Package 'portvine' reference manual

Title:	Vine Based (Un)Conditional Portfolio Risk Measure Estimation
Description:	Following Sommer (2022) <https://mediatum.ub.tum.de/1658240> portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates.
Authors:	Emanuel Sommer [cre, aut]
Maintainer:	Emanuel Sommer <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.3.9000
Built:	2025-02-15 03:56:03 UTC
Source:	https://github.com/emanuelsommer/portvine

Default specifications for ARMA-GARCH models

Description

This function is used as the default for the univariate model fitting i.e. the marginal models and can be used to easily specify a different individual marginal model specification or default in marginal_settings(). The ARMA(ar,ma)-GARCH(arch,garch) is fitted with the distribution given by dist that specifies the conditional density used for the innovations.

Usage

default_garch_spec(ar = 1, ma = 1, arch = 1, garch = 1, dist = "sstd")
default_garch_spec(ar = 1, ma = 1, arch = 1, garch = 1, dist = "sstd")

Arguments

`ar`	integer for the autoregressive order
`ma`	integer for the moving average order
`arch`	integer for the ARCH order
`garch`	integer for the GARCH order
`dist`	a single character value of the possible distributions allowed in `rugarch::ugarchspec`

Value

object of class rugarch::ugarchspec

Examples

# the default is then just using
default_garch_spec()
# to specify a ARMA(2,2)-GARCH(1,1) model with normal residual distribution
default_garch_spec(ar = 2, ma = 2, dist = "norm")
# the default is then just using
default_garch_spec()
# to specify a ARMA(2,2)-GARCH(1,1) model with normal residual distribution
default_garch_spec(ar = 2, ma = 2, dist = "norm")

Estimate the Expected Shortfall (ES)

Description

The Expected Shortfall at level $\alpha$ is defined as the expected value of the returns under the condition that the returns are smaller than the Value at Risk for the same $\alpha$ level. Note that an absolutely continuous distribution of the returns is assumed. The three estimation methods are:

mean the mean of the samples that fall under the corresponding VaR.
median the median of the samples that fall under the corresponding VaR.
mc Calculation of the expected value using Monte Carlo integration over the $\alpha$ levels. One draws mc_samples Monte Carlo samples .

Usage

est_es(sample, alpha, method = c("mean", "median", "mc"), mc_samples = 100)
est_es(sample, alpha, method = c("mean", "median", "mc"), mc_samples = 100)

Arguments

`sample`	Numeric vector representing the sample upon which the Expected Shortfall is calculated.
`alpha`	Numeric vector with entries in (0,1) specifying the levels at which the ES is calculated.
`method`	Method of estimation one of `mean`, `median`, `mc`. For more information see the Description section.
`mc_samples`	Number of Monte Carlo samples used for the `mc` method.

Value

Numeric vector with Expected Shortfall estimates (same length as alpha).

Examples

est_es(0:100, c(0.1, 0.2, 0.3))
est_es(0:100, c(0.1, 0.2, 0.3))

Estimate the Value at Risk (VaR)

Description

The VaR is defined as the empirical $\alpha$ level quantile of the empirical distribution based on a return sample.

Usage

est_var(sample, alpha)
est_var(sample, alpha)

Arguments

`sample`	Numeric vector representing the sample upon which the Value at Risk is calculated.
`alpha`	Numeric vector with entries in (0,1) specifying the levels at which the VaR is calculated.

Value

Numeric vector with VaR estimates (same length as alpha).

Examples

est_var(0:100, c(0.1, 0.2, 0.3))
est_var(0:100, c(0.1, 0.2, 0.3))

(Un-)conditional rolling risk estimation using vine copulas

Description

As this is the main workhorse function with a lot going on under the hood it is advised to have a look at the vignettes or even better the package website as they provide a detailed hands on and theoretical documentation of what this function is doing and how it is intended to be used. For a short summarized explanation have a look at the Details section below.

