stress_wass: Stressing Random Variables Using Wasserstein Distance

In spesenti/SWIM: Scenario Weights for Importance Measurement

Description

Provides weights on simulated scenarios from a baseline stochastic model, such that stressed random variables fulfill given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the Wasserstein Distance to the baseline model.

Usage

stress_wass(type = c("RM", "mean sd", "RM mean sd", "HARA RM"), x, ...)

Arguments

type	Type of stress, one of "RM", "mean sd", "RM mean sd", "HARA RM".
x	A vector, matrix or data frame containing realisations of random variables. Columns of x correspond to random variables; OR A SWIMw object, where x corresponds to the underlying data of the SWIMw object.
...	Arguments to be passed on, depending on type.

Value

An object of class SWIMw, see SWIM for details.

Author(s)

Zhuomin Mao

References

\insertRef

Pesenti2019reverseSWIM

\insertRef

Pesenti2020SSRNSWIM

\insertRef

Csiszar1975SWIM

See Also

Other stress functions: stress_HARA_RM_w(), stress_RM_mean_sd_w(), stress_RM_w(), stress_VaR_ES(), stress_VaR(), stress_mean_sd_w(), stress_mean_sd(), stress_mean_w(), stress_mean(), stress_moment(), stress_prob(), stress_user(), stress()

Examples

## Not run: 
set.seed(0)
x <- as.data.frame(cbind(
  "normal" = rnorm(1000), 
  "gamma" = rgamma(1000, shape = 2)))
res <- stress_wass(type = "RM", x = x, 
  alpha = 0.9, q_ratio = 1.05)
summary(res)   

## End(Not run)

spesenti/SWIM documentation built on Jan. 15, 2022, 11:19 a.m.