risk-analysis

To calculate Value at Risk and Conditional Value at Risk, you provide a portfolio return series to determine the maximum potential loss at a specified confidence level. This process requires statistical libraries like NumPy, pandas, and SciPy for mathematical operations.

Monte Carlo simulation models future uncertainty of financial instruments by generating various scenarios to evaluate risk. It applies mathematical operations using NumPy and SciPy to project potential portfolio losses.

Yes, you can use pandas and SciPy to measure max drawdown, which calculates the maximum loss from a peak to a trough before a new peak is achieved. This automates risk calculation for financial portfolios.

Yes, you need NumPy, pandas, and SciPy to run VaR calculations and Monte Carlo simulations. These statistical analysis libraries handle the mathematical operations and data processing required for risk assessment.

The best way to model a portfolio's potential loss distribution is using Monte Carlo simulation alongside VaR and CVaR calculations. This approach helps identify critical risk factors and optimize the portfolio for lower risk.

Max drawdown analysis measures the maximum loss from peak to trough but should be combined with VaR and CVaR calculations for comprehensive risk assessment. Relying solely on max drawdown may not capture future uncertainty modeled by Monte Carlo simulation.