risk-analysis

Portfolio VaR and CVaR are calculated using historical, parametric, and Monte Carlo methods by processing return series or net value data to project potential losses and tail-risk exposure. Results include structured risk metrics and recommended controls.

Historical VaR uses actual past return distributions, while Monte Carlo VaR generates simulated future scenarios. Both methods, alongside parametric VaR, are implemented to quantify portfolio risk and calculate metrics like CVaR and maximum drawdown.

Yes, stress testing and tail-risk analytics using Extreme Value Theory apply to equities, fixed income, and multi-asset allocations. The analysis covers historical and hypothetical scenarios, providing structured outputs and recommended risk controls.

VaR backtesting validates risk models by comparing projected losses against actual return series data. The analysis evaluates historical, parametric, and Monte Carlo VaR accuracy, producing structured outputs and recommended risk control adjustments.

You need to provide a portfolio's return series or net value data to generate VaR, CVaR, maximum drawdown, and Monte Carlo projections. This data drives scenario analysis and structured risk reporting across asset classes.

Use CVaR alongside VaR when tail-risk analysis is needed, as CVaR measures expected losses beyond the VaR threshold. Both metrics are calculated through historical, parametric, and Monte Carlo methods for comprehensive portfolio risk quantification.