Package 'svines' reference manual

Title:	Stationary Vine Copula Models
Description:	Provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.
Authors:	Thomas Nagler [aut, cre]
Maintainer:	Thomas Nagler <[email protected]>
License:	GPL-3
Version:	0.2.4
Built:	2025-02-10 06:30:57 UTC
Source:	https://github.com/tnagler/svines

Stock returns of 20 companies

Description

A dataset containing the log-returns of daily returns of 20 companies. The observation period is from 2015-01-01 to 2019-12-31.

Usage

returns
returns

Format

A data frame with 1296 rows and 20 variables:

Source

Yahoo finance.

Stationary vine distribution models

Description

Automated fitting or creation of custom S-vine distribution models

Usage

svine(
  data,
  p,
  margin_families = univariateML::univariateML_models,
  selcrit = "aic",
  ...
)
svine(
  data,
  p,
  margin_families = univariateML::univariateML_models,
  selcrit = "aic",
  ...
)

Arguments

`data`	a matrix or data.frame of data.
`p`	the Markov order.
`margin_families`	either a vector of univariateML::univariateML_models to select from (used for every margin) or a list with one entry for every variable. Can also be `"empirical"` for empirical cdfs.
`selcrit`	criterion for family selection, either `"loglik"`, `"aic"`, `"bic"`, `"mbicv"`.
`...`	arguments passed to `svinecop()`.

Value

Returns the fitted model as an object with classes svine and svine_dist. A list with entries

⁠$margins⁠: list of marginal models from univariateML::univariateML_models,
⁠$copula⁠: an object of svinecop_dist.

Examples

# load data set
data(returns)  

# fit parametric S-vine model with Markov order 1
fit <- svine(returns[1:100, 1:3], p = 1, family_set = "parametric")
fit 
summary(fit)
plot(fit$copula)
contour(fit$copula)
logLik(fit)

pairs(svine_sim(500, rep = 1, fit))
# load data set
data(returns)  

# fit parametric S-vine model with Markov order 1
fit <- svine(returns[1:100, 1:3], p = 1, family_set = "parametric")
fit 
summary(fit)
plot(fit$copula)
contour(fit$copula)
logLik(fit)

pairs(svine_sim(500, rep = 1, fit))

Bootstrap S-vine models

Description

Computes bootstrap replicates of a given model using the one-step block multiplier bootstrap of Nagler et. al (2022).

Usage

svine_bootstrap_models(n_models, model)
svine_bootstrap_models(n_models, model)

Arguments

`n_models`	number of bootstrap replicates.
`model`	the initial fitted model

Value

A list of length n_models, with each entry representing one bootstrapped model as object of class svine.

Examples

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)
  }
) 
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)
  }
)

Custom S-vine distribution models

Description

Custom S-vine distribution models

Usage

svine_dist(margins, copula)
svine_dist(margins, copula)

Arguments

`margins`	A list of length `d` containing `univariateML` objects.
`copula`	the copula model; an object of class `svinecop_dist` with cross-sectional dimension `d`.

Value

Returns the model as an object with class svine_dist. A list with entries

⁠$margins⁠: list of marginal models from univariateML::univariateML_models,
⁠$copula⁠: an object of svinecop_dist.

Examples

## marginal objects
# create dummy univariateML models
univ1 <- univ2 <- univariateML::mlnorm(rnorm(10))

# modify the parameters to N(5, 10) and N(0, 2) distributions
univ1[] <- c(5, 10)
univ2[] <- c(0, 2)

## copula óbject
cs_struct <- cvine_structure(1:2)
pcs <- list(
  list(  # first tree
    bicop_dist("clayton", 0, 3), # cross sectional copula
    bicop_dist("gaussian", 0, -0.1)  # serial copula
  ),
  list(  # second tree
    bicop_dist("gaussian", 0, 0.2), bicop_dist("indep")  
  ),
  list( # third tree
    bicop_dist("indep")
  )
)

cop <- svinecop_dist(
  pcs, cs_struct, p = 1, out_vertices = 1:2, in_vertices = 1:2)
    
model <- svine_dist(margins = list(univ1, univ2), copula = cop)
summary(model)

## marginal objects
# create dummy univariateML models
univ1 <- univ2 <- univariateML::mlnorm(rnorm(10))

# modify the parameters to N(5, 10) and N(0, 2) distributions
univ1[] <- c(5, 10)
univ2[] <- c(0, 2)

## copula óbject
cs_struct <- cvine_structure(1:2)
pcs <- list(
  list(  # first tree
    bicop_dist("clayton", 0, 3), # cross sectional copula
    bicop_dist("gaussian", 0, -0.1)  # serial copula
  ),
  list(  # second tree
    bicop_dist("gaussian", 0, 0.2), bicop_dist("indep")  
  ),
  list( # third tree
    bicop_dist("indep")
  )
)

cop <- svinecop_dist(
  pcs, cs_struct, p = 1, out_vertices = 1:2, in_vertices = 1:2)
    
model <- svine_dist(margins = list(univ1, univ2), copula = cop)
summary(model)

