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tests/longtests/test-prediction.R
test_that("prediction long-run: GARCH(1,1)",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "garch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
})
test_that("prediction long-run: GARCH(2,1)",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "garch",
order = c(2,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
})
test_that("prediction long-run: EGARCH(1,1)-NORM",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "egarch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 1000, seed = 100, burn = 1000)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.0001)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2), trim = 0.01), u, tolerance = 0.01)
})
test_that("prediction long-run: EGARCH(1,1)-GHST",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "egarch",
order = c(1,1), distribution = "ghst")
local_mod_garch <- suppressWarnings(estimate(local_spec_garch))
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 1000, seed = 100, burn = 1000)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.0001)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2), trim = 0.01), u, tolerance = 0.01)
})
test_that("prediction long-run: EGARCH(1,1)-GH",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "egarch",
order = c(1,1), distribution = "gh")
local_mod_garch <- estimate(local_spec_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 1000, seed = 100, burn = 1000)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.0001)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2), trim = 0.01), u, tolerance = 0.01)
})
test_that("prediction long-run: EGARCH(2,1)-NORM",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "egarch",
order = c(2,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 1000, seed = 100, burn = 1000)
p <- predict(local_mod_garch, h = 4000, nsim = 0, seed = 500)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.01)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2)), u, tolerance = 0.01)
})
test_that("prediction long-run: APARCH(1,1)-NORM",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "aparch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
delta <- coef(local_mod_garch)["delta"]
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.001)
})
test_that("prediction long-run: APARCH(1,1)-GH",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "aparch",
order = c(1,1), distribution = "gh")
local_mod_garch <- estimate(local_spec_garch)
delta <- coef(local_mod_garch)["delta"]
p <- predict(local_mod_garch, h = 1500)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.01)
})
test_that("prediction long-run: APARCH(1,1)-JSU",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "aparch",
order = c(1,1), distribution = "jsu")
local_mod_garch <- estimate(local_spec_garch)
delta <- coef(local_mod_garch)["delta"]
p <- predict(local_mod_garch, h = 1500)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.01)
})
test_that("prediction long-run: APARCH(2,1)",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "aparch",
order = c(2,1), distribution = "norm")
local_spec_garch$parmatrix[parameter == "alpha2", lower := -0.2]
local_spec_garch$parmatrix[parameter == "gamma2", lower := -0.2]
local_mod_garch <- estimate(local_spec_garch)
delta <- coef(local_mod_garch)["delta"]
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.001)
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.01)
})
test_that("prediction long-run: CGARCH(1,1)",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "cgarch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1500)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 3000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), unname(u), tolerance = 0.0001)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2)), unname(u), tolerance = 0.01)
})
test_that("prediction long-run: CGARCH(2,1)",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "cgarch",
order = c(2,1))
local_spec_garch$parmatrix[parameter == "alpha1", lower := -0.5]
local_spec_garch$parmatrix[parameter == "alpha2", lower := -0.5]
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1500)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 3000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), unname(u), tolerance = 0.0001)
expect_equal(mean(rowMeans(local_mod_sim$sigma^2)), unname(u), tolerance = 0.01)
})
test_that("prediction long-run: FGARCH(1,1)-NORM",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "fgarch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
delta <- coef(local_mod_garch)["delta"]
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
delta <- coef(local_mod_garch)["delta"]
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.01)
})
test_that("prediction long-run: FGARCH(1,1)-SSTD",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "fgarch",
order = c(1,1), distribution = "sstd")
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1500)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 8000, seed = 800, burn = 2000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
delta <- coef(local_mod_garch)["delta"]
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^delta))^(2/delta))
expect_equal(sim_u, u, tolerance = 0.03)
})
test_that("prediction long-run: GJR(1,1)-NORM",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "gjrgarch",
order = c(1,1), distribution = "norm")
local_mod_garch <- estimate(local_spec_garch)
p <- predict(local_mod_garch, h = 1000)
u <- unconditional(local_mod_garch)
spec_copy <- local_spec_garch
spec_copy$parmatrix <- copy(local_mod_garch$parmatrix)
local_mod_sim <- simulate(spec_copy, nsim = 1000, h = 5000, seed = 100, burn = 1000)
expect_equal(as.numeric(tail(p$sigma^2,1)), u)
sim_u <- unname(mean(rowMeans(local_mod_sim$sigma^2)))
expect_equal(sim_u, u, tolerance = 0.001)
})
test_that("prediction long-run: GJR(1,1)-SSTD",{
local_spec_garch <- garch_modelspec(y[1:1800,1], constant = TRUE, model = "gjrgarch",
order = c(1,1), distribution = "sstd")
local_mod_garch <- estimate(local_spec_garch, stationarity_constraint = 0.97)
p <- predict(local_mod_garch, h = 4500)
u <- unconditional(local_mod_garch)
expect_equal(as.numeric(tail(p$sigma^2,1)), u, tolerance = 0.00001)
})
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