Question:

Does @RISK handle a generalized Pareto distribution?

Answer:

Yes, @RISK can handle a generalized Pareto distribution, but the shape parameter must be positive.

The generalized Pareto distribution (GPD) uses three parameters:

• Location (μ) (mu)

• Scale (σ) (sigma)

• Shape (k)

@RISK’s equivalent is RiskPareto2, which takes:

• Scale (b)

• Shape (q)

• Optional location shift via RiskShift()

Parameter conversions

GPD Parameter	RiskPareto2 Parameter	Conversion
σ (scale)	b	b = σ / k
k (shape)	q	q = 1 / k
μ (location)	RiskShift(μ)	μ = RiskShift value

Function equivalence

• GPD(μ, σ, k) → RiskPareto2(σ/k, 1/k, RiskShift(μ))

• RiskPareto2(b, q, RiskShift(μ)) → GPD(μ, b/q, 1/q)

This lets you map generalized Pareto distributions to @RISK functions for simulations.