risk-analysis

To calculate Value at Risk (VaR) and Expected Shortfall (CVaR), you apply statistical modeling to historical asset return series. These quantitative risk metrics measure potential portfolio losses and identify vulnerabilities beyond simple volatility.

To stress test your portfolio against historical market shocks, you apply historical scenario simulations to your asset return series. This evaluates portfolio resilience by modeling extreme market conditions like a financial crisis or liquidity dry-up.

Yes, you can run Monte Carlo simulations for financial risk analysis using Python libraries like numpy, pandas, and scipy. These dependencies execute the statistical modeling required to simulate asset returns and predict portfolio performance under extreme conditions.

Yes, this risk analysis approach detects fat-tail risks in asset returns by applying extreme value theory and statistical modeling. It identifies severe loss probabilities that standard volatility metrics often miss during sudden market shocks.

The best way to measure maximum drawdown for a trading portfolio is through quantitative analysis of asset return series. This calculates the maximum observed loss from a peak to a trough, highlighting portfolio vulnerabilities before they occur.

Yes, you need numpy, pandas, and scipy to perform Monte Carlo stress testing. These Python dependencies are required to execute the statistical modeling and extreme value theory analysis necessary for accurate financial risk measurement.