tests/testthat/test_risk.R

In bpfaff/mcrp: Multiple criteria risk parity optimization

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)
})