Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.
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.
Portfolio credit risk modeling project evolving from independent to multi-factor migration, with regime stress and tail diagnostics. Part of a broader transition toward quantitative finance and systematic risk modeling.
Calculate VaR of Tesla Equity share with Historical, Variance-Covariance and MonteCarlo simulations methods
A Python-based portfolio optimization tool that implements Modern Portfolio Theory (MPT) to create efficient portfolios.
Mobile-first MRI-based market regime interpretation engine with risk-adjusted confidence modeling.
Derivatives pricing and risk engine: Black-Scholes, Greeks, Monte Carlo, implied vol, VaR/CVaR, real market data, and a 12-page Streamlit dashboard.
Rolling parametric VaR model for an equity portfolio with backtesting, exception analysis, and diagnostic plots.
Static Parametric (Variance–Covariance) VaR model for equity portfolios using historical returns and normal distribution assumptions.
Commercial credit stress testing repo for scenario overlays across PD, LGD, EAD, and expected loss at facility and portfolio level for risk review and lending strategy.
Python framework for multi-asset portfolio market risk analysis including rolling beta, VaR/CVaR, Monte Carlo simulation, stress testing, and risk decomposition.
A data-driven risk analytics system for fund and benchmark analysis, translating NAV time series into actionable risk insights, drawdown signals, and structured reports to support investment decision-making.
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.
📊 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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