Type: | Package |
Title: | Spectral Decomposition of Connectedness Measures |
Version: | 0.2.4 |
Date: | 2023-02-23 |
Description: | Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (https://github.com/tomaskrehlik/frequencyConnectedness) for relevant information. |
Depends: | vars, urca, knitr, pbapply |
Suggests: | testthat, stringr, mAr, reshape2, ggplot2, parallel, zoo, BigVAR |
Imports: | methods |
License: | GPL-2 |
RoxygenNote: | 7.2.3 |
BugReports: | https://github.com/tomaskrehlik/frequencyConnectedness/issues |
URL: | https://github.com/tomaskrehlik/frequencyConnectedness |
NeedsCompilation: | no |
Packaged: | 2023-02-24 21:24:42 UTC; tomaskrehlik |
Author: | Tomas Krehlik [aut, cre] |
Maintainer: | Tomas Krehlik <tomas.krehlik@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2023-02-24 21:50:02 UTC |
Method for for collapsing bound for frequency spillovers
Description
Method for for collapsing bound for frequency spillovers
Usage
collapseBounds(spillover_table, which)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
which |
integer vector indicating which of the frequency bounds we want to have collapsed |
Value
New spillover object with collapsed bounds
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to collapse bounds
Description
Taking in list_of_spills, if the individual spillover_tables are frequency based, it allows you to collapse several frequency bands into one.
Usage
## S3 method for class 'list_of_spills'
collapseBounds(spillover_table, which)
Arguments
spillover_table |
a list_of_spills object, ideally from the provided estimation functions |
which |
which frequency bands to collapse. Should be a sequence like 1:2 or 1:5, etc. |
Value
list_of_spills with less frequency bands.
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to collapse bounds
Description
Taking in spillover_table, if the spillover_table is frequency based, it allows you to collapse several frequency bands into one.
Usage
## S3 method for class 'spillover_table'
collapseBounds(spillover_table, which)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
which |
which frequency bands to collapse. Should be a sequence like 1:2 or 1:5, etc. |
Value
spillover_table with less frequency bands.
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
The simulated time-series
Description
The dataset includes three simulated processes with spillover dynamics.
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com
Compute a forecast error vector decomposition in recursive identification scheme
Description
This function computes the standard forecast error vector decomposition given the estimate of the VAR.
Usage
fevd(est, n.ahead = 100, no.corr = F)
Arguments
est |
the VAR estimate from the vars package |
n.ahead |
how many periods ahead should be taken into account |
no.corr |
boolean if the off-diagonal elements should be set to 0. |
Value
a matrix that corresponds to contribution of ith variable to jth variance of forecast
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com
Compute a FFT transform of forecast error vector decomposition in recursive identification scheme
Description
This function computes the decomposition of standard forecast error vector decomposition given the estimate of the VAR. The decomposition is done according to the Stiassny (1996)
Usage
fftFEVD(est, n.ahead = 100, no.corr = F, range)
Arguments
est |
the VAR estimate from the vars package |
n.ahead |
how many periods ahead should be taken into account |
no.corr |
boolean if the off-diagonal elements should be set to 0. |
range |
defines the frequency partitions to which the spillover should be decomposed |
Value
a list of matrices that corresponds to contribution of ith variable to jth variance of forecast
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com
Compute a FFT transform of forecast error vector decomposition in generalised VAR scheme.
Description
This function computes the decomposition of standard forecast error vector decomposition given the estimate of the VAR. The decomposition is done according to the Stiassny (1996)
Usage
fftGenFEVD(est, n.ahead = 100, no.corr = F, range)
Arguments
est |
the VAR estimate from the vars package |
n.ahead |
how many periods ahead should be taken into account |
no.corr |
boolean if the off-diagonal elements should be set to 0. |
range |
defines the frequency partitions to which the spillover should be decomposed |
Value
a list of matrices that corresponds to contribution of ith variable to jth variance of forecast
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com
Method for computing FROM spillovers
Description
Method for computing FROM spillovers
Usage
from(spillover_table, ...)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
Value for FROM spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute from spillovers
Description
Taking in list_of_spillovers, the function computes the from spillovers for all the individual spillover_table.
