BEKKs (version 1.4.5)

backtest: Backtesting via Value-at-Risk (VaR)

Description

Method for backtesting a model obtained from bekk_fit in terms of VaR-forcasting accuracy using a rolling window approach.

Usage

backtest(
  x,
  window_length = 1000,
  p = 0.99,
  portfolio_weights = NULL,
  n.ahead = 1,
  distribution = "empirical",
  nc = 1
)

Value

Returns a S3 class "backtest" object containing the VaR forecast, out-of-sample returns and backtest statistics according to the R-package "GAS". conf

Arguments

x

An object of class "bekkFit" from the function bekk_fit.

window_length

An integer specifying the length of the rolling window.

p

A numerical value that determines the confidence level. The default value is set at 0.99 in accordance with the Basel Regulation.

portfolio_weights

A vector determining the portfolio weights to calculate the portfolio VaR. If set to "NULL", the univariate VaR for each series are calculated.

n.ahead

Number of periods to predict conditional volatility. Default is a one-period ahead forecast.

distribution

A character string determining the assumed distribution of the residuals. Implemented are "normal", "empirical" and "t". The default is assuming the empirical distribution of the residuals.

nc

Number of cores to be used for parallel computation.

Examples

# \donttest{
Sys.setenv("OMP_THREAD_LIMIT"="1")
data(StocksBonds)
obj_spec <- bekk_spec()
x1 <- bekk_fit(obj_spec, StocksBonds, QML_t_ratios = FALSE, max_iter = 50, crit = 1e-9)

# backtesting
x2 <- backtest(x1, window_length = 6000, n.ahead = 1, nc = 1)
plot(x2)
# backtesting using 5 day-ahead forecasts
x3 <- backtest(x1, window_length = 6000, n.ahead = 5, nc = 1)
plot(x3)
# backtesting using 20 day-ahead forecasts and portfolio
x4 <- backtest(x1, window_length = 6000, portfolio_weights = c(0.5,0.5), n.ahead = 20, nc = 1)
plot(x4)
# }