Usage

estimate_risk_roll(
  data,
  weights = NULL,
  marginal_settings,
  vine_settings,
  alpha = 0.05,
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 1000,
  cond_vars = NULL,
  cond_u = 0.05,
  n_mc_samples = 1000,
  trace = FALSE,
  cutoff_depth = NULL,
  prior_resid_strategy = FALSE
)
estimate_risk_roll(
  data,
  weights = NULL,
  marginal_settings,
  vine_settings,
  alpha = 0.05,
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 1000,
  cond_vars = NULL,
  cond_u = 0.05,
  n_mc_samples = 1000,
  trace = FALSE,
  cutoff_depth = NULL,
  prior_resid_strategy = FALSE
)

Arguments

`data`	Matrix, data.frame or other object coercible to a data.table storing the numeric asset returns in the named columns (at least 3). Moreover missing values must be imputed beforehand.
`weights`	Corresponding named non-negative weights of the assets (conditioning variables must have weight 0). Default `NULL` gives equal weight of 1 to each non conditional asset. Alternatively one can use a matrix with as many rows as vine windows for changing weights. The matrix must have column names corresponding to the assets and conditional assets have to have weight 0.
`marginal_settings`	`marginal_settings` S4 object containing the needed information for the ARMA-GARCH i.e. marginal models fitting. Note that the `marginal_settings` and `vine_settings` objects have to match as described further below.
`vine_settings`	`vine_settings` S4 object containing the needed information for the vine copula model fitting. Note that the `marginal_settings` and `vine_settings` objects have to match as described further below.
`alpha`	Numeric vector specifying the confidence levels in (0,1) at which the risk measures should be calculated.
`risk_measures`	Character vector with valid choices for risk measures to estimate. Currently available are the Value at Risk `VaR` which is implemented in `est_var()` and 3 estimation methods of the Expected Shortfall `ES_mean`, `ES_median` and `ES_mc` all implemented in `est_es()` .
`n_samples`	Positive count of samples to be used at the base of the risk measure estimation.
`cond_vars`	Names of the variables to sample conditionally from (currently $\le 2$ variables).
`cond_u`	Numeric vector specifying the corresponding quantiles in (0,1) of the conditional variable(s) conditioned on which the conditional risk measures should be calculated. Additionally always the conditioning values corresponding to the residual of one time unit prior are used as conditional variables (`cond_u` = 'prior_resid' in the risk measure output) if the flag`prior_resid` is set to TRUE, otherwise the conditioning values corresponding to the realized residual are used (`cond_u` = 'resid' in the risk measure output). The latter case corresponds to the default.
`n_mc_samples`	Positive count of samples for the Monte Carlo integration if the risk measure `ES_mc` is used. (See `est_es()`)
`trace`	If set to TRUE the algorithm will print a little information while running.
`cutoff_depth`	Positive count that specifies the depth up to which the edges of the to be constructed D-vine copula are considered in the algorithm that determines the ordering for the D-vine fitting using partial correlations. The default `NULL` considers all edges and seems in most use cases reasonable. This argument is only relevant if D-vines are used.
`prior_resid_strategy`	Logical flag that indicates whether as the additionally used conditioning values the prior day residual (if this flag is TRUE) or the realized residuals are used. The default are the realized residuals. Note that the resulting conditional risk measures use realized data so they are only for comparisons as they suffer from information leakage.

Details

Roughly speaking the function performs the following steps for the unconditional risk measure estimation:

Fit for each asset marginal time series models i.e. ARMA-GARCH models in a rolling window fashion. The models as well as the rolling window size and training size are specified via the marginal_settings argument.
Model the dependence between the assets with a vine copula model trained on the standardized residuals transformed to the copula scale via the probability integral transform. This is also performed in a rolling window fashion where one can use the same window size for the vine windows as used for the marginal ones or a smaller window size. This window size, the training size for the vine copula as well as the copula fitting arguments are specified via the vine_settings argument.
Using the copula and the forecasted means and volatilities of the assets one simulates n_samples many forecasted portfolio level log returns for every time unit in every specified rolling window.
Based on these samples one estimates portfolio level risk measures.

