parametricES: parametricES

In f-kailin/varmonitor: Risk Management Tools: Value-at-Risk Monitor

Description

The function calculate the Expected Shortfall / Conditional Value-at-Risk for a portfolio of one or more securities with a parametric approach.

Usage

parametricES(confidence, position, sigma, horizon = 1)

Arguments

confidence	Quantile of the normal distirbution. (e.g. "0.95" or a vector with different confidence interval)
position	Vector of positions in monetary terms.
sigma	Volatility of a single secutiry or variance-covariance matrix of the whole portfolio.
horizon	Time horizon. (e.g. "daily: 1", "weekly: 5", "monthly: 22")

Details

The function automatically recognises if the input values are in scalar or multidimensional form.
It is also scalable with vectors containing multiple confidence intervals and/or time horizons.

Value

Expected Shortfall of a portfolio of "N" assets in monetary terms.

Note

Reminder: the central assumption under this function states that underlying market variables is normally distributed. This involves assuming a model for the joint distribution of changes in the market variables and using historical data to estimate the model parameters.

Author(s)

Gatti Riccardo, Lin Francesco

References

Artzner P., Delbaen F., Eber J.M., Heath D. (1999): "Coherent Measures of Risk" in "Mathematical Finance", 9th vol, Wiley.
Hull J.C. (2015): "Value-at-Risk and Expected Shortfall" in "Risk Management and Financial Institutions", Wiley.
Jorion P. (2007): "Portfolio Risk: Analytical Methods" in "Value at Risk", McGraw-Hill.

See Also

parametricVaR analyticVaR analyticES

Examples

##
parametricES(.99, -1000000, .01)
##
parametricES(c(0.95, 0.975, 0.99), 1000000, 0.01, c(1, 5, 22))
##
c <- c(0.95, 0.99)
t <- c(1, 5)
weights <- c(100000, -100000, 100000)
varcov <- matrix(c(0.05, 0.03, 0.01, 0.03, 0.04, 0.02, 0.01, 0.02, 0.03), nrow = 3)
parametricES(c, weights, varcov, t)

f-kailin/varmonitor documentation built on Dec. 20, 2021, 7:39 a.m.