stress_wass {SWIM}

Stressing Random Variables Using Wasserstein Distance

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

Pesenti SM, Millossovich P, Tsanakas A (2019). “Reverse sensitivity testing: What does it take to break the model?” European Journal of Operational Research, 274(2), 654–670.

Pesenti S BAMPTA (2020). “Scenario Weights for Importance Measurement (SWIM) - An R package for sensitivity analysis.” Annals of Actuarial Science 15.2 (2021): 458-483. Available at SSRN: https://www.ssrn.com/abstract=3515274.

Csiszar I (1975). “I-divergence geometry of probability distributions and minimization problems.” The Annals of Probability, 146–158.

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)