Expected hessian of a parametric S-vine models

Description

Expected hessian of a parametric S-vine models

Usage

svine_hessian(x, model, cores = 1)
svine_hessian(x, model, cores = 1)

Arguments

`x`	the data.
`model`	S-vine model (inheriting from svine_dist).
`cores`	number of cores to use.

Value

A returns a k-by-k matrix, where k is the total number of parameters in the model. Parameters are ordered as follows: marginal parameters, copula parameters of first tree, copula parameters of second tree, etc. Duplicated parameters in the copula model are omitted.

Examples

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")

# Implementation of asymptotic variances
I <- cov(svine_scores(dat, model))
H <- svine_hessian(dat, model)
Hi <- solve(H)
Hi %*% I %*% t(Hi) / nrow(dat)
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")

# Implementation of asymptotic variances
I <- cov(svine_scores(dat, model))
H <- svine_hessian(dat, model)
Hi <- solve(H)
Hi %*% I %*% t(Hi) / nrow(dat)

Log-likelihood for S-vine models

Description

Log-likelihood for S-vine models

Usage

svine_loglik(x, model, cores = 1)
svine_loglik(x, model, cores = 1)

Arguments

`x`	the data.
`model`	model inheriting from class svine_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

Returns the log-likelihood of the data for the model.

Examples

# load data set
data(returns)  

# fit parametric S-vine model with Markov order 1
fit <- svine(returns[1:100, 1:3], p = 1, family_set = "parametric")

svine_loglik(returns[1:100, 1:3], fit)
# load data set
data(returns)  

# fit parametric S-vine model with Markov order 1
fit <- svine(returns[1:100, 1:3], p = 1, family_set = "parametric")

svine_loglik(returns[1:100, 1:3], fit)

Pseudo-residuals of S-vine models

Description

Pseudo-residuals are defined as the Rosenblatt transform of the data, conditional on the past. Under a correctly specified model, they are approximately iid uniform on $[0, 1]^d$ .

Usage

svine_pseudo_residuals(x, model, cores = 1)
svine_pseudo_residuals(x, model, cores = 1)

Arguments

`x`	the data.
`model`	model inheriting from class svine_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

Returns a multivariate time series of pseudo-residuals

Examples

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")

# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svinecop_pseudo_residuals(u, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")

# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svinecop_pseudo_residuals(u, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))

Score function of parametric S-vine models

Description

Score function of parametric S-vine models

Usage

svine_scores(x, model, cores = 1)
svine_scores(x, model, cores = 1)

Arguments

`x`	the data.
`model`	S-vine model (inheriting from svine_dist).
`cores`	number of cores to use.

Value

A returns a n-by-k matrix, where n = NROW(x) and k is the total number of parameters in the model. Parameters are ordered as follows: marginal parameters, copula parameters of first tree, copula parameters of second tree, etc. Duplicated parameters in the copula model are omitted.

Examples

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")

# Implementation of asymptotic variances
I <- cov(svine_scores(dat, model))
H <- svine_hessian(dat, model)
Hi <- solve(H)
Hi %*% I %*% t(Hi) / nrow(dat)
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")

# Implementation of asymptotic variances
I <- cov(svine_scores(dat, model))
H <- svine_hessian(dat, model)
Hi <- solve(H)
Hi %*% I %*% t(Hi) / nrow(dat)

Simulate from a S-vine model

Description

Simulate from a S-vine model

Usage

svine_sim(n, rep, model, past = NULL, qrng = FALSE, cores = 1)
svine_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-byrep 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)  
returns <- returns[1:100, 1:3]

# fit parametric S-vine model with Markov order 1
fit <- svine(returns, p = 1, family_set = "parametric")

pairs(returns)  # original data
pairs(svine_sim(100, rep = 1, model = fit))   # simulated data

# simulate the next day conditionally on the past 500 times
pairs(t(svine_sim(1, rep = 100, model = fit, past = returns)[1, , ]))
# load data set
data(returns)  
returns <- returns[1:100, 1:3]

# fit parametric S-vine model with Markov order 1
fit <- svine(returns, p = 1, family_set = "parametric")

pairs(returns)  # original data
pairs(svine_sim(100, rep = 1, model = fit))   # simulated data

