parametricVaR: parametricVaR

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

Description

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

Usage

parametricVaR(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

Value-at-Risk 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

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

parametricES analyticVaR analyticES

Examples

##
parametricVaR(.99, -1000000, .01)
##
parametricVaR(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)
parametricVaR(c, weights, varcov, t)

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