risk-analysis

Portfolio VaR and CVaR are calculated using historical, parametric, or Monte Carlo simulations to assess portfolio vulnerability. This approach applies statistical modeling and distribution fitting to return series to quantify potential drawdowns.

Stress-testing assets evaluates portfolio resilience against historical crises or custom hypothetical shocks. By simulating events like a 100bp interest rate hike, you can measure vulnerability and identify potential impacts on current asset allocations before making adjustments.

Tail-risk analysis identifies hidden vulnerabilities by detecting fat-tail risks using Extreme Value Theory and Generalized Pareto Distribution fitting. This method uncovers extreme market movements beyond standard volatility metrics in your investment strategies.

Yes, scipy and numpy are required to perform statistical modeling and distribution fitting on return series. These dependencies enable the mathematical computations necessary for calculating risk metrics and running Monte Carlo simulations.

Historical return data measures financial risk and evaluates drawdown potential by calculating Value at Risk and Conditional Value at Risk. This helps you move beyond simple volatility to understand tail-risk and the impact of extreme market scenarios.