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.
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.
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.
Static Parametric (Variance–Covariance) VaR model for equity portfolios using historical returns and normal distribution assumptions.
📊 Analyze portfolio risk using PCA on daily returns of 20 large-cap US equities to reveal hidden factors and enhance interpretability.
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