// Personal Portfolio
MSCSIS Graduate Student
Building quantitative tools in market risk, credit risk, and portfolio stress testing, using Python, live market data, and industry standard methodologies.
// Portfolio at a glance
Computes Value at Risk using Historical, Parametric, and Monte Carlo methods on a live equity portfolio. Visualises the return distribution with all three VaR cutoff lines marked.
Logistic regression trained on the German Credit Dataset. Outputs probability of default per applicant and computes Expected Loss using EL = PD × LGD × EAD.
Applies five historical crisis scenarios (2008, COVID, 2022 rate hikes, dot-com bust) plus a custom hypothetical shock to a live portfolio, computing P&L impact per scenario.
Rolling 60-day correlation charts across equities, bonds, gold, oil, and crypto — revealing how diversification collapses in stress periods, exposing the core flaw in static VaR models.
A unified portfolio risk dashboard combining all four projects into one live tool. Enter any tickers and weights to see real-time VaR, rolling volatility, stress test results, and a cross-asset correlation heatmap — all computed from live Yahoo Finance data.
// Who I am
Background
Focused on building practical risk analytics skills to enter the finance sector as a Risk Analyst. Self-directed learner working through market risk, credit risk, and quantitative methods — building every concept into a working tool rather than just studying theory.
Currently learning
Deepening knowledge in quantitative risk methods through self-directed study and hands-on project work — building every concept into a working tool rather than studying theory alone.
Technical skills
Python · pandas · NumPy · SciPy · scikit-learn · Plotly · Streamlit · SQL · Git · yfinance
Risk domains
Market risk · Credit risk · Operational risk · Stress testing · VaR · CVaR · Expected Loss · Basel III frameworks