Package 'Spillover' reference manual

Title:	Spillover/Connectedness Index Based on VAR Modelling
Description:	A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.
Authors:	Jilber Urbina
Maintainer:	Jilber Urbina <[email protected]>
License:	GPL-2
Version:	0.1.1
Built:	2024-11-08 06:33:14 UTC
Source:	CRAN

Diebold and Yilmaz (2009) dataset

Description

A dataset consisting of 829 weekly observations of Global Stock Market Returns, 10/1/1992 – 23/11/2007. Find details in Diebold and Yilmaz (2009)

Format

a data.frame-class dataset

References

Diebold, F. X. & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119, 158–171

Examples

data(dy2009)
head(dy2009)  
summary(dy2009)  # Same as Diebold and Yilmaz (2012) summary statistics
data(dy2009)
head(dy2009)  
summary(dy2009)  # Same as Diebold and Yilmaz (2012) summary statistics

Diebold and Yilmaz (2012) dataset

Description

A dataset consisting of 2771 log volatility daily observations of 4 variables: Stocks (SP500), Bonds (R_10Y), Commodities (DJUBSCOM) and FX (USDX). The period for this dataset is from Jan 25, 1999 to Jan 29, 2010.

Format

a data.frame-class dataset

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting. 28, 57–66.

Examples

data(dy2012)
head(dy2012)  
summary(dy2012)  # Same as Diebold and Yilmaz (2012) summary statistics
data(dy2012)
head(dy2012)  
summary(dy2012)  # Same as Diebold and Yilmaz (2012) summary statistics

dynamnic_spillover is a function for estimating DY directional spillovers.

Description

dynamnic_spillover is a function for estimating DY directional spillovers.

Usage

dynamic.spillover(
  data,
  width,
  n.ahead = 10,
  standardized = TRUE,
  na.fill = FALSE,
  remove.own = TRUE,
  ...
)
dynamic.spillover(
  data,
  width,
  n.ahead = 10,
  standardized = TRUE,
  na.fill = FALSE,
  remove.own = TRUE,
  ...
)

Arguments

`data`	a data.frame consisting of dates in its first column and numeric variables for the others
`width`	a integer specifying the window width in number of observations.
`n.ahead`	An integer indicating the how many steps ahead the spillover should be forecasted.
`standardized`	A logical value indicating whether the values should be divided by the number of columns to get a percentage.
`na.fill`	A logical value for filling with NA at the begining window due to `width`.
`remove.own`	should own directional spillover be removed?
`...`	Further arguments to be passed to var function

Value

A list of data.frames holding all directional spillovers as described in Diebold and Yilmaz (2012)

Examples

data(dy2012)

dy_results <- dynamic.spillover(data=dy2012, width=200, remove.own = FALSE) 
 

data(dy2012)

dy_results <- dynamic.spillover(data=dy2012, width=200, remove.own = FALSE)

Generalized Forecast Error Variance Decomposition

Description

Computes the generalized forecast error variance decomposition of a VAR(p) for n.ahead steps.

Usage

g.fevd(x, n.ahead = 10, normalized = TRUE)
g.fevd(x, n.ahead = 10, normalized = TRUE)

Arguments

`x`	Object of class ‘`varest`’ generated by `VAR()` from vars package.
`n.ahead`	Integer specifying the steps ahead.
`normalized`	a logical value indicating whether the result should be normalized to sum up to 1, see Details

Details

When normalized=FALSE this function computes the generalized forecast error variance decomposition proposed by Pesaran and Shin (1998) which takes the form:

$\alpha _{ij}^{g}(h) = \frac{\sigma_{ii}^{-1}\sum_{l=0}^{h-1}(\mathbf{e'}_{i}\Theta _{l}\Sigma_{\varepsilon}\mathbf{e}_{j})^{2}}{\sum_{l=0}^{h-1}(\mathbf{e'}_{i}\Theta _{l}\Sigma _{\varepsilon }\Theta'_{l}\mathbf{e}_{i})}, \quad i,j = 0,1,2\ldots, K$

Where $\mathbf{\Theta}_{l}$ , are the coefficients matrix of the MA representation of the VAR model, $\mathbf{\Sigma}_{\varepsilon}$ is the variance matrix of the reduced-form error vector $\varepsilon$ , $\sigma_{ii}$ is the standard deviation of the error term for the $ith$ equation and $e_{i}$ and $e_{j}$ are selection vectors with ones as the ith element and zeros elsewhere.

If normalized=TRUE (the default value) then g.fevd computes:

$\tilde{a}_{ij}^{g}(h) = \frac{a_{ij}^{g}(h)} {\sum_{j=1}^{K} a_{ij}^{g}(h)}$

This fact implies the normalization is simply each entry of the generalized fevd divided by the its corresponding row sum.