Usage
## S3 method for class 'list_of_spills'
from(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the from spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute from spillovers
Description
Taking in spillover_table, the function computes the from spillover.
Usage
## S3 method for class 'spillover_table'
from(spillover_table, within = F, ...)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the from spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Compute a forecast error vector decomposition in generalised VAR scheme.
Description
This function computes the standard forecast error vector decomposition given the
estimate of the VAR.
There are common complaints and requests whether the computation is ok and why
it does not follow the original Pesaran Shin (1998) article. So let me clear two things
out. First, the \sigma
in the equation on page 20 refers to elements of \Sigma
, not standard
deviation. Second, the indexing is wrong, it should be \sigma_jj
not \sigma_ii
. Look, for example,
to Diebold and Yilmaz (2012) or ECB WP by Dees, Holly, Pesaran, and Smith (2007)
for the correct version.
Usage
genFEVD(est, n.ahead = 100, no.corr = F)
Arguments
est |
the VAR estimate from the vars package |
n.ahead |
how many periods ahead should be taken into account |
no.corr |
boolean if the off-diagonal elements should be set to 0. |
Value
a matrix that corresponds to contribution of ith variable to jth variance of forecast
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com
Get the indeces for the individual intervals
Description
This function returns the indeces of the vector coming from DFT of time series of length n.ahead that correspond to frequencies in the interval (up, down].
Usage
getIndeces(n.ahead, up, down)
Arguments
n.ahead |
the length of the vector coming out of the DFT |
up |
the upper boundary of the interval |
down |
the lower boundary of the interval |
Author(s)
Tomas Krehlik tomas.krehlik@sorgmail.com
Get a list of indeces corresponding to parts of frequency partition
Description
This function takes in a vector of numbers denoting the breaks in partition of an interval and returns a list of indeces that correspond to indeces that are contained within an individual intervals. The individual parts then contain (a,b] for all pairs in the interval. Hence if you want pi to be included, the partition should start with something slightly bigger than pi.
Usage
getPartition(partition, n.ahead)
Arguments
partition |
breaking points of partition of frequency interval, should be ordered decreasingly. |
n.ahead |
how many observations is the FFT done on. |
Value
a list of vectors of indeces corresponding to individual partitions
Author(s)
Tomas Krehlik tomas.krehlik@sorgmail.com
Method for computing NET spillovers
Description
Method for computing NET spillovers
Usage
net(spillover_table, ...)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
Value for NET spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute net spillovers
Description
Taking in list_of_spillovers, the function computes the net spillovers for all the individual spillover_table.
Usage
## S3 method for class 'list_of_spills'
net(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the net spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute net spillovers
Description
Taking in spillover_table, the function computes the net spillover.
Usage
## S3 method for class 'spillover_table'
net(spillover_table, within = F, ...)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the net spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for computing overall spillovers
Description
Method for computing overall spillovers
Usage
overall(spillover_table, ...)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
Value for overall spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute overall spillovers
Description
Taking in list_of_spillovers, the function computes the overall spillovers for all the individual spillover_table.
Usage
## S3 method for class 'list_of_spills'
overall(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the overall spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute overall spillovers
Description
Taking in spillover_table, the function computes the overall spillover.
Usage
## S3 method for class 'spillover_table'
overall(spillover_table, within = F, ...)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the overall spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for computing PAIRWISE spillovers
Description
Method for computing PAIRWISE spillovers
Usage
pairwise(spillover_table, ...)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
Value for PAIRWISE spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute pairwise spillovers
Description
Taking in list_of_spillovers, the function computes the pairwise spillovers for all the individual spillover_table.
Usage
## S3 method for class 'list_of_spills'
pairwise(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the pairwise spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute pairwise spillovers
Description
Taking in spillover_table, the function computes the pairwise spillover.