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

Stationary vine copula models

Description

Automated fitting or creation of custom S-vine copula models

Usage

svinecop(
  data,
  p,
  var_types = rep("c", NCOL(data)),
  family_set = "all",
  cs_structure = NA,
  out_vertices = NA,
  in_vertices = NA,
  type = "S",
  par_method = "mle",
  nonpar_method = "constant",
  mult = 1,
  selcrit = "aic",
  weights = numeric(),
  psi0 = 0.9,
  presel = TRUE,
  trunc_lvl = Inf,
  tree_crit = "tau",
  threshold = 0,
  keep_data = FALSE,
  show_trace = FALSE,
  cores = 1
)
svinecop(
  data,
  p,
  var_types = rep("c", NCOL(data)),
  family_set = "all",
  cs_structure = NA,
  out_vertices = NA,
  in_vertices = NA,
  type = "S",
  par_method = "mle",
  nonpar_method = "constant",
  mult = 1,
  selcrit = "aic",
  weights = numeric(),
  psi0 = 0.9,
  presel = TRUE,
  trunc_lvl = Inf,
  tree_crit = "tau",
  threshold = 0,
  keep_data = FALSE,
  show_trace = FALSE,
  cores = 1
)

Arguments

`data`	a matrix or data.frame (copula data should have approximately uniform margins).
`p`	the Markov order.
`var_types`	variable types; discrete variables not (yet) allowed.
`family_set`	a character vector of families; see `rvinecopulib::bicop()` for additional options.
`cs_structure`	the cross-sectional vine structure (see `rvinecopulib::rvine_structure()`; `cs_structure = NA` performs automatic structure selection.
`out_vertices`	the out-vertex; if `NA`, the out-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
`in_vertices`	the in-vertex; if `NA`, the in-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
`type`	type of stationary vine; `"S"` (default) for general S-vines, `"D"` for Smith's long D-vine, `"M"` for Beare and Seo's M-vine.
`par_method`	the estimation method for parametric models, either `"mle"` for sequential maximum likelihood, `"itau"` for inversion of Kendall's tau (only available for one-parameter families and `"t"`.
`nonpar_method`	the estimation method for nonparametric models, either `"constant"` for the standard transformation estimator, or `"linear"`/`"quadratic"` for the local-likelihood approximations of order one/two.
`mult`	multiplier for the smoothing parameters of nonparametric families. Values larger than 1 make the estimate more smooth, values less than 1 less smooth.
`selcrit`	criterion for family selection, either `"loglik"`, `"aic"`, `"bic"`, `"mbic"`. For `vinecop()` there is the additional option `"mbicv"`.
`weights`	optional vector of weights for each observation.
`psi0`	prior probability of a non-independence copula (only used for `selcrit = "mbic"` and `selcrit = "mbicv"`).
`presel`	whether the family set should be thinned out according to symmetry characteristics of the data.
`trunc_lvl`	currently unsupported.
`tree_crit`	the criterion for tree selection, one of `"tau"`, `"rho"`, `"hoeffd"`, or `"mcor"` for Kendall's $\tau$ , Spearman's $\rho$ , Hoeffding's $D$ , and maximum correlation, respectively.
`threshold`	for thresholded vine copulas; `NA` indicates that the threshold should be selected automatically by `rvinecopulib::mBICV()`.
`keep_data`	whether the data should be stored (necessary for using `fitted()`).
`show_trace`	logical; whether a trace of the fitting progress should be printed.
`cores`	number of cores to use; if more than 1, estimation of pair copulas within a tree is done in parallel.

Value

Returns the fitted model as an object with classes svinecop and svinecop_dist. Also inherits from vinecop, vinecop_dist such that many functions from rvinecopulib can be called.

Examples

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")
fit 
summary(fit)
plot(fit)
contour(fit)
logLik(fit)

pairs(svinecop_sim(500, rep = 1, fit))
# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")
fit 
summary(fit)
plot(fit)
contour(fit)
logLik(fit)

pairs(svinecop_sim(500, rep = 1, fit))

Custom S-vine models

Description

Custom S-vine models

Usage

svinecop_dist(
  pair_copulas,
  cs_structure,
  p,
  out_vertices,
  in_vertices,
  var_types = rep("c", dim(cs_structure)[1])
)
svinecop_dist(
  pair_copulas,
  cs_structure,
  p,
  out_vertices,
  in_vertices,
  var_types = rep("c", dim(cs_structure)[1])
)

Arguments

`pair_copulas`	A nested list of 'bicop_dist' objects, where `pair_copulas[[t]][[e]]` corresponds to the pair-copula at edge `e` in tree `t`. Only the most-left unique pair copulas are used, others can be omitted.
`cs_structure`	The cross-sectional structure. Either a matrix, or an `rvine_structure` object; see `rvinecopulib::rvine_structure()`
`p`	the Markov order.
`out_vertices`	the out-vertex; if `NA`, the out-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
`in_vertices`	the in-vertex; if `NA`, the in-vertex is selected automatically if no structure is provided, and is equivalent to 1 if a structure is provided.
`var_types`	variable types; discrete variables not (yet) allowed.

