MSc Economics & Finance candidate at LUISS Guido Carli, focused on quantitative finance, market risk, asset pricing and portfolio analytics. I build empirical finance projects in Python and MATLAB, with applications to prediction markets, derivatives-implied probabilities and multi-asset risk management.
MSc thesis comparing Bitcoin prediction-market probabilities on Polymarket and Kalshi with a DVOL-based Deribit benchmark.
The empirical pipeline covers API-based data collection, contract parsing and classification, timestamp and maturity matching, benchmark construction, bootstrap inference, OLS regressions with robust and clustered standard errors, and monthly and horizon-based robustness checks.
Tools & methods: Python, REST APIs, pandas, market data processing, Black-Scholes-style probability benchmark, bootstrap inference, OLS regressions, clustered standard errors.
Python-based market risk engine covering equities, Treasuries, credit, gold and crypto.
The project includes portfolio construction, volatility forecasting, VaR and Expected Shortfall estimation, stress testing, risk model backtesting and regime-aware risk monitoring. The objective is to evaluate how standard market risk models behave across normal and stressed market conditions.
Tools & methods: Python, pandas, NumPy, SciPy, statsmodels, GARCH, VaR/Expected Shortfall, stress testing, risk attribution, risk model backtesting.
Research project testing whether a systematic long-short cryptocurrency strategy can enhance a traditional 60/40 equity-bond portfolio.
The strategy is based on Size and Risk-Adjusted Momentum signals and was tested on 79 liquid cryptocurrencies, excluding stablecoins, using a framework including CAPM/Fama-French regressions, GARCH models, bootstrap simulations and stress scenarios.
Tools & methods: MATLAB, Python, portfolio backtesting, factor models, GARCH, bootstrap simulation, stress testing.
Top 3 project at the LUISS Prompt-a-thon 2025. Co-developed a WatsonX-based AI infrastructure proposal for NASA data governance, mission autonomy and operational efficiency.
Focus: AI strategy, data governance, IBM WatsonX, enterprise architecture.
Programming & Data Python, MATLAB, R, VBA, Advanced Excel, LaTeX
Finance & Econometrics OLS regressions, CAPM/Fama-French models, GARCH, VaR/Expected Shortfall, Fama-MacBeth tests, bootstrap simulation, stress testing, risk model backtesting, portfolio backtesting
Platforms Bloomberg Terminal, Aladdin, Quantalys, Refinitiv, yfinance, REST APIs
- Building applied quantitative finance projects in Python and MATLAB
- Researching cross-market pricing and derivatives-implied probabilities
- Developing portfolio analytics and market risk tools
- Preparing for roles in market risk, quantitative finance, portfolio analytics and asset management
- LinkedIn: linkedin.com/in/giannandreadestefano
- GitHub: github.com/giannandreadestefano
- Email: giannandrea979@gmail.com