risk-metrics-calculation

Calculate VaR and CVaR for portfolios by processing daily returns series with pandas and scipy. The skill computes historical Value at Risk and Conditional Value at Risk at specified confidence levels, alongside volatility and drawdown analysis for individual assets or entire portfolios.

Rolling window risk calculation and stress testing are supported natively. You can track metrics like Sharpe and Sortino ratios over time and simulate portfolio performance under historical or hypothetical adverse market conditions to quantify potential downside risk.

Beyond VaR and CVaR, available financial risk metrics include volatility, maximum drawdown, Sharpe ratio, Sortino ratio, and Calmar ratio. The skill also assesses portfolio risk contributions, diversification benefits, and asset correlations for comprehensive risk evaluation.

Yes, pandas, numpy, and scipy are required dependencies for statistical computations. These libraries handle data manipulation and statistical calculations necessary for computing risk metrics, correlations, and stress testing scenarios.

Analyze diversification and risk contributions by evaluating individual asset volatility and correlations within the portfolio context. The skill calculates risk metrics at both individual asset and portfolio levels to identify concentration risks and diversification benefits.

Calmar and Sortino ratios provide differentiated views on risk-adjusted returns. While Sharpe uses total volatility, Sortino focuses on downside deviation and Calmar relates returns to maximum drawdown, offering better downside risk assessment for investment portfolios.