Value

A list of length K holding the generalized forecast error variances as matrices. This is an object of class ‘varfevd’ from vars package.

Author(s)

Jilber Urbina

References

Pesaran, M. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17-29.

Examples

library(vars)
data(stock.prices)
stocks <- stock.prices[,1:2]
VAR.1 <- VAR(stocks)
g.fevd(VAR.1, n.ahead = 10) # normalized 
g.fevd(VAR.1, n.ahead = 10, normalized=FALSE) # Not normalized 
library(vars)
data(stock.prices)
stocks <- stock.prices[,1:2]
VAR.1 <- VAR(stocks)
g.fevd(VAR.1, n.ahead = 10) # normalized 
g.fevd(VAR.1, n.ahead = 10, normalized=FALSE) # Not normalized

Generalized spillover index

Description

Computes the generalized spillover index proposed in Diebold and Yilmaz (2012) which is based on the General Forecast Variance Decompositon introduced by Pesaran and Shin (1998).

Usage

G.spillover(x, n.ahead = 10, standardized = TRUE)
G.spillover(x, n.ahead = 10, standardized = TRUE)

Arguments

`x`	Object of class ‘`varest`’ generated by `VAR()` from vars package.
`n.ahead`	Integer specifying the steps ahead.
`standardized`	A logical value indicating whether the values should be divided by the number of columns to get a percentage.

Details

This function computes the Generalized Directional Spillover Table which has as its $ij^{th}$ entry the estimated contribution to the forecast error variance of variable i coming from innovations to variable j. The off-diagonal column sums are the Contributions to Others, while the row sums represent Contributions from Others, when these are totaled across countries then we have the numerator of the Spillover Index. Similarly, the columns sums or rows sums (including diagonal), when totaled across countries, give the denominator of the Spillover Index, which is 100%.

G.spillover is based upon the General Forecast Error Variance Decompositon introduced by Pesaran and Shin (1998) and its explicit formulation can be found in Diebold and Yilmaz (2010).

Value

A data.frame consisting of the spillover index.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Pesaran, M. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17-29.

Examples

# Replicating Diebold and Yilmaz (2012)
data(dy2012)
VAR_4 <- VAR(dy2012[,-1], p=4) 
G.spillover(VAR_4, standardized = FALSE)
# Replicating Diebold and Yilmaz (2012)
data(dy2012)
VAR_4 <- VAR(dy2012[,-1], p=4) 
G.spillover(VAR_4, standardized = FALSE)

Net spillovers

Description

Computes the net spillover index.

Usage

net(x)
net(x)

Arguments

`x`	Object of class `spillover.table` generated by either `O.spillover()` or `G.spillover()`.

Value

A list length K holding the generalized forecast error variances as matrices.

Author(s)

Jilber Urbina

References

Pesaran, M. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17-29.

Orthogonalized spillover index

Description

Computes the orthogonalized spillover index proposed in Diebold and Yilmaz (2009) which is based on the Orthogonalized Forecast Error Variance Decompositon.

Usage

O.spillover(
  x,
  n.ahead = 10,
  ortho.type = c("single", "partial", "total"),
  standardized = TRUE
)
O.spillover(
  x,
  n.ahead = 10,
  ortho.type = c("single", "partial", "total"),
  standardized = TRUE
)

Arguments

`x`	Object of class ‘`varest`’ generated by `VAR()` from vars package.
`n.ahead`	Integer specifying the steps ahead.
`ortho.type`	A character string indicating the type of orthogonalized index is required. `"single"` takes the original ordering of variables in VAR model and applies Cholesky decomposition for the fevd. Whereas `"partial"` takes a random sample out of all the possible combinations generated for the Cholesky decomposition, while `"total"` uses all the combinations, therefore it takes more time to finish. Both, `"partial"` and `"total"` provide average results.
`standardized`	A logical value indicating whether the values should be divided by the number of columns to get a percentage.

Details

This function computes the Orthogonalized Directional Spillover Table which has as its $ij^{th}$ entry the estimated contribution to the forecast error variance of variable i coming from innovations to variable j. The off-diagonal column sums are the Contributions to Others, while the row sums represent Contributions from Others, when these are totaled across countries then we have the numerator of the Spillover Index. Similarly, the columns sums or rows sums (including diagonal), when totaled across countries, give the denominator of the Spillover Index, which is 100%.

O.spillover is based upon the Orthogonalized (using Cholesky orthogonalization) Forecast Error Variance Decompositon (see Lutkepohl, 2006) and its explicit formulation can be found in Diebold and Yilmaz (2009).

