context("skew")
library(mcrp)
data(MultiAsset)
MA <- as.timeSeries(MultiAsset[, 1:4])
r <- na.omit(diff(log(MA)) * 100)
N <- ncol(r)
w <- rep(1 / N, N) ## equal weight allocation
test_that("Co-skewness is matrix (N x N^2)", {
a <- M3(r)
expect_true(is.matrix(a))
expect_equal(dim(a), c(N, N ^ 2))
})
test_that("Portfolio skewness is scalar", {
a <- pm3(r, w)
expect_identical(length(a), 1L)
})
test_that("Partial derivatives of portfolio skewness is matrix", {
a <- dm3(r, w)
expect_true(is.matrix(a))
expect_equal(dim(a), c(N, 1))
b <- PortSkewDeriv(r, w)
expect_true(is.matrix(b))
expect_equal(dim(b), c(N, 1))
})
test_that("Skewness contributions sum to one or
are equal to portfolio skewness", {
a <- PortSkewContrib(r, w, percentage = TRUE)
expect_true(is.matrix(a))
expect_equal(dim(a), c(N, 1))
expect_equal(sum(a), 1.0)
b <- PortSkewContrib(r, w, percentage = FALSE)
pskew1 <- PortSkew(r, w)
expect_true(is.matrix(b))
expect_equal(dim(b), c(N, 1))
expect_equal(sum(b), pskew1)
d <- cm3(r, w, percentage = TRUE)
expect_true(is.matrix(d))
expect_equal(dim(d), c(N, 1))
expect_equal(sum(d), 1.0)
e <- cm3(r, w, percentage = FALSE)
pskew2 <- pm3(r, w)
expect_true(is.matrix(e))
expect_equal(dim(e), c(N, 1))
expect_equal(sum(e), pskew2)
})
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