Additionally one can perform conditional risk measure estimation with up to two conditional log return series like market indices. Using this approach does not change the marginal models part but for the copula a D-vine with a special ordering i.e. the index or the indices are fixed as the rightmost leafs is fitted. One then simulates conditional forecasted portfolio log returns which then results in conditional risk measure estimates that can be particularly interesting in stress testing like situations. One conditions on a pre-specified quantile level (cond_u) of the conditioning assets (cond_vars) and for comparison one also conditions either on the behavior of the conditioning asset one time unit before (prior_resid_strategy = TRUE) or the realized behavior of the conditioning asset (prior_resid_strategy = FALSE).

Value

In the unconditional case an S4 object of class portvine_roll and in the conditional case its child class cond_portvine_roll. For details see portvine_roll.

Matching marginal and vine settings

First of all there must be at least 2 marginal windows. Thus train_size + refit_size slot in the marginal_settings class object must be smaller than the overall input data size. Moreover the refit_size of the marginal models must be dividable by the refit_size of the vine copula models e.g. possible combinations are 50 and 50, 50 and 25, 50 and 10. Furthermore the train_size of the vines must be smaller or equal to the train_size of the marginal models.

Parallel processing

This function uses the future framework for parallelization that allows maximum flexibility for the user while having safe speedups for example regarding random number generation. The default is of course the standard non parallel sequential evaluation. The user has to do nothing in order for this default to work. If the user wants to run the code in parallel there are many options from parallel on a single machine up to a high performance compute (HPC) cluster, all of this with just one setting switch i.e. by calling the function future::plan() with the respective argument before the function call. Common options are future::plan("multisession") which works on all major operating systems and uses all available cores to run the code in parallel local R sessions. To specify the number of workers use future::plan("multisession", workers = 2). To go back to sequential processing and to shut down the parallel sessions use future::plan("sequential"). For more information have a look at future::plan(). The two following loops are processed in parallel by default if a parallel future::plan() is set:

The marginal model fitting i.e. all assets individually in parallel.
The vine windows i.e. the risk estimates and the corresponding vine copula models are computed in parallel for each rolling vine window.

In addition the function allows for nested parallelization which has to be done with care. So in addition to the 2 loops above one can further run each computation for each time unit in the vine windows in parallel which might be especially interesting if the n_samples argument is large. Then the default parallelization has to be tweaked to not only parallelize the first level of parallelization which are the 2 loops above. This can be achieved e.g. via future::plan(list(future::tweak(future::multisession, workers = 4), future::tweak(future::multisession, workers = 2))). This setting would run the 2 primary loops in 4 parallel R sessions and in addition each of the 4 primary parallel sessions would itself use 2 sessions within the nested parallel loop over the time units in the vine window. This results in a need for at least 2 times 4 so 8 threads on the hardware side. More details can be found in the extensive documentation of the future framework.

Author(s)

Emanuel Sommer

Examples

# For better illustrated examples have a look at the vignettes
# and/or the package website.

data("sample_returns_small")
ex_marg_settings <- marginal_settings(
  train_size = 900,
  refit_size = 50
)
ex_vine_settings <- vine_settings(
  train_size = 100,
  refit_size = 50,
  family_set = c("gaussian", "gumbel"),
  vine_type = "dvine"
)
# unconditionally
risk_roll <- estimate_risk_roll(
  sample_returns_small,
  weights = NULL,
  marginal_settings = ex_marg_settings,
  vine_settings = ex_vine_settings,
  alpha = c(0.01, 0.05),
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 10,
  trace = FALSE
)
# conditional on one asset
risk_roll_cond <- estimate_risk_roll(
  sample_returns_small,
  weights = NULL,
  marginal_settings = ex_marg_settings,
  vine_settings = ex_vine_settings,
  alpha = c(0.01, 0.05),
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 10,
  cond_vars = "GOOG",
  cond_u = c(0.05, 0.5),
  trace = FALSE,
  prior_resid_strategy = TRUE
)

