It is approximate method for deriving quantiles of any random variable distribution based on its cumulants. Cornish-Fisher Expansion can be used , for example, to calculate various
Value At Risk (VaR) quantiles.
Cumulants
κr are an alternative expression of
distribution moments
µr. For a cumulant
κr of an order
r it holds for all real
t (where
µr‘ denotes raw moment):
Not trivial to derive at all, but can be generally expressed by recursion:
This leads to expression from raw moments (first 4 cumulants showed):
Alternatively derived from central moments (for
r>1):
The Cornish-Fischer expansion for approximate determination of quantile
xq builds on the variable
X mean
µ, standard deviation
σ and cumulants
κr, with the help of quantiles of standard normal distribution
N(0,1):
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