VaR Methodologies: The strengths and weaknesses of each method

Boris Agranovich

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    VAR or Value at risk is a summary measure of downside risk expressed in the reference currency. A general definition is: VAR is the maximum expected loss over a given period at a given level of confidence. VaR does not inform on the size of loss that might occur beyond that confidence level.

    The method used to calculate VaR may be historical simulation (either based on sensitivities or full revaluation), parametric, or Monte Carlo simulation. All methodologies share both a dependency on historic data, and a set of assumptions about the liquidity of the underlying positions and the continuous nature of underlying markets. In the wake of the current crisis, the weaknesses of VAR methodology became apparent and they need to be addressed.

    VaR Methodologies: comparative analysis
    Addressing the limitations

    Where these limitations may cause a material inaccuracy of VaR results, additional measures should be taken including one or more of the following:

    1.      The prompt review & correction of time series data used as an input to the VaR Model.

    2.      A VaR Add-on using a methodology that addresses the weakness.

    3.      The implementation of an appropriate full revaluation stress test.

    4.      the implementation of full revaluation or an alternative VaR methodology at a portfolio level                                (e.g. Monte Carlo simulation)

    5.      Market risk limit monitoring.

    A VAR system alone will not be effective in protecting against market risk. It needs to be used only in combination with limits both on notional amounts and exposures and in addition should be reinforced by vigorous stress tests.

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    Written by Boris Agranovich