Usage
## S3 method for class 'spillover_table'
pairwise(spillover_table, within = F, ...)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the pairwise spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting FROM spillovers
Description
Method for ploting FROM spillovers
Usage
plotFrom(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot from spillovers
Description
Taking in list_of_spillovers, the function plots the from spillovers using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotFrom(
spillover_table,
within = F,
which = 1:nrow(spillover_table$list_of_tables[[1]]$tables[[1]]),
...
)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
which |
a vector with indices specifying which plots to plot. |
... |
for the sake of CRAN not to complain |
Value
a plot of from spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting NET spillovers
Description
Method for ploting NET spillovers
Usage
plotNet(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot net spillovers
Description
Taking in list_of_spillovers, the function plots the net spillovers using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotNet(
spillover_table,
within = F,
which = 1:nrow(spillover_table$list_of_tables[[1]]$tables[[1]]),
...
)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
which |
a vector with indices specifying which plots to plot. |
... |
for the sake of CRAN not to complain |
Value
a plot of net spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting overall spillovers
Description
Method for ploting overall spillovers
Usage
plotOverall(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot overall spillovers
Description
Taking in list_of_spillovers, the function plots the overall spillovers using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotOverall(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a plot of overall spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting PAIRWISE spillovers
Description
Method for ploting PAIRWISE spillovers
Usage
plotPairwise(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot pairwise spillovers
Description
Taking in list_of_spillovers, the function plots the pairwise spillovers using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotPairwise(
spillover_table,
within = F,
which = 1:ncol(utils::combn(nrow(spillover_table$list_of_tables[[1]]$tables[[1]]), 2)),
...
)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
which |
a vector with indices specifying which plots to plot. |
... |
for the sake of CRAN not to complain |
Value
a plot of pairwise spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting specific pair spillover
Description
Method for ploting specific pair spillover
Usage
plotSpecific(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like which specifi pair to plot. |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot specific spilover from i to j
Description
Taking in list_of_spillovers, the function plots the spillover from i to j using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotSpecific(spillover_table, i, j, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
i |
from variable |
j |
to variable |
... |
for the sake of CRAN not to complain |
Value
a plot of pairwise spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for ploting TO spillovers
Description
Method for ploting TO spillovers
Usage
plotTo(spillover_table, ...)
Arguments
spillover_table |
the output of rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
The plot
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to plot to spillovers
Description
Taking in list_of_spillovers, the function plots the to spillovers using the zoo::plot.zoo function
Usage
## S3 method for class 'list_of_spills'
plotTo(
spillover_table,
within = F,
which = 1:nrow(spillover_table$list_of_tables[[1]]$tables[[1]]),
...
)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
which |
a vector with indices specifying which plots to plot. |
... |
for the sake of CRAN not to complain |
Value
a plot of to spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to not print the list_of_spills object
Description
Usually it is not a good idea to print the list_of_spills object, hence this function implements warning and shows how to print them individually if the user really wants to.
Usage
## S3 method for class 'list_of_spills'
print(x, ...)
Arguments
x |
a list_of_spills object, ideally from the provided estimation functions |
... |
for the sake of CRAN not to complain |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to print the spillover table object
Description
The function takes as an argument the spillover_table object and prints it nicely to the console. While doing that it also computes all the neccessary measures.
Usage
## S3 method for class 'spillover_table'
print(x, ...)
Arguments
x |
a spillover_table object, ideally from the provided estimation functions |
... |
for the sake of CRAN not to complain |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing spillover from a fevd
Description
This function is an internal implementation of the spillover. The spillover is in general defined as the contribution of the other variables to the fevd of the self variable. This function computes the spillover as the contribution of the diagonal elements of the fevd to the total sum of the matrix. The other functions are just wrappers around this function. In general, other spillovers could be implemented using this function.
Usage
spillover(func, est, n.ahead, no.corr = F)
Arguments
func |
name of the function that returns FEVD for the estimtate est |
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing the decomposed spillover from a fevd as defined by Barunik, Krehlik (2018)
Description
This function is an internal implementation of the frequency spillover. We apply the identification scheme suggested by fevd to the frequency decomposition of the transfer functions from the estimate est.
