tscokurt.tsmarch: Cokurtosis Extractor

In tsmarch: Multivariate ARCH Models

Cokurtosis Extractor

Description

Extracts the conditional cokurtosis matrices.

Usage

## S3 method for class 'gogarch.estimate'
tscokurt(
  object,
  index = NULL,
  distribution = FALSE,
  standardize = TRUE,
  folded = TRUE,
  ...
)

## S3 method for class 'gogarch.predict'
tscokurt(
  object,
  index = NULL,
  distribution = FALSE,
  standardize = TRUE,
  folded = TRUE,
  ...
)

## S3 method for class 'gogarch.simulate'
tscokurt(
  object,
  index = NULL,
  distribution = FALSE,
  standardize = TRUE,
  folded = TRUE,
  ...
)

Arguments

object	an object class from one of the models in the package.
index	the time index (integer) from which to extract a subset of the cokurtosis array rather than the whole time series.
distribution	whether to return the full simulated cokurtosis distribution for the predicted and simulated objects, else the average cokurtosis across each horizon.
standardize	whether to standardize the 4th co-moment so that it represents the cokurtosis.
folded	whether to return the result as a folded or unfolded array. The folded array is n_series x n_series x n_series x n_series x horizon (x simulation if predicted or simulated object). The unfolded array is a n_series x (n_series^3) x horizon array. Calculations such as weighted co-moments are based on the unfolded array using the Kronecker operator.
...	none

Details

The calculation of the cokurtosis array from the independent factors is very expensive in terms of memory footprint as well as computation time. While it does take advantage of multiple threads if required (see setThreadOptions), in the case of many series this will quickly become difficult for systems low RAM. Because of this, there is the option to extract a specific point in time output using the index argument.

Value

the cokurtosis (see details).

Author(s)

Alexios Galanos

tsmarch documentation built on April 3, 2025, 7:40 p.m.