# have a superficial look
risk_roll_cond
# a slightly more detailed look
summary(risk_roll_cond)

# actually use the results by extracting important fitted quantities
fitted_vines(risk_roll_cond)
fitted_marginals(risk_roll_cond)

# and of course most importantly the risk measure estimates
risk_estimates(
  risk_roll,
  risk_measures = "ES_mean",
  alpha = 0.05, exceeded = TRUE
)
risk_estimates(
  risk_roll_cond,
  risk_measures = "ES_mean",
  alpha = 0.05, exceeded = TRUE,
  cond_u = c("prior_resid", 0.5)
)

# For better illustrated examples have a look at the vignettes
# and/or the package website.

data("sample_returns_small")
ex_marg_settings <- marginal_settings(
  train_size = 900,
  refit_size = 50
)
ex_vine_settings <- vine_settings(
  train_size = 100,
  refit_size = 50,
  family_set = c("gaussian", "gumbel"),
  vine_type = "dvine"
)
# unconditionally
risk_roll <- estimate_risk_roll(
  sample_returns_small,
  weights = NULL,
  marginal_settings = ex_marg_settings,
  vine_settings = ex_vine_settings,
  alpha = c(0.01, 0.05),
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 10,
  trace = FALSE
)
# conditional on one asset
risk_roll_cond <- estimate_risk_roll(
  sample_returns_small,
  weights = NULL,
  marginal_settings = ex_marg_settings,
  vine_settings = ex_vine_settings,
  alpha = c(0.01, 0.05),
  risk_measures = c("VaR", "ES_mean"),
  n_samples = 10,
  cond_vars = "GOOG",
  cond_u = c(0.05, 0.5),
  trace = FALSE,
  prior_resid_strategy = TRUE
)

# have a superficial look
risk_roll_cond
# a slightly more detailed look
summary(risk_roll_cond)

# actually use the results by extracting important fitted quantities
fitted_vines(risk_roll_cond)
fitted_marginals(risk_roll_cond)

# and of course most importantly the risk measure estimates
risk_estimates(
  risk_roll,
  risk_measures = "ES_mean",
  alpha = 0.05, exceeded = TRUE
)
risk_estimates(
  risk_roll_cond,
  risk_measures = "ES_mean",
  alpha = 0.05, exceeded = TRUE,
  cond_u = c("prior_resid", 0.5)
)

Accessor method for the fitted marginal models of `⁠(cond_)portvine_roll⁠` objects

Description

Extract the marginal models that are ARMA-GARCH models which were fitted in a rolling window fashion using rugarch::ugarchroll. For the residual analysis of the models encompassed in such a rugarch::ugarchroll class object one can have a look at the utility function roll_residuals().

Usage

fitted_marginals(roll, ...)

## S4 method for signature 'portvine_roll'
fitted_marginals(roll)
fitted_marginals(roll, ...)

## S4 method for signature 'portvine_roll'
fitted_marginals(roll)

Arguments

`roll`	Object of class `portvine_roll` or a child class
`...`	Additional parameters for child class methods

Value

Named list with an entry for each asset containing a rugarch::ugarchroll class object that encompasses the marginal model fit.

Accessor method for the fitted vine copula models of `⁠(cond_)portvine_roll⁠` objects

Description

Accessor method for the fitted vine copula models of ⁠(cond_)portvine_roll⁠ objects

Usage

fitted_vines(roll, ...)

