lossfun: Loss Functions

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

Loss Functions

Description

This functions allows for the calculation of loss functions in order to assess the performance of models in regard to forecasting ES.

Usage

lossfun(obj = list(loss = NULL, ES = NULL), beta = 1e-04)

Arguments

obj	a list that contains the following elements: loss a numeric vector that contains the values of a loss series ordered from past to present; is set to NULL by default ES a numeric vector that contains the estimated values of the ES for the same time points of the loss series loss; is set to NULL by default Please note that a list returned by the rollcast function can be directly passed to lossfun.
beta	a single numeric value; a measure for the opportunity cost of capital; default is 1e-04.

Details

Given a negative return series obj$loss, the corresponding Expected Shortfall (ES) estimates obj$ES and a parameter beta that defines the opportunity cost of capital, four different definitions of loss functions are considered.

Value

an S3 class object, which is a list of

loss.fun1

regulatory loss function

loss.fun2

firm's loss function following Sarma et al. (2003)

loss.fun3

loss function following Abad et al. (2015)

loss.fun4

Feng's loss function; a compromise of regulatory and firm's loss function

References

Abad, P., Muela, S. B., & Martín, C. L. (2015). The role of the loss function in value-at-risk comparisons. The Journal of Risk Model Validation, 9(1), 1-19.

Sarma, M., Thomas, S., & Shah, A. (2003). Selection of Value-at-Risk models. Journal of Forecasting, 22(4), 337-358.

Examples

prices <- DAX$price.close
returns <- diff(log(prices))
n <- length(returns)
nout <- 250 # number of obs. for out-of-sample forecasting
nwin <- 500 # window size for rolling forecasts
results <- rollcast(x = returns, p = 0.975, method = 'age', nout = nout,
                     nwin = nwin)
loss <- -results$xout
ES <- results$ES
loss.data <- list(loss = loss, ES = ES)
lossfun(loss.data)

# directly passing the output object of 'rollcast()' to 'lossfun()'
lossfun(results)

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