Repository represents python usability of measuring and managing risks (practice tasks and real cases)

Risk attribution report for portfolio management, using MatLab and Excel

Essential techniques to assess financial risks

Quantitative analysis techniques with Python and Pandas to determine which portfolio is performing best across many areas: volatility, returns, risk, and Sharpe Ratios.

Static Parametric (Variance–Covariance) VaR model for equity portfolios using historical returns and normal distribution assumptions.

A Python-based portfolio optimization tool that implements Modern Portfolio Theory (MPT) to create efficient portfolios.

Calculate VaR of Tesla Equity share with Historical, Variance-Covariance and MonteCarlo simulations methods

Rolling parametric VaR model for an equity portfolio with backtesting, exception analysis, and diagnostic plots.

Mobile-first MRI-based market regime interpretation engine with risk-adjusted confidence modeling.

Python framework for multi-asset portfolio market risk analysis including rolling beta, VaR/CVaR, Monte Carlo simulation, stress testing, and risk decomposition.

Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.

Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.

📊 Analyze portfolio risk using PCA on daily returns of 20 large-cap US equities to reveal hidden factors and enhance interpretability.