Value

Returns the model as an object with classes svinecop_dist. Also inherits from vinecop_dist such that many functions from rvinecopulib can be called.

Examples

cs_struct <- cvine_structure(1:2)
pcs <- list(
  list(  # first tree
    bicop_dist("clayton", 0, 3), # cross sectional copula
    bicop_dist("gaussian", 0, -0.1)  # serial copula
  ),
  list(  # second tree
    bicop_dist("gaussian", 0, 0.2), bicop_dist("indep")  
  ),
  list( # third tree
    bicop_dist("indep")
  )
)

cop <- svinecop_dist(
  pcs, cs_struct, p = 1, out_vertices = 1:2, in_vertices = 1:2)
cs_struct <- cvine_structure(1:2)
pcs <- list(
  list(  # first tree
    bicop_dist("clayton", 0, 3), # cross sectional copula
    bicop_dist("gaussian", 0, -0.1)  # serial copula
  ),
  list(  # second tree
    bicop_dist("gaussian", 0, 0.2), bicop_dist("indep")  
  ),
  list( # third tree
    bicop_dist("indep")
  )
)

cop <- svinecop_dist(
  pcs, cs_struct, p = 1, out_vertices = 1:2, in_vertices = 1:2)

Expected hessian for S-vine copula models

Description

Expected hessian for S-vine copula models

Usage

svinecop_hessian(u, model, cores = 1)
svinecop_hessian(u, model, cores = 1)

Arguments

`u`	the data; should have approximately uniform margins..
`model`	model inheriting from class svinecop_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

Returns the observed Hessian matrix. Rows/columns correspond to to model parameters in the order: copula parameters of first tree, copula parameters of second tree, etc. Duplicated parameters in the copula model are omitted.

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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)
# 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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)

Log-likelihood for S-vine copula models

Description

Log-likelihood for S-vine copula models

Usage

svinecop_loglik(u, model, cores = 1)
svinecop_loglik(u, model, cores = 1)

Arguments

`u`	the data; should have approximately uniform margins.
`model`	model inheriting from class svinecop_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

Returns the log-likelihood of the data for the model.

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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)
# 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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)

Pseudo-residuals of S-vine copula models

Description

Pseudo-residuals are defined as the Rosenblatt transform of the data, conditional on the past. Under a correctly specified model, they are approximately iid uniform on $[0, 1]^d$ .

Usage

svinecop_pseudo_residuals(u, model, cores = 1)
svinecop_pseudo_residuals(u, model, cores = 1)

Arguments

`u`	the data; should have approximately uniform margins.
`model`	model inheriting from class svinecop_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

Returns a multivariate time series of pseudo-residuals

Examples

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")

# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svinecop_pseudo_residuals(u, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))

# load data set
data(returns)  

# convert to pseudo observations with empirical cdf for marginal distributions
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")

# compute pseudo-residuals
# (should be independent uniform across variables and time)
v <- svinecop_pseudo_residuals(u, fit)
pairs(cbind(v[-1, ], v[-nrow(v), ]))

Log-likelihood scores for S-vine copula models

Description

Log-likelihood scores for S-vine copula models

Usage

svinecop_scores(u, model, cores = 1)
svinecop_scores(u, model, cores = 1)

Arguments

`u`	the data; should have approximately uniform margins..
`model`	model inheriting from class svinecop_dist.
`cores`	number of cores to use; if larger than one, computations are done in parallel on `cores` batches .

Value

A matrix containing the score vectors in its rows, where each row corresponds to one observation (row in u). The columns correspond to model parameters in the order: copula parameters of first tree, copula parameters of second tree, etc. Duplicated parameters in the copula model are omitted.

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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)
# 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")

svinecop_loglik(u, fit)
svinecop_scores(u, fit)
svinecop_hessian(u, fit)

Simulate from a S-vine copula model

Description

Simulate from a S-vine copula model

Usage

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

Package 'svines'

Help Index

Stock returns of 20 companies

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Custom S-vine models

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Expected hessian for S-vine copula models

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See Also

Examples

Log-likelihood for S-vine copula models

Description

Usage