023). Built with Shiny for Python, featuring real-time model comparison and Plotly visualizations.

Bayesian parameter estimation using MCMC (Stan)
GARCH(1,1) vs Stochastic Volatility model comparison
Out-of-sample prediction including the COVID-19 volatility spike

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2. Portfolio Optimization: Quantitative Approaches

Comparing five portfolio optimization strategies on a diversified 16-asset portfolio backtested over 2021 to 2024.

Mean-Variance Optimization with efficient frontier construction
Maximum Sharpe Ratio, GMV, and Risk Parity strategies
Backtesting against S&P 500 and equal-weight benchmarks

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