risk.esscher()

Esscher premium E[X exp(h X)] / E[exp(h X)]: the mean after tilting

Usage

risk.esscher(
    dist,
    h,
)

probability towards large losses.

The mean at h = 0; mu + h sigma**2 for a normal loss.

Parameters

dist: Sampled, PredictiveDistribution or list of float

A predictive distribution is measured on its total.

h: float

Returns

float

Examples

>>> import math
>>> from prospicio.risk import esscher
>>> round(esscher([0.0, 1.0], math.log(3.0)), 12)

0.75