risk-metrics-calculation

To calculate Value at Risk and Conditional Value at Risk, apply historical, parametric, or Cornish-Fisher VaR methods to your periodic returns data. The computation quantifies tail risk exposures and expected shortfall for portfolio risk monitoring and regulatory reporting.

Historical VaR measures portfolio risk using actual past return distributions, while Cornish-Fisher VaR adjusts parametric calculations for skewness and kurtosis. Both quantify tail risk, but Cornish-Fisher provides better estimates for non-normal return series.

Compute rolling-window volatility and drawdown durations by applying rolling-window metrics to your periodic asset-level returns data. This generates time-varying risk measures, maximum drawdown statistics, and drawdown duration analytics for continuous risk monitoring.

Yes, you can calculate covariance-based portfolio volatility, marginal risk contributions, and component risk contributions. These portfolio-level analyses support risk parity optimization by identifying how individual assets impact overall portfolio risk and diversification ratio.

Yes, Monte Carlo stress simulation supports historical scenario tests and hypothetical shocks for elevated volatility analysis. The stress testing functionality models portfolio risk under extreme conditions, complementing rolling-window computations and standard risk metrics.

You can calculate Sharpe, Sortino, Calmar, Omega, and information ratio metrics using periodic returns and an optional benchmark series. These risk-adjusted performance metrics evaluate portfolio returns relative to volatility, downside risk, and benchmark comparisons.