Simulate from a S-vine copula model

svinecop_sim(n, rep, model, past = NULL, qrng = FALSE, cores = 1)

Arguments

n: how many steps of the time series to simulate.
rep: number of replications; rep time series of length n are generated.
model: a S-vine copula model object (inheriting from svinecop_dist).
past: (optional) matrix of past observations. If provided, time series are simulated conditional on the past.
qrng: if TRUE, generates quasi-random numbers using the multivariate Generalized Halton sequence up to dimension 300 and the Generalized Sobol sequence in higher dimensions (default qrng = FALSE).
cores: number of cores to use; if larger than one, computations are done parallel over replications.

Value

An n-by-d-by-rep array, where d is the cross-sectional dimension of the model. This reduces to an n-by-d matrix if rep == 1.

Examples

# load data set
data(returns)  

# convert to uniform margins
u <- pseudo_obs(returns[1:100, 1:3])

# fit parametric S-vine copula model with Markov order 1
fit <- svinecop(u, p = 1, family_set = "parametric")

pairs(u)   # original data

pairs(svinecop_sim(100, rep = 1, model = fit))   # simulated data


# simulate the next day conditionally on the past 500 times
pairs(t(svinecop_sim(1, rep = 100, model = fit, past = u)[1, , ]))