DACTest: Directional Accuracy Test

In rgarch: Flexible GARCH modelling in R

Description

Implements the Directional Accuracy Test of Pesaran and Timmerman and Excess Profitability Test of Anatolyev and Gerko.

Usage

DACTest(forecast, actual, test = c("PT", "AG"), conf.level = 0.95)

Arguments

forecast	A numeric vector of the forecasted values.
actual	A numeric vector of the actual (realized) values.
test	Choice of Pesaran and Timmermann (‘PT’) or Anatolyen and Gerko (‘AG’) tests.
conf.level	The confidence level at which the Null Hypothesis is evaluated.

Details

See the references for details on the tests. The Null is effectively that of independence, and distributed as N(0,1).

Value

A list with the following items:

Test	The type of test performed.
Stat	The test statistic.
p-value	The p-value of the test statistic.
H0	The Null Hypothesis.
Decision	Whether to reject or not the Null given the conf.level.
DirAcc	The directional accuracy of the forecast.

Author(s)

Alexios Ghalanos

References

Anatolyev, S. and Gerko, A. A trading approach to testing for predictability, 2005, Journal of Business and Economic Statistics, 23, 4, 455-461.
Pesaran, M.H. and Timmermann, A. A simple nonparametric test of predictive performance, 1992, Journal of Business and Economic Statistics, 10, 4, 461-465.

Examples

## Not run: 
	#  A univariate GARCH model is used with rolling out of sample forecasts.
	data(dji30ret)
	spec = ugarchspec(mean.model = list(armaOrder = c(6,1), include.mean = TRUE),
	variance.model = list(model = "gjrGARCH"), distribution.model = "nig")
	fit = ugarchfit(spec, data = dji30ret[, 1, drop = FALSE], out.sample = 1000)
	pred = ugarchforecast(fit, n.ahead = 1, n.roll = 999)
	dmatrix = cbind(as.array(pred)[,2,],as.array(pred)[,1,], coef(fit)["skew"], coef(fit)["shape"])
	colnames(dmatrix) = c("mu", "sigma", "skew", "shape")
	
	# Get Realized (Oberved) Data
	obsx = tail(dji30ret[,1], 1000)
	# you can check that this is correct by looking at the dates of the
	# first and last predictions:
	as.data.frame(pred, rollframe = 0)
	head(tail(dji30ret[,1, drop = FALSE], 1000), 1)
	
	as.data.frame(pred, rollframe = 999)
	tail(dji30ret[,1, drop = FALSE], 1)
	
	# Transform to Uniform
	uvector = apply(cbind(obsx,dmatrix), 1, FUN = function(x) pdist("nig", q = x[1],
	mu = x[2], sigma = x[3], skew = x[4], shape = x[5]))

	# hist(uvector)
	# transform to N(0,1)
	nvector = qnorm(uvector)
	test = BerkowitzLR(data = nvector, lags = 1, significance = 0.05)
	print(test)
	# We fail to reject Null at the 5% level, but not at 10%.
	# plot(density(nvector))

## End(Not run)

rgarch documentation built on May 2, 2019, 5:22 p.m.