Since O.spillover is based on orthogonalized FEVD, then the result is as many indeces as combinations is allowed according to the number of variables in the VAR model, this is exactly equal to $K!$ , then output has three options: table, summary and all.ind. table produces a data.frame holding the (orthogonalized) directional mean spillover indices.

When output="table", a data.frame is generated consisting of either mean or median directional spillover indeces, this because for each possible order of the variables the o.fevd is computed and over this result a spillover index is generated and this procedure repeats until reaching the last order (this means all the possible combinations given by $K!$ ). When output="table" a mean directional spillover table is generated, but this can be changed using stat="median" for a median directional spillover to be genereated. Note that stat argument only affects the results of output="table".

When output="summary" an vector is generated, this contains Mean, Min, Max.

This is a user-frendly version of fastSOM::sot_avg_exact() function.

Value

When output="table", a data.frame consisting of the spillover index.

When output="summary", a summary of all spillover indeces.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119, 158-171

Lutkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.

Examples

library(vars)

# Replicating Table 3, Diebold and Yilmaz (2009)

data(dy2009)
VAR.2 <- VAR(dy2009[,-1], p=2)
O.spillover(VAR.2, ortho.type  = "single", standardized = FALSE) 
O.spillover(VAR.2, ortho.type  = "partial" ) 


library(vars)

# Replicating Table 3, Diebold and Yilmaz (2009)

data(dy2009)
VAR.2 <- VAR(dy2009[,-1], p=2)
O.spillover(VAR.2, ortho.type  = "single", standardized = FALSE) 
O.spillover(VAR.2, ortho.type  = "partial" )

Plot directional spillover index

Description

Plot directional spillover index

Usage

plotdy(
  data,
  direction = c("from", "to", "net", "net_pairwise", "from_to_pairwise")
)
plotdy(
  data,
  direction = c("from", "to", "net", "net_pairwise", "from_to_pairwise")
)

Arguments

`data`	An object class `directional.spillover` resulting from applying `dynamic.spillover`
`direction`	A character string indicating the direction of the spillover, see Diebold and Yilmaz (2012)

Value

a ggplot2 object consisting of directional plots

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Examples

 
library(Spillover)
require(tidyr)
require(dplyr)
require(ggplot2)

data(dy2012)
dy_results <- dynamic.spillover(data=dy2012, width=200, remove.own = FALSE) 

pp_from <- plotdy(dy_results, direction = "from")

pp_from +
 labs(title="Any title") +
 facet_wrap(~variables, ncol = 2)

pp_to <- plotdy(dy_results, direction = "to")

pp_net <- plotdy(dy_results, direction = "net")

pp_netpairwise <- plotdy(dy_results, direction = "net_pairwise")

pp_from_to_pairwise <- plotdy(dy_results, direction = "from_to_pairwise")





library(Spillover)
require(tidyr)
require(dplyr)
require(ggplot2)

data(dy2012)
dy_results <- dynamic.spillover(data=dy2012, width=200, remove.own = FALSE) 

pp_from <- plotdy(dy_results, direction = "from")

pp_from +
 labs(title="Any title") +
 facet_wrap(~variables, ncol = 2)

pp_to <- plotdy(dy_results, direction = "to")

pp_net <- plotdy(dy_results, direction = "net")

pp_netpairwise <- plotdy(dy_results, direction = "net_pairwise")

pp_from_to_pairwise <- plotdy(dy_results, direction = "from_to_pairwise")

Two-days Rolling Average Returns

Description

A dataset of class zoo consisting of 1632 two-days rolling average observations on returns based on closed price for six leading stock indices: S&P 500 (US), FTSE 100 (UK), EURO STOXX 50 (Eurozone), BOVESPA (Brazil), NIKKEI 225 (Japan) and S&P ASX 200 (Australia). EURO STOXX 50 covers 50 stocks from 12 Eurozone countries: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain. The period for this dataset is from June 16, 2003 to September 15, 2009. All series are in US Dollars.

Format

a zoo-class dataset

Examples

data(rol.returns)
head(rol.returns)  # First 6 observations
tail(rol.returns)  # Last 6 observations
data(rol.returns)
head(rol.returns)  # First 6 observations
tail(rol.returns)  # Last 6 observations

Two-days Rolling Average Intra-day Volatilities

Description

A dataset of class zoo consisting of 1633 two-days rolling average observations on intraday volatilities based on Garman and Klass (1980) for six leading stock indices: S&P 500 (US), FTSE 100 (UK), EURO STOXX 50 (Eurozone), BOVESPA (Brazil), NIKKEI 225 (Japan) and S&P ASX 200 (Australia). EURO STOXX 50 covers 50 stocks from 12 Eurozone countries: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain. The period for this dataset is from June 13, 2003 to September 15, 2009. All series are in US Dollars.

