Choosing a Parametric VaR Model to Compare with ISDA SIMM
Summary
The document asks how to build a simple parametric value-at-risk estimate for comparison with ISDA SIMM initial margin. It specifically raises whether the profit-and-loss distribution should be normal or asymmetric, and whether a model should include option vega or gamma. A shifted lognormal distribution is suggested as a possibility, but no model is selected or derived.
The discussion also asks how SIMM can produce different initial margin amounts for counterparties if it uses a normal-distribution assumption, and seeks a mathematical derivation or explanation of the formulas. It provides no answers, calculations, empirical comparisons, or citations, so it does not establish that SIMM uses a normal distribution or explain its margin differences. Its value is as a framing of model design questions; any practical comparison would need further research into SIMM’s sensitivities, risk weights, correlations, and calibration.
Key ideas
- A parametric VaR comparison with SIMM must specify how option vega and gamma enter the risk estimate.
- The choice between normal and asymmetric profit-and-loss distributions is raised but not resolved.
- The document asks how SIMM can produce counterparty-specific initial margin under a normality assumption.
- No derivation, evidence, or recommended VaR method is provided.
Tags
Full text
# what is a simple parametric VaR approach that can be used to compare with ISDA SIMM results? # what is a simple parametric VaR approach that can be used to compare with ISDA SIMM results? i am looking for a simple parametric VaR approach that includes vega and/or gamma. i am not sure if to choose a non symmetric distribution for the pnl, what would people use, perhaps some shifted lognormal (shifted so it's mean is zero) ? , or to just choose normal. i think isda simm uses normal , but then, how do they achieve that the IM for each party is different ? also if anyone has any links to maths that show the derivation for the isda simm formulas, or can offer a simple explanation of them here that would be great to see!
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