forward-risk

Value at Risk and Expected Shortfall are calculated using parametric models, empirical distributions, or Monte Carlo simulations to quantify potential portfolio losses at specific confidence thresholds and analyze tail risk.

Monte Carlo simulation for stress testing models portfolio performance under adverse market events like crashes or interest rate shocks, generating distribution scenarios to estimate potential losses and evaluate risk management strategies.

Decomposing portfolio risk into component and marginal VaR identifies which specific assets or factors contribute most to overall risk, enabling targeted risk management and quantitative analysis of portfolio vulnerabilities.

Yes, numpy and scipy support regulatory capital calculations and factor risk analysis by implementing parametric models and quantitative risk management algorithms to estimate potential future losses accurately.

Conditional VaR is needed when analyzing tail risk beyond standard VaR thresholds, calculating average losses given threshold breaches to provide deeper insight into extreme portfolio loss scenarios.