HDGC_HVAR_RV_RCoV_all: Networks of Realized Volatilities conditional on the set of...

In Marga8/HDGCvar: Granger Causality Testing in High Dimensional Vector Autoregressive Models

Networks of Realized Volatilities conditional on the set of Realized Correlations

Description

Networks of Realized Volatilities conditional on the set of Realized Correlations

Usage

HDGC_HVAR_RV_RCoV_all(
  realized_variances,
  realized_covariances,
  fisher_transf = TRUE,
  log = TRUE,
  bound = 0.5 * nrow(realized_variances),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

realized_variances	Dataset of (stationary) realized volatilities. A matrix or object that can be coerced to a matrix.
realized_covariances	Dataset of (stationary) realized covariances. A matrix or object that can be coerced to a matrix. Note: the columns should exactly be (((ncol(realized_volatilities)^2)-ncol(realized_volatilities))/2)
fisher_transf	Logical: if TRUE the correlations are computed and Fisher transformed
log	Default is TRUE, if the realized volatilities are already log transformed then put to FALSE
bound	Lower bound on lambda
parallel	TRUE for parallel computing
n_cores	Nr of cores to use in parallel computing, default is all but one

Value

Granger causality matrix and Lasso selections are printed to the console

References

Hecq, A., Margaritella, L., Smeekes, S., "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure." arXiv preprint arXiv:1902.10991 (2019).

Corsi, Fulvio. "A simple approximate long-memory model of realized volatility." Journal of Financial Econometrics 7.2 (2009): 174-196.

Examples

## Not run:  HDGC_HVAR_RV_RCoV_all(real_var, real_cov, fisher_transf=T, log=TRUE ,parallel = TRUE) 

Marga8/HDGCvar documentation built on May 25, 2024, 11:12 a.m.