vwhs: Volatility weighted historical simulation

In quarks: Simple Methods for Calculating and Backtesting Value at Risk and Expected Shortfall

Volatility weighted historical simulation

Description

Calculates univariate Value at Risk and Expected Shortfall (Conditional Value at Risk) by means of volatility weighted historical simulation. Volatility can be estimated with an exponentially weighted moving average or a GARCH-type model.

Usage

vwhs(x, p = 0.975, model = c("EWMA", "GARCH"), lambda = 0.94, ...)

Arguments

x	a numeric vector of asset returns
p	confidence level for VaR calculation; default is 0.975
model	model for estimating conditional volatility; default is 'EWMA'
lambda	decay factor for the calculation of weights; default is 0.94
...	additional arguments of the ugarchspec function from the rugarch-package; the default settings for the arguments variance.model and mean.model are list(model = 'sGARCH', garchOrder = c(1, 1)) and list(armaOrder = c(0, 0)), respectively

Value

Returns a list with the following elements:

VaR

Calculated Value at Risk

ES

Calculated Expected Shortfall (Conditional Value at Risk)

p

Confidence level for VaR calculation

garchmod

The model fit. Is the respective GARCH fit for model = 'GARCH' (see rugarch documentation) and 'EWMA' for model = 'EWMA'

Examples

prices <- DAX$price.close
returns <- diff(log(prices))
# volatility weighting via EWMA
ewma <- vwhs(x = returns, p = 0.975, model = "EWMA", lambda = 0.94)
ewma
# volatility weighting via GARCH
garch <- vwhs(x = returns, p = 0.975, model = "GARCH", variance.model =
list(model = "sGARCH"))
garch

quarks documentation built on Sept. 1, 2022, 1:06 a.m.