Format

a zoo-class dataset

Examples

data(rol.vol)
head(rol.vol)  # First 6 observations
tail(rol.vol)  # Last 6 observations
data(rol.vol)
head(rol.vol)  # First 6 observations
tail(rol.vol)  # Last 6 observations

Dynamic Spillover Index

Description

Estimates the dynamic spillover index given a moving window as described in Diebold and Yilmaz (2012). We recommend switching to dynamic.spillover.

Usage

roll.net(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("partial", "total"),
  ...
)
roll.net(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("partial", "total"),
  ...
)

Arguments

`data`	Object of class ‘`zoo`’.
`width`	An integer specifying the window width which is aligned to the original sample.
`n.ahead`	An integer indicating the how many steps ahead the spillover should be forecasted.
`index`	A character string indicating whether the orthogonalized or the generalized index is computed.
`ortho.type`	A character string indicating the type of orthogonalized index is required. `"partial"` takes a random sample out of all the possible combinations generated for the Choleski decomposition, while `"total"` uses all the combinations, therefore it takes more time to finish.
`...`	Further arguments to be passed to `VAR` function from vars package.

Value

A zoo object holding all the net spillover index estimations.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Examples


data(dy2012) 
G_net <- roll.net(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, index="generalized")

data(dy2012)
# orthogonalized rolling net spillover index, based on a VAR(2)
O_net_dy2012 <- roll.net(as.zoo(dy2012[,-1]), width = 200) 
# Generalized rolling net spillover index, based on a VAR(2)
G_net_dy2012 <- roll.net(as.zoo(dy2012[,-1]), width = 200, index="generalized")

data(dy2012) 
G_net <- roll.net(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, index="generalized")

data(dy2012)
# orthogonalized rolling net spillover index, based on a VAR(2)
O_net_dy2012 <- roll.net(as.zoo(dy2012[,-1]), width = 200) 
# Generalized rolling net spillover index, based on a VAR(2)
G_net_dy2012 <- roll.net(as.zoo(dy2012[,-1]), width = 200, index="generalized")

Dynamic Spillover Index

Description

Estimates the dynamic spillover index given a rolling window as described in Diebold and Yilmaz (2012). We recommend switching to dynamic.spillover.

Usage

roll.spillover(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("single", "partial", "total"),
  ...
)
roll.spillover(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("single", "partial", "total"),
  ...
)

Arguments

`data`	Object of class ‘`zoo`’.
`width`	An integer specifying the window width which is aligned to the original sample.
`n.ahead`	An integer indicating the how many steps ahead the spillover should be forecasted.
`index`	A character string indicating whether the orthogonalized or the generalized index is computed.
`ortho.type`	Applicable only if `index="orthogonalized"`. A character string indicating the type of orthogonalized index is required. `"single"` takes the original ordering of variables in VAR model and applies Cholesky decomposition for the fevd. Whereas `"partial"` takes a random sample out of all the possible combinations generated for the Cholesky decomposition, while `"total"` uses all the combinations, therefore it takes more time to finish. Both, `"partial"` and `"total"` provide average results.
`...`	Further arguments to be passed to `VAR` function from vars package.

Value

A zoo object holding all the indeces.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Examples

 
data(dy2012)
O_index <- roll.spillover(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, p=4)
# Orthogonalized rolling spillover index based on a VAR(4), single order
 O_index <- roll.spillover(as.zoo(dy2012[,-1]), width = 200, p=4)
# Generalized rolling spillover index based on a VAR(4)
 G_index<- roll.spillover(as.zoo(dy2012[,-1]), width = 200, index="generalized", p=4) 
# A comparison: (warning: It can take several minutes.)
 single <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4)
 partial <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, ortho.type = "partial")
 total <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, ortho.type = "total")
 out <- cbind(single, partial, total)
 head(out)
 plot(out, col=1:3, main="Spillover index")

data(dy2012)
O_index <- roll.spillover(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, p=4)
# Orthogonalized rolling spillover index based on a VAR(4), single order
 O_index <- roll.spillover(as.zoo(dy2012[,-1]), width = 200, p=4)
# Generalized rolling spillover index based on a VAR(4)
 G_index<- roll.spillover(as.zoo(dy2012[,-1]), width = 200, index="generalized", p=4) 
# A comparison: (warning: It can take several minutes.)
 single <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4)
 partial <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, ortho.type = "partial")
 total <- roll.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, ortho.type = "total")
 out <- cbind(single, partial, total)
 head(out)
 plot(out, col=1:3, main="Spillover index")

Spillover

Description

Spillover index based on both, orthogonalized and generalized forecast error variance decomposition of a VAR(p) for n step ahead forecast. It computes both orthogonalized and generalized directional spillover indices proposed by Diebold and Yilmaz (2009, 2012)

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119, 158-171.

