context("risk")
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("Output of M2() equals cov()", {
a <- M2(r)
b <- cov(r)
expect_equal(dim(a), c(N, N))
expect_equal(a, b)
})
test_that("Portfolio variance is positive and scalar", {
a <- pm2(r, w)
b <- PortRisk(r, w)
expect_equal(a, b)
expect_equal(dim(a), NULL)
expect_identical(length(a), 1L)
expect_true(a > 0)
})
test_that("Partial derivatives of portfolio variance is matrix", {
a <- dm2(r, w)
b <- PortRiskDeriv(r, w)
expect_equal(a, b)
expect_true(is.matrix(a))
expect_equal(dim(a), c(N, 1))
})
test_that("Risk contributions sum to one or are equal to portfolio variance", {
a <- PortRiskContrib(r, w, percentage = TRUE)
expect_true(is.matrix(a))
expect_equal(dim(a), c(N, 1))
expect_equal(sum(a), 1.0)
b <- PortRiskContrib(r, w, percentage = FALSE)
pvar1 <- PortRisk(r, w)
expect_true(is.matrix(b))
expect_equal(dim(b), c(N, 1))
expect_equal(sum(b), pvar1)
d <- cm2(r, w, percentage = TRUE)
expect_true(is.matrix(d))
expect_equal(dim(d), c(N, 1))
expect_equal(sum(d), 1.0)
e <- cm2(r, w, percentage = FALSE)
pvar2 <- pm2(r, w)
expect_true(is.matrix(e))
expect_equal(dim(e), c(N, 1))
expect_equal(sum(e), pvar2)
})
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