Usage
spilloverBK09(est, n.ahead = 100, no.corr, partition)
Arguments
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
partition |
defines the frequency partitions to which the spillover should be decomposed |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing the decomposed spillover from a generalized fevd as defined by Barunik, Krehlik (2018)
Description
This function is an internal implementation of the frequency spillover. We apply the identification scheme suggested by fevd to the frequency decomposition of the transfer functions from the estimate est.
Usage
spilloverBK12(est, n.ahead = 100, no.corr, partition)
Arguments
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
partition |
defines the frequency partitions to which the spillover should be decomposed |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing spillover from a fevd according to Diebold Yilmaz (2009)
Description
This function is an internal implementation of the spillover. The spillover is in general defined as the contribution of the other variables to the fevd of the self variable. This function computes the spillover as the contribution of the diagonal elements of the fevd to the total sum of the matrix. The other functions are just wrappers around this function. In general, other spillovers could be implemented using this function.
Usage
spilloverDY09(est, n.ahead = 100, no.corr)
Arguments
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing spillover from a generalized fevd according to Diebold Yilmaz (2012)
Description
This function is an internal implementation of the spillover. The spillover is in general defined as the contribution of the other variables to the fevd of the self variable. This function computes the spillover as the contribution of the diagonal elements of the fevd to the total sum of the matrix. The other functions are just wrappers around this function. In general, other spillovers could be implemented using this function.
Usage
spilloverDY12(est, n.ahead = 100, no.corr)
Arguments
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing the decomposed spillover from a fevd
Description
This function is an internal implementation of the frequency spillover. We apply the identification scheme suggested by fevd to the frequency decomposition of the transfer functions from the estimate est.
Usage
spilloverFft(func, est, n.ahead, partition, no.corr = F)
Arguments
func |
name of the function that returns FEVD for the estimtate est |
est |
the estimate of a system, typically VAR estimate in our case |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
partition |
defines the frequency partitions to which the spillover should be decomposed |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
Value
spillover_table object
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing rolling spillover
Description
This function computes the rolling spillover using the standard VAR estimate. We implement the parallel version for faster processing. The window is of fixed window and is rolled over the data. Interpretation of the other parameters is the same as in the standard computation of spillover. For usage, see how spilloverRollingDY09, etc. are implemented.
Usage
spilloverRolling(
func_spill,
params_spill,
func_est,
params_est,
data,
window,
cluster = NULL,
check_data = TRUE
)
Arguments
func_spill |
name of the function that returns FEVD for the estimtate est |
params_spill |
parameters from spillover estimation function as a list |
func_est |
name of the estimation function |
params_est |
parameters from the estimation function as a list |
data |
variable containing the dataset |
window |
length of the window to be rolled |
cluster |
either NULL for no parallel processing or the variable containing the cluster. |
check_data |
whether to check the data for NAs before starting estimation. Typically should be left true unless the underlying estimate is providing a way how to infer those NAs. |
Value
A corresponding spillover value on a given freqeuncy band, ordering of bands corresponds to the ordering of original bounds.
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing rolling frequency spillover from a fevd as defined by Barunik, Krehlik (2018)
Description
This function computes the rolling spillover using the standard VAR estimate. We implement the parallel version for faster processing. The window is of fixed window and is rolled over the data. Interpretation of the other parameters is the same as in the standard computation of spillover.
Usage
spilloverRollingBK09(
data,
n.ahead = 100,
no.corr,
partition,
func_est,
params_est,
window,
cluster = NULL
)
Arguments
data |
variable containing the dataset |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
partition |
how to split up the estimated spillovers into frequency bands. Should be a vector of bound points that starts with 0 and ends with pi+0.00001. |
func_est |
estimation function, usually would be VAR or BigVAR function to estimate the multivariate system |
params_est |
parameters passed to the estimation function, as a list, for parameters refer to documentation of the estimating function |
window |
length of the window to be rolled |
cluster |
either NULL for no parallel processing or the variable containing the cluster. |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing rolling frequency spillover from a generalized fevd as defined by Barunik, Krehlik (2018)
Description
This function computes the rolling spillover using the standard VAR estimate. We implement the parallel version for faster processing. The window is of fixed window and is rolled over the data. Interpretation of the other parameters is the same as in the standard computation of spillover.