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting. 28, 57–66.

Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.

Lutkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.

Pesaran, M. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17-29.

Daily Stock Prices

Description

A dataset consisting of 3507 daily observations on closed price for six leading stock indices: S&P 500 (US), FTSE 100 (UK), EURO STOXX 50 (Eurozone), BOVESPA (Brazil), NIKKEI 225 (Japan) and S&P ASX 200 (Australia). EURO STOXX 50 covers 50 stocks from 12 Eurozone countries: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain. The period for this dataset is from December 31, 1999 to June 10, 2013. All series are in US Dollars.

Format

a zoo-class dataset

Examples

data(stock.prices)
head(stock.prices)  # First 6 observations
tail(stock.prices)  # Last 6 observations
data(stock.prices)
head(stock.prices)  # First 6 observations
tail(stock.prices)  # Last 6 observations

Dynamic Spillover Index

Description

Estimates the dynamic spillover index given a rolling window as described in Diebold and Yilmaz (2012).

Usage

total.dynamic.spillover(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("single", "partial", "total"),
  ...
)
total.dynamic.spillover(
  data,
  width,
  n.ahead = 10,
  index = c("orthogonalized", "generalized"),
  ortho.type = c("single", "partial", "total"),
  ...
)

Arguments

`data`	Object of class ‘`zoo`’.
`width`	An integer specifying the window width which is aligned to the original sample.
`n.ahead`	An integer indicating the how many steps ahead the spillover should be forecasted.
`index`	A character string indicating whether the orthogonalized or the generalized index is computed.
`ortho.type`	Applicable only if `index="orthogonalized"`. A character string indicating the type of orthogonalized index is required. `"single"` takes the original ordering of variables in VAR model and applies Cholesky decomposition for the fevd. Whereas `"partial"` takes a random sample out of all the possible combinations generated for the Cholesky decomposition, while `"total"` uses all the combinations, therefore it takes more time to finish. Both, `"partial"` and `"total"` provide average results.
`...`	Further arguments to be passed to `VAR` function from vars package.

Value

A zoo object holding total dynamic index.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Examples

data(dy2012)
O_index <- total.dynamic.spillover(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, p=4)
 
# Orthogonalized rolling spillover index based on a VAR(4), single order
 O_index <- total.dynamic.spillover(as.zoo(dy2012[,-1]), width = 200, p=4)
# Generalized rolling spillover index based on a VAR(4)
 G_index<- total.dynamic.spillover(as.zoo(dy2012[,-1]), width = 200, index="generalized", p=4) 
# A comparison: (warning: It can take several minutes.)
 single <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4)
 partial <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, 
 ortho.type = "partial")
 total <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4,
 ortho.type = "total")
 out <- cbind(single, partial, total)
 head(out)
 plot(out, col=1:3, main="Spillover index")

data(dy2012)
O_index <- total.dynamic.spillover(as.zoo(dy2012[1:300,c(2,3,4)]), width = 200, p=4)
 
# Orthogonalized rolling spillover index based on a VAR(4), single order
 O_index <- total.dynamic.spillover(as.zoo(dy2012[,-1]), width = 200, p=4)
# Generalized rolling spillover index based on a VAR(4)
 G_index<- total.dynamic.spillover(as.zoo(dy2012[,-1]), width = 200, index="generalized", p=4) 
# A comparison: (warning: It can take several minutes.)
 single <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4)
 partial <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4, 
 ortho.type = "partial")
 total <- total.dynamic.spillover(as.zoo(dy2012[1:1200,2:4]), width = 200, p=4,
 ortho.type = "total")
 out <- cbind(single, partial, total)
 head(out)
 plot(out, col=1:3, main="Spillover index")

Package 'Spillover'

Help Index

Diebold and Yilmaz (2009) dataset

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Diebold and Yilmaz (2012) dataset

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dynamnic_spillover is a function for estimating DY directional spillovers.

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Generalized Forecast Error Variance Decomposition

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Generalized spillover index

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Net spillovers

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Orthogonalized spillover index

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Plot directional spillover index

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Two-days Rolling Average Returns

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Two-days Rolling Average Intra-day Volatilities

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Dynamic Spillover Index

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Dynamic Spillover Index

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