data(returns)
dat <- returns[1:100, 1:2]
# fit parametric S-vine model with Markov order 1
model <- svine(dat, p = 1, family_set = "parametric")
# compute 10 bootstrap replicates of the model
boot_models <- svine_bootstrap_models(10, model)
# compute bootstrap replicates of 90%-quantile of X_1 + X_2.
mu_boot <- sapply(
boot_models,
function(m) {
xx <- rowSums(t(svine_sim(1, 10^2, m, past = dat)[1, ,]))
quantile(xx, 0.9)
}
)
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