risk-metrics-calculation

To calculate portfolio VaR and CVaR, you can use Python libraries like numpy, pandas, and scipy to compute tail risks across single and multi-asset datasets. This Skill supports historical, parametric, and Cornish-Fisher methods for various portfolio horizons.

Measuring maximum drawdown and drawdown duration involves analyzing your return series to identify peak-to-trough declines. This Skill computes these drawdown metrics to help portfolio managers evaluate capital preservation and risk exposure.

Computing risk-adjusted metrics like Sharpe, Sortino, Calmar, and Omega ratios requires your portfolio's return series. This Skill calculates these performance ratios alongside volatility to generate comprehensive risk governance and dashboarding reports.

Yes, you can use scipy and numpy to calculate risk metrics across intraday to annual horizons. This Skill leverages these Python libraries to compute distributions, tail risks, and summary reports suitable for both single and multi-asset portfolios.

Historical VaR uses actual past return distributions, while parametric VaR assumes a normal distribution, and Cornish-Fisher adjusts for skewness and kurtosis. This Skill supports all three methods to quantify portfolio risk depending on your data characteristics.

Cornish-Fisher VaR adjusts parametric calculations for non-normal skewness and kurtosis in return distributions, providing a more accurate tail risk estimate. This Skill includes the Cornish-Fisher method alongside historical and parametric VaR for comprehensive risk analysis.