## S4 method for signature 'portvine_roll'
fitted_vines(roll)
fitted_vines(roll, ...)

## S4 method for signature 'portvine_roll'
fitted_vines(roll)

Arguments

`roll`	Object of class `portvine_roll` or a child class
`...`	Additional parameters for child class methods

Value

List of rvinecopulib::vinecop class objects each entry corresponds to one fitted vine copula model for the respective vine window.

S4 class for the marginal settings

Description

Specify which marginal models (individual_spec & default_specs) are fitted and how often they are refit as well as how big the training data set is. Remember that the forecasting is done in a rolling window fashion and the arguments (train and refit size) will have to match with the arguments of the also to be specified vine_settings.

Usage

marginal_settings(
  train_size,
  refit_size,
  individual_spec = list(),
  default_spec = default_garch_spec()
)

## S4 method for signature 'marginal_settings'
show(object)
marginal_settings(
  train_size,
  refit_size,
  individual_spec = list(),
  default_spec = default_garch_spec()
)

## S4 method for signature 'marginal_settings'
show(object)

Arguments

`train_size`	equivalent to the slot definition below
`refit_size`	equivalent to the slot definition below
`individual_spec`	equivalent to the slot definition below
`default_spec`	equivalent to the slot definition below
`object`	An object of class `marginal_settings`

Details

For specifying the list for individual_spec or the argument default_spec the function default_garch_spec() might come in handy.

Value

Object of class marginal_settings

Functions

marginal_settings(): Class constructor taking the arguments specified in the slots below

Slots

train_size: Positive count specifying the training data size.
refit_size: Positive count specifying size of the forecasting window.
individual_spec: A named list. Specify ARMA-GARCH models for individual assets by naming the list entry as the asset and providing a rugarch::ugarchspec object.
default_spec: rugarch::ugarchspec object specifying the default marginal model (used if the marginal model is not specified through individual_spec)

Examples

# the most basic initialization
marginal_settings(train_size = 100, refit_size = 10)
# some individualism
marginal_settings(
  train_size = 100, refit_size = 10,
  individual_spec = list("GOOG" = default_garch_spec(ar = 3)),
  default_spec = default_garch_spec(dist = "norm")
)
# the most basic initialization
marginal_settings(train_size = 100, refit_size = 10)
# some individualism
marginal_settings(
  train_size = 100, refit_size = 10,
  individual_spec = list("GOOG" = default_garch_spec(ar = 3)),
  default_spec = default_garch_spec(dist = "norm")
)

S4 output class for the function `estimate_risk_roll()`

Description

The main output class for the function estimate_risk_roll()is portvine_roll but in the conditional case the child class cond_portvine_roll with some extra slots (below visible by the !C!) is returned.

Usage

## S4 method for signature 'portvine_roll'
show(object)

## S4 method for signature 'cond_portvine_roll'
show(object)

## S4 method for signature 'portvine_roll'
summary(object)

## S4 method for signature 'cond_portvine_roll'
summary(object)
## S4 method for signature 'portvine_roll'
show(object)

## S4 method for signature 'cond_portvine_roll'
show(object)

## S4 method for signature 'portvine_roll'
summary(object)

## S4 method for signature 'cond_portvine_roll'
summary(object)

Arguments

object

An object of class portvine_roll or cond_portvine_roll

Details

For easy access for the most important slots and some filtering functionality have a look at the accessor methods risk_estimates(), fitted_vines(), fitted_marginals().

