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Value at Risk – “VaR” – has become the single most widely recognised measure of the risk of an investment portfolio. Typically it is used to estimate the likely maximum absolute loss of a portfolio though in certain circumstances it is also calculated relative to a benchmark. The Excerpt VaR report is designed for evaluating the full returns distribution of a selected portfolio.

Comprehensive reporting

The Excerpt VaR report provides a histogram of the returns distribution, VaR and expected shortfall (conditional VaR) for different confidence levels over the time horizon selected by the user. The reports show the headline VaR for both the portfolio and the active portfolio at the 90%, 95% and 99% confidence levels over a variety of time horizons from 1 day to 1 month.

Multiple VaR methodologies

Excerpt provides several VaR calculation methods - Linear Parametric, Historic Simulation and Monte-Carlo. Both the Simulation and the Monte Carlo VaR perform full instrument repricing to build an accurate picture of non-linear risks in the portfolio. VaR calculations are available for all portfolios – equity, bond, derivative based and multi-asset.


Excerpt provides linear parametric VaR estimates based on our APT style factor models coupled with sophisticated pricing models. Using linear parametric VaR allows the intuitive deconstruction of the sources of VaR for almost any invested asset, with a wide variety of user selected parameters such as time period or implied volatilities.


The Historic Simulation Value-at-Risk (HSVaR) calculation uses an archive of historic price data. Equity prices, FX rates and yield curve structures are all stored and used in the calculation. Users can supply an alternative frequency or period of returns data.


Monte-Carlo uses the historical factor model to generate random shifts for each factor and each residual-risk component. These shifts are applied to all of the key instruments – equities, FX rates, interest rates and credit rates in a way that is consistent with the market data covariance structure.

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