risk: Risk Measures of Fitted Objects

In mistr: Mixture and Composite Distributions

Risk Measures of Fitted Objects

Description

risk computes the VaR, ES and expectiles at a given level for fitted distribution.

Usage

risk(
  model,
  alpha,
  expectile = TRUE,
  plot = FALSE,
  ggplot = FALSE,
  text_ylim = -0.15,
  size = 1
)

## S3 method for class 'PNP'
risk(
  model,
  alpha = 0.05,
  expectile = TRUE,
  plot = FALSE,
  ggplot = FALSE,
  text_ylim = -0.15,
  size = 1
)

## S3 method for class 'GNG'
risk(
  model,
  alpha = 0.05,
  expectile = TRUE,
  plot = FALSE,
  ggplot = FALSE,
  text_ylim = -0.15,
  size = 1
)

Arguments

model	output object of GNG_fit() or PNP_fit().
alpha	levels of risk measures.
expectile	logical, if also expectiles should be computed, default: TRUE.
plot	plot the results?, default: FALSE.
ggplot	plot the results with ggplot2?, default: FALSE.
text_ylim	y coordinate for annotation in ggplot2, default: -0.15.
size	size of the text indicating the risk measures in the plot, default: 1.

Details

VaR are computed using the q() call of the fitted distribution.

ES is computed directly (i.e. the integrals are precomputed, not numerically) as an integral of the quantile function.

Expectiles can be obtained as a unit-root solution of the identity between quantiles and expectiles. These are equivalent for corresponding τ and α if

τ=(α q(α) -G(α))/(μ - 2G(α)-(1-2α)q(α))

where μ is mean, q() is the quantile function and G(α) =\int_{-∞}^{q(α)} y dF(y).

Value

List of class risk_measures.

Examples

## Not run: 
 GNG <- GNG_fit(stocks$SAP)
 PNP <- PNP_fit(stocks$MSFT)

 risk(PNP, alpha = c(0.01,0.05,0.08,0.1))
 risk(GNG, alpha = c(0.01,0.05,0.08,0.1), plot = TRUE)

## End(Not run)

mistr documentation built on March 7, 2023, 7:42 p.m.