Value

object of class portvine_roll

object of class cond_portvine_roll

Slots

risk_estimates: data.table with the columns risk_measure, risk_est, alpha, row_num, vine_window and realized (here all samples also in the conditional case are used)
fitted_marginals: named list with an entry for each asset containing a rugarch::ugarchroll class object that encompasses the marginal model fit.
fitted_vines: list of rvinecopulib::vinecop class objects each entry corresponds to one vine window.
marginal_settings: containing the specification used for the ARMA-GARCH fitting i.e. marginal models. Is of class marginal_settings.
vine_settings: containing the specifications used for the vine fitting. Is of class vine_settings.
risk_measures: a character vector displaying the estimated risk measures.
alpha: numeric vector in (0,1) displaying the confidence levels used when estimating the risk measures.
weights: the numeric positive weights of the assets. (Matrix with each row corresponding to one vine window) The weights of conditional variables are always 0.
cond_estimation: logical value indicating whether the conditional estimation approach for the risk measures was used.
n_samples: positive numeric count displaying how many return samples were used for the risk measure estimation.
time_taken: numeric value displaying how many minutes the whole estimation process took.
cond_risk_estimates: !C! data.table with the same columns as the risk_estimate slot has + the additional conditional columns with the respective conditioning value and the column character cond_u that indicates the used conditional quantile level or the conditional value corresponding to the residual one time unit prior with "prior_resid" or the realized residual with "resid".
cond_vars: !C! character vector with the names of the variables that were used to sample conditionally from.
cond_u: !C! a numeric vector specifying the corresponding quantiles in (0,1) of the conditional variable(s) conditioned on which the conditional risk measures were calculated.

Accessor methods for the risk estimates of `⁠(cond_)portvine_roll⁠` objects

Description

Accessor methods for the risk estimates of ⁠(cond_)portvine_roll⁠ objects

Usage

risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE,
  ...
)

## S4 method for signature 'portvine_roll'
risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE
)

## S4 method for signature 'cond_portvine_roll'
risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE,
  cond = TRUE,
  cond_u = NULL
)
risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE,
  ...
)

## S4 method for signature 'portvine_roll'
risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE
)

## S4 method for signature 'cond_portvine_roll'
risk_estimates(
  roll,
  risk_measures = NULL,
  alpha = NULL,
  df = TRUE,
  exceeded = FALSE,
  cond = TRUE,
  cond_u = NULL
)

Arguments

`roll`	Object of class `portvine_roll` or a child class
`risk_measures`	Character vector of risk measures to filter for. Note that they must be fitted in the `roll` argument. The default will return all fitted risk measures
`alpha`	Numeric $\alpha$ levels of the estimated risk measures to filter for. Note that they must be fitted in the `roll` argument. The default will return all fitted $\alpha$ levels
`df`	Logical value if `TRUE` a `data.frame` is returned otherwise a `data.table` is returned.
`exceeded`	Logical value. If set to `TRUE` a column named `exceeded` will be appended that contains logical values telling whether the realized portfolio value exceeded the estimated risk.
`...`	Additional parameters for child class methods
`cond`	If set to TRUE returns the conditional risk estimates and otherwise returns the overall risk estimates.
`cond_u`	Numeric or character vector specifying the corresponding quantiles in (0,1) of the conditional variable(s) conditioned on which the conditional risk measures were calculated to filter for and/or the class "prior_resid"/"resid". Note that they must be fitted in the `roll` argument. The default will return all fitted risk measures.

Value

(Un-)filtered data.frame or data.table (see df argument) with at least the columns risk_measure, risk_est, alpha, row_num, vine_window and realized. exceeded column if the corresponding argument is set to TRUE. In the conditional case further columns are available (see: portvine_roll).

Extract fitted standardized residuals from a uGARCHroll object

Description

The rugarch::ugarchroll class object encompasses fitting information about a number of models fitted in a rolling window fashion. This utility function gives an easy interface to extract the fitted residuals from one of these models. This can be especially helpful for assessing the model quality with a residual analysis.

Usage

roll_residuals(ugarchroll, roll_num = 1)
roll_residuals(ugarchroll, roll_num = 1)

Arguments

`ugarchroll`	Object of class `rugarch::ugarchroll`.
`roll_num`	Count that specifies the fitted model to extract the residuals from.

Value

Numeric vector of the fitted standardized residuals.

A sample of log returns for 3 assets.

