fastSOM (version 1.0.1)

sot_avg_est: Estimation of the Average, Minimal, and Maximal Entries of a Spillover Table

Description

Calculates estimates of the average, minimal, and maximal entries of a spillover.

Usage

sot_avg_est(Sigma, A, ncores = 1, ...)

Arguments

Sigma

Either a covariance matrix or a list thereof.

A

Either a 3-dimensional array with A[,,h] being MA coefficient matrices of the same dimension as Sigma or a list thereof.

ncores

Number of cores. Missing ncores or ncores=1 means no parallelization (just one core is used). ncores=0 means automatic detection of the number of available cores. Any other integer determines the maximal number of cores to be used.

…

Further arguments, especially perms which is used to reorder variables. If perms is missing, then 10.000 randomly created permutations of 1:N will be used as reorderings of the model variables. If perms is defined, it has to be either a matrix with each column being a permutation of 1:N, or, alternatively, an integer value defining the number of randomly created permutations.

Value

The 'single' version returns a list containing the exact average, minimal, and maximal values for the spillover table. The 'list' version returns a list with three elements (Average, Minimum, Maximum) which themselves are lists of the corresponding tables.

Details

The spillover tables introduced by Diebold and Yilmaz (2009) (see References) depend on the ordering of the model variables. While sot_avg_exact provides a fast algorithm for exact calculation of average, minimum, and maximum of the spillover table over all permutations, there might be reasons to prefer to estimate these quantities using a limited number of permutations (mainly to save time when \(N\) is large). This is exactly what sot_avg_est does.

The typical application of the 'list' version of sot_avg_est is a rolling windows approach when Sigma and A are lists representing the corresponding quantities at different points in time (rolling windows).

References

[1] Diebold, F. X. and Yilmaz, K. (2009): Measuring financial asset return and volatitliy spillovers, with application to global equity markets, Economic Journal 199(534): 158-171.

[2] Kloessner, S. and Wagner, S. (2012): Exploring All VAR Orderings for Calculating Spillovers? Yes, We Can! - A Note on Diebold and Yilmaz (2009), Journal of Applied Econometrics 29(1): 172-179

See Also

fastSOM-package, sot_avg_exact

Examples

# NOT RUN {
# generate randomly positive definite matrix Sigma of dimension N 
N <- 10
Sigma <- crossprod(matrix(rnorm(N*N),nrow=N)) 
# generate randomly coefficient matrices
H <- 10 
A <- array(rnorm(N*N*H),dim=c(N,N,H)) 
# calculate estimates of the average, minimal, 
# and maximal entries within a spillover table
sot_avg_est(Sigma, A) 
# }