This is the ninth article in the series "Popular Risk management". The aim of the series is to describe the main Risk management topics in a simple, but at the same time a clear and concise language.  
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Written by Boris Agranovich

VaR Methodologies: The strengths and weaknesses of each method

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

Historical Simulation

Parametric

Monte Carlo


Sensitivities

Full revaluation



Accuracy

Poor for non‑linear and/or complex products

Good

Poor (for non‑linear and/or complex products)

Poor if based on sensitivities

Excellent if based on full revaluation

Treatment of Correlation

As good as historic data

As good as historic data

Poor

Flexible but complex

Treatment of path dependant pay-offs

No

Depends on the implementation

No

Yes

Treatment of illiquid positions

ad‑hoc & poor

ad-hoc & poor

ad-hoc & poor

ad-hoc & poor

Implementation

Efficient

Slow to run

Easy

Difficult

Potential for Stress testing

Sometimes inaccurate and only as good as historic data

Accurate but only as good as historic data

Poor

Flexible

Transparency

Good – VaR may be understood in terms of both sensitivities and market events on historic days

Good

Medium-Poor

Medium-Poor

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,

a)      the implementation of an appropriate full revaluation stress test,

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

c)      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