There are three major methodologies for calculating VaR.
- Monte Carlo
Note that the risk of nonlinear instruments (for example, options) is more complex to estimate than the risk of linear instruments (for example, traditional stocks, bonds, swaps, forwards, and futures), which can be approximated with simple formulas.
Financial instruments are nonlinear when their price does not change by a constant amount given a small movement in an underlying reference asset.
Brief description and use of each approach:
|Parametric||Estimates VaR with equation that specifies parameters (for example, volatility and correlation) as input.||Accurate for traditional assets and linear derivatives, but less accurate for nonlinear derivatives|
|Monte Carlo||Estimates VaR by simulating random scenarios and revaluing instruments in the portfolio||Appropriate for all types of instruments, linear or nonlinear|
|Historical||Estimates VaR by reliving history; we take actual
historical rates and revalue a portfolio for each
change in the market
|Appropriate for all types of instruments, linear or non-linear|
Advantages and Disadvantages
|Parametric||Fast and simple to calculate||Less accurate for non-linear portfolios|
||Takes a lot of computational power (and hence a
longer time to estimate results)
From a user’s perspective, the important point to remember is that if you have significant nonlinear exposures in your portfolio, a simulation approach will generally be more accurate for estimating VaR than a parametric approximation–however, at the cost of greater complexity and computational requirements.
All three approaches for estimating VaR have something to offer and can be used together to get a more robust estimate of VaR. For example, a parametric approach may be used to get an instant snapshot of risks taken during a trading day, while a simulation approach may be used to provide a fuller picture of risks (in particular, nonlinear risks) on a next-day basis.