README

install.packages("svines")

# install.packages("remotes")
remotes::install_github("tnagler/svines")

Usage

Use svine() for observed data: it estimates the marginal distributions and the S-vine copula. Use svinecop() when the margins have already been transformed to approximately uniform pseudo-observations.

library(svines)
data(returns)  # data set of stock returns
returns <- returns[1:500, 1:2]

Fitting models

fit <- svine(returns, p = 1)  # Markov order 1
summary(fit)
#> $margins
#> # A data.frame: 2 x 5 
#>  margin    name          model                         parameters loglik
#>       1 Allianz Skew Student-t 0.00039, 0.01589, 5.45534, 0.91785   1382
#>       2     AXA Skew Student-t 0.00052, 0.02089, 4.35198, 0.90611   1260
#> 
#> $copula
#> # A data.frame: 5 x 10 
#>  tree edge conditioned conditioning var_types family rotation   parameters df
#>     1    1        3, 2                    c,c      t        0 0.037, 4.893  2
#>     1    2        2, 1                    c,c      t        0   0.86, 3.48  2
#>     2    1        4, 2            3       c,c    joe       90          1.1  1
#>     2    2        3, 1            2       c,c  indep        0               0
#>     3    1        4, 1         2, 3       c,c      t        0 0.079, 8.994  2
#>     tau
#>   0.023
#>   0.662
#>  -0.033
#>   0.000
#>   0.051

contour(fit$copula)

Simulation

Generate a new time series of length 500

sim <- svine_sim(n = 500, rep = 1, model = fit)
pairs(sim)

pairs(returns)

Sample conditionally on the observed past

sim <- svine_sim(n = 1, rep = 100, model = fit, past = returns)
pairs(t(sim[1, , ]))

Standard errors

To generate bootstrap replicates with the one-step block multiplier bootstrap, use

set.seed(2026)
models <- svine_bootstrap_models(2, fit)
summary(models[[1]])
#> $margins
#> # A data.frame: 2 x 5 
#>  margin    name          model                         parameters loglik
#>       1 Allianz Skew Student-t 0.00057, 0.01437, 7.22824, 0.97850     NA
#>       2     AXA Skew Student-t 0.00065, 0.01842, 5.29395, 0.98127     NA
#> 
#> $copula
#> # A data.frame: 5 x 10 
#>  tree edge conditioned conditioning var_types family rotation    parameters df
#>     1    1        3, 2                    c,c      t        0 -0.022, 5.380  2
#>     1    2        2, 1                    c,c      t        0    0.84, 2.99  2
#>     2    1        4, 2            3       c,c    joe       90             1  1
#>     2    2        3, 1            2       c,c  indep        0                0
#>     3    1        4, 1         2, 3       c,c      t        0    0.11, 6.83  2
#>     tau
#>  -0.014
#>   0.634
#>  -0.008
#>   0.000
#>   0.068

Discrete variables

Declare discrete variables through var_types and restrict their marginal families to suitable discrete distributions. The following model uses Poisson margins and a Gaussian pair-copula family.

counts <- cbind(
  claims = rpois(250, lambda = 2),
  events = rpois(250, lambda = 4)
)
fit_discrete <- svine(
  counts,
  p = 1,
  var_types = c("d", "d"),
  margin_families = "pois",
  family_set = "gaussian"
)
fit_discrete
#> 2-dimensional S-vine distribution model of order p = 1 ('svine_dist')
svine_sim(5, rep = 1, model = fit_discrete)
#>      claims events
#> [1,]      3      4
#> [2,]      0      5
#> [3,]      0      1
#> [4,]      3      5
#> [5,]      1      1

svine() constructs the required CDF and left-limit CDF values automatically. Users calling svinecop() directly must supply all regular F(x) columns, followed by one F(x-) column for each discrete variable.

References

Nagler, T., Krüger, D., and Min, A. (2022). Stationary vine copula models for multivariate time series. Journal of Econometrics, 227(2), pp. 305-324 [pdf] [doi]

svines

Installation