[Returnanalytics-commits] r3658 - in pkg/Dowd: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri May 29 23:20:42 CEST 2015
Author: dacharya
Date: 2015-05-29 23:20:41 +0200 (Fri, 29 May 2015)
New Revision: 3658
Added:
pkg/Dowd/R/BootstrapESConfInterval.R
pkg/Dowd/R/BootstrapESFigure.R
pkg/Dowd/R/BootstrapVaR.R
pkg/Dowd/R/BootstrapVaRConfInterval.R
pkg/Dowd/R/BootstrapVaRFigure.R
pkg/Dowd/man/BootstrapESConfInterval.Rd
pkg/Dowd/man/BootstrapESFigure.Rd
pkg/Dowd/man/BootstrapVaR.Rd
pkg/Dowd/man/BootstrapVarConfInterval.Rd
Modified:
pkg/Dowd/DESCRIPTION
pkg/Dowd/NAMESPACE
pkg/Dowd/R/BootstrapES.R
pkg/Dowd/R/HSES.R
pkg/Dowd/man/BootstrapES.Rd
pkg/Dowd/readme.txt
Log:
Source and documentation for BootstrapES, BootstrapESConfInterval, BootstrapESFigure, BootstrapVaR, BootstrapVaRConfInterval, BootstrapVaRFigure,
Modified: pkg/Dowd/DESCRIPTION
===================================================================
--- pkg/Dowd/DESCRIPTION 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/DESCRIPTION 2015-05-29 21:20:41 UTC (rev 3658)
@@ -1,12 +1,14 @@
Package: Dowd
Type: Package
-Title: R-version of Matlab Toolbox offered in Kevin Dowd's book Measuring Market Risk
+Title: R-version of MMR II toolbox offered in Kevin Dowd's book Measuring Market Risk
Version: 0.1
Date: 2015-05-24
Author: Dinesh Acharya <dines.acharya at gmail.com>
Maintainer: Dinesh Acharya <dines.acharya at gmail.com>
-Description:
-Depends: R (>= 2.14.0)
-Suggests: PerformanceAnalytics,
- testthat
+Description: This package is R-version of MMR2 Toolbox that supplements
+ Kevin Dowd's book measuring market risk.
+Depends: R (>= 3.0.0),
+ bootstrap
+Suggests: PerformanceAnalytics,
+ testthat
License: GNU Public License
Modified: pkg/Dowd/NAMESPACE
===================================================================
--- pkg/Dowd/NAMESPACE 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/NAMESPACE 2015-05-29 21:20:41 UTC (rev 3658)
@@ -4,6 +4,10 @@
export(BinomialBacktest)
export(BlancoIhleBacktest)
export(BootstrapES)
+export(BootstrapESConfInterval)
+export(BootstrapESFigure)
+export(BootstrapVaR)
+export(BootstrapVarConfInterval)
export(ChristoffersenBacktestForIndependence)
export(ChristoffersenBacktestForUnconditionalCoverage)
export(HSES)
Modified: pkg/Dowd/R/BootstrapES.R
===================================================================
--- pkg/Dowd/R/BootstrapES.R 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/R/BootstrapES.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -4,8 +4,8 @@
#' implied by data frequency.
#'
#' @param Ra Vector corresponding to profit and loss distribution
-#' @param number.sample Number of samples to be taken in bootstrap procedure
-#' @return cl Number corresponding to Value at Risk confidence level
+#' @param number.resamples Number of samples to be taken in bootstrap procedure
+#' @return cl Number corresponding to Expected Shortfall confidence level
#'
#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
#'
@@ -13,12 +13,12 @@
#' @author Dinesh Acharya
#' @examples
#'
-#' # Estimates bootstrapped Es for given parameters
+#' # Estimates bootstrapped ES for given parameters
#' a <- rnorm(100) # generate a random profit/loss vector
-#' BootstrappedES(a, 50, 0.95)
+#' BootstrapVaR(a, 50, 0.95)
#'
#' @export
-BootstrapES <- function(Ra, number.sample, cl){
+BootstrapES <- function(Ra, number.resamples, cl){
if (nargs() < 3){
error("Too few arguments")
@@ -37,7 +37,7 @@
if (length(cl) != 1) {
error("Confidence level must be a scalar")
}
- if (length(number.samples) != 1){
+ if (length(number.resamples) != 1){
error("Number of resamples must be a scalar");
}
@@ -45,16 +45,17 @@
if (cl >= 1){
stop("Confidence level must be less that 1")
}
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
if (number.resamples <= 0){
stop("Number of resamples must be at least 0")
}
- #############################################
- # suitable alternative to bootstrp in R is still to be explored.
- #############################################
+ # Load bootstrap package
+ library(bootstrap)
# ES estimation
- #
- # es <- bootstrp(number.resamples, "hses", losses.data, cl)
- # y <- mean(es)
- # return (y)
+ es <- bootstrap(losses.data, number.resamples, HSES, cl)$thetastar
+ y <- mean(es)
+ return (y)
}
\ No newline at end of file
Added: pkg/Dowd/R/BootstrapESConfInterval.R
===================================================================
--- pkg/Dowd/R/BootstrapESConfInterval.R (rev 0)
+++ pkg/Dowd/R/BootstrapESConfInterval.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,66 @@
+#' Bootstrapped ES Confidence Interval
+#'
+#' Estimates the 90% confidence interval for bootstrapped ES, for confidence
+#' level and holding period implied by data frequency.
+#'
+#' @param Ra Vector corresponding to profit and loss distribution
+#' @param number.resample Number of samples to be taken in bootstrap procedure
+#' @param cl Number corresponding to Expected Shortfall confidence level
+#' @return 90% Confidence interval for bootstrapped ES
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # To be modified with appropriate data.
+#' # Estimates 90% confidence interval for bootstrapped ES for 95%
+#' # confidence interval
+#' Ra <- rnorm(1000)
+#' BootstrapESConfInterval(Ra, 50, 0.95)
+#'
+#' @export
+BootstrapESConfInterval <- function(Ra, number.resamples, cl){
+
+ # Determine if there are three arguments
+ if (nargs() < 3){
+ stop("Too few arguments")
+ }
+ if (nargs() > 3){
+ stop("Too many arguments")
+ }
+
+ profit.loss.data <- as.vector(Ra)
+
+ # Preprocess data
+ unsorted.loss.data <- -profit.loss.data # Derives L/P data from input P/L data
+ losses.data <- sort(unsorted.loss.data) # Puts losses in ascending order
+ n <- length(losses.data)
+
+ # Check that inputs have correct dimensions
+ if (is.vector(cl) & (length(cl) != 1) ) {
+ error("Confidence level must be a scalar")
+ }
+ if (length(number.resamples) != 1) {
+ error("Number of resamples must be a scalar")
+ }
+ # Check that inputs obey sign and value restrictions
+ if (cl >= 1){
+ stop("Confidence level must be less that 1")
+ }
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
+ if (number.resamples <= 0){
+ stop("Number of resamples must be at least 0")
+ }
+
+ library(bootstrap)
+
+ # ES estimation
+ es <- bootstrap(losses.data, number.resamples, HSES, cl)[1]
+ y <- quantile(es, c(.05, .95))
+ return(y)
+
+}
\ No newline at end of file
Added: pkg/Dowd/R/BootstrapESFigure.R
===================================