R/rmgarch-tests.R

In mcremene/changedRmgarch2: Multivariate GARCH Models

#################################################################################
##
##   R package rmgarch by Alexios Ghalanos Copyright (C) 2008-2013.
##   This file is part of the R package rmgarch.
##
##   The R package rmgarch is free software: you can redistribute it and/or modify
##   it under the terms of the GNU General Public License as published by
##   the Free Software Foundation, either version 3 of the License, or
##   (at your option) any later version.
##
##   The R package rmgarch is distributed in the hope that it will be useful,
##   but WITHOUT ANY WARRANTY; without even the implied warranty of
##   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
##   GNU General Public License for more details.
##
#################################################################################

# DCC Test of Engle and Sheppard (using a CCC-Normal Copula rather than a CCC-Normal)
################################################################################
DCCtest = function(Data, garchOrder = c(1,1), n.lags = 1, solver = "solnp", 
		solver.control = list(), cluster = NULL, Z = NULL)
{
	if(is.null(Z)){
		Data = as.matrix(Data)
		n = dim(Data)[1]
		m = dim(Data)[2]
		uspec = ugarchspec(mean.model = list(armaOrder=c(0,0), include.mean = TRUE))
		mspec = multispec(replicate(m, uspec))
		cspec = cgarchspec(mspec, distribution.model = list(copula = "mvnorm", 
					transformation = "parametric", method = "Kendall"))
		cfit = cgarchfit(cspec, Data, fit.control = list(eval.se = FALSE, trace = FALSE),
				cluster = cluster, solver = solver, solver.control = solver.control)
		Z = cfit@mfit$stdresid
	} else{
		n = dim(Z)[1]
		m = dim(Z)[2]
	}
	OP = NULL
	for(i in 1:m){
		if(i<m){
		for(j in (i+1):m){
			OP = cbind(OP, Z[,i]*Z[,j])
		}}
	}
	j = dim(OP)[2]
	regressors = regressand = NULL
	for(i in 1:j){
		tmp = newlagmatrix(OP[,i,drop=FALSE],n.lags,1)
		regressors = rbind(regressors, tmp$x)
		regressand = c(regressand, tmp$y)
	}
	regressors = as.matrix(regressors)
	regressand = as.matrix(regressand)
	beta = t(qr.solve(regressand, regressors))
	XpX = t(regressors)%*%regressors
	e = regressand - regressors%*%beta
	sig = t(e)%*%e/(nrow(regressors))
	stat = t(beta)%*%XpX%*%beta/sqrt(sig)
	pval = 1-pchisq(stat, n.lags+1)
	H0 = "Constant Probability"
	ans = list()
	ans$H0 = H0
	ans$p.value = as.numeric(pval)
	ans$statistic = as.numeric(stat)
	return(ans)
}

################################################################################
MardiaTest = function(X, alpha){
	n = dim(X)[1]
	m = dim(X)[2]
	difT = scale(X, scale = FALSE)
	# Variance-covariance matrix
	S = cov(X)
	# Mahalanobis' distances matrix
	D = difT %*% solve(S) %*% t(difT)
	# Multivariate skewness coefficient
	b1p = sum(apply(D, 2, FUN = function(x) sum(x^3)))/(n^2)
	# Multivariate kurtosis coefficient
	b2p = sum(diag(D^2))/n
	# Small sample correction
	k = ((m+1)*(n+1)*(n+3))/(n*(((n+1)*(m+1))-6))
	# Degrees of freedom
	v = (m*(m+1)*(m+2))/6
	# Skewness test statistic corrected for small sample (approximates to a chi-square distribution)
	g1c = (n*b1p*k)/6
	# Skewness test statistic (approximates to a chi-square distribution)
	g1 = (n*b1p)/6
	# Significance value of skewness
	P1 = 1 - pchisq(g1,v)
	# Significance value of skewness corrected for small sample
	P1c = 1 - pchisq(g1c,v)
	# Kurtosis test statistic (approximates to a unit-normal distribution)
	g2 = (b2p-(m*(m+2)))/(sqrt((8*m*(m+2))/n))
	# Significance value of kurtosis
	P2 = 1-pnorm(abs(g2))
	stats = list()
	stats$Hs  = as.logical(P1 < alpha)
	stats$Ps  = P1
	stats$Ms  = g1
	stats$CVs = qchisq(1-alpha,v)
	stats$Hsc = as.logical(P1c < alpha)
	stats$Psc = P1c
	stats$Msc = g1c
	stats$Hk  = as.logical(P2 < alpha)
	stats$Pk  = P2
	stats$Mk  = g2
	stats$CVk = qnorm(1-alpha,0,1)
	# H1 = Alternative Hypothesis (Reject Multivariate Normality)
	H1 = c(stats$Hs, stats$Hsc, stats$Hk)
	stats$H1 = H1
	return(stats)
}