Description

Data extracted from Yahoo Finance representing the daily log returns for Google, Apple, and Amazon stocks between 2014-01-13 and 2018-01-01 which results in exactly 1000 observations.

Usage

data(sample_returns_small)
data(sample_returns_small)

Format

data.table with 3 columns and 1000 rows, columns GOOG, AAPL, AMZN contain the daily log return of the 3 stocks.

Source

Yahoo Finance

Examples

data(sample_returns_small)
head(sample_returns_small)
data(sample_returns_small)
head(sample_returns_small)

S4 class for the vine settings

Description

Specify which vine copula models are fitted and how often they are refit as well as how big the training data set is. Remember that the estimation process is done in a rolling window fashion and the arguments (train and refit size) will have to match with the arguments of the also to be specified marginal_settings.

Usage

vine_settings(train_size, refit_size, family_set = "all", vine_type = "rvine")

## S4 method for signature 'vine_settings'
show(object)
vine_settings(train_size, refit_size, family_set = "all", vine_type = "rvine")

## S4 method for signature 'vine_settings'
show(object)

Arguments

`train_size`	equivalent to the slot definition below
`refit_size`	equivalent to the slot definition below
`family_set`	equivalent to the slot definition below
`vine_type`	equivalent to the slot definition below
`object`	An object of class `vine_settings`

Value

Object of class vine_settings

Functions

vine_settings(): Class constructor taking the arguments specified in the slots below

Slots

train_size: Positive count specifying the training data size.
refit_size: Positive count specifying for how many periods a vine is used
family_set: Character vector specifying the family of copulas that are used. For possible choices see rvinecopulib::bicop. Note for conditional sampling just parametric copula families are possible so do not use the family arguments all and tll.
vine_type: character value that specifies which vine class should be fitted. Possible choices right now are rvine (regular vine) and dvine (drawable vine).

Examples

# the most basic initialization
vine_settings(100, 25)
# some individual note
vine_settings(
  train_size = 100, refit_size = 20,
  family_set = c("gumbel", "joe"),
  vine_type = "dvine"
)
# the most basic initialization
vine_settings(100, 25)
# some individual note
vine_settings(
  train_size = 100, refit_size = 20,
  family_set = c("gumbel", "joe"),
  vine_type = "dvine"
)

Package 'portvine'

Help Index

Default specifications for ARMA-GARCH models

Description

Usage

Arguments

Value

See Also

Examples

Estimate the Expected Shortfall (ES)

Description

Usage

Arguments

Value

See Also

Examples

Estimate the Value at Risk (VaR)

Description

Usage

Arguments

Value

See Also

Examples

(Un-)conditional rolling risk estimation using vine copulas

Description

Usage

Arguments

Details

Value

Matching marginal and vine settings

Parallel processing

Author(s)

See Also

Examples

Accessor method for the fitted marginal models of ⁠(cond_)portvine_roll⁠ objects

Description

Usage

Arguments

Value

See Also

Accessor method for the fitted vine copula models of ⁠(cond_)portvine_roll⁠ objects

Description

Usage

Arguments

Value

See Also

S4 class for the marginal settings

Description

Usage

Arguments

Details

Value

Functions

Slots

See Also

Examples

S4 output class for the function estimate_risk_roll()

Description

Usage

Arguments

Details

Value

Slots

See Also

Accessor methods for the risk estimates of ⁠(cond_)portvine_roll⁠ objects

Description

Usage

Arguments

Value

See Also

Extract fitted standardized residuals from a uGARCHroll object

Description

Usage

Arguments

Value

A sample of log returns for 3 assets.

Description

Usage

Format

Source

Accessor method for the fitted marginal models of `⁠(cond_)portvine_roll⁠` objects

Accessor method for the fitted vine copula models of `⁠(cond_)portvine_roll⁠` objects

S4 output class for the function `estimate_risk_roll()`

Accessor methods for the risk estimates of `⁠(cond_)portvine_roll⁠` objects