Usage
spilloverRollingBK12(
data,
n.ahead = 100,
no.corr,
partition,
func_est,
params_est,
window,
cluster = NULL
)
Arguments
data |
variable containing the dataset |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
partition |
defines the frequency partitions to which the spillover should be decomposed |
func_est |
a name of the function to estimate with, for example "var" for VAR from vars package |
params_est |
a list of the parameters to pass to the function besides the data that are passed as a first element. |
window |
length of the window to be rolled |
cluster |
either NULL for no parallel processing or the variable containing the cluster. |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing rolling spillover according to Diebold Yilmaz (2009)
Description
This function computes the rolling spillover using the standard VAR estimate. We implement the parallel version for faster processing. The window is of fixed window and is rolled over the data. Interpretation of the other parameters is the same as in the standard computation of spillover.
Usage
spilloverRollingDY09(
data,
n.ahead = 100,
no.corr,
func_est,
params_est,
window,
cluster = NULL
)
Arguments
data |
variable containing the dataset |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
func_est |
estimation function, usually would be VAR or BigVAR function to estimate the multivariate system |
params_est |
parameters passed to the estimation function, as a list, for parameters refer to documentation of the estimating function |
window |
length of the window to be rolled |
cluster |
either NULL for no parallel processing or the variable containing the cluster. |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Computing rolling spillover from the generalized fevd according to Diebold Yilmaz (2012)
Description
This function computes the rolling spillover using the standard VAR estimate. We implement the parallel version for faster processing. The window is of fixed window and is rolled over the data. Interpretation of the other parameters is the same as in the standard computation of spillover.
Usage
spilloverRollingDY12(
data,
n.ahead = 100,
no.corr,
func_est,
params_est,
window,
cluster = NULL
)
Arguments
data |
variable containing the dataset |
n.ahead |
how many periods ahead should the FEVD be computed, generally this number should be high enough so that it won't change with additional period |
no.corr |
boolean parameter whether the off-diagonal in the covariance matrix should be set to zero |
func_est |
estimation function, usually would be VAR or BigVAR function to estimate the multivariate system |
params_est |
parameters passed to the estimation function, as a list, for parameters refer to documentation of the estimating function |
window |
length of the window to be rolled |
cluster |
either NULL for no parallel processing or the variable containing the cluster. |
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Method for computing TO spillovers
Description
Method for computing TO spillovers
Usage
to(spillover_table, ...)
Arguments
spillover_table |
the output of spillover estimation function or rolling spillover estimation function |
... |
other arguments like whether it is within or absolute spillover in case of the frequency spillovers |
Value
Value for TO spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute to spillovers
Description
Taking in list_of_spillovers, the function computes the to spillovers for all the individual spillover_table.
Usage
## S3 method for class 'list_of_spills'
to(spillover_table, within = F, ...)
Arguments
spillover_table |
a list_of_spills object, ideally from rolling window estimation |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the to spillovers
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Function to compute to spillovers
Description
Taking in spillover_table, the function computes the to spillover.
Usage
## S3 method for class 'spillover_table'
to(spillover_table, within = F, ...)
Arguments
spillover_table |
a spillover_table object, ideally from the provided estimation functions |
within |
whether to compute the within spillovers if the spillover tables are frequency based. |
... |
for the sake of CRAN not to complain |
Value
a list containing the to spillover
Author(s)
Tomas Krehlik <tomas.krehlik@gmail.com>
Volatilities from Ox Man Institute
Description
The dataset includes median realised volatilities of some financial indices
Author(s)
Tomas Krehlik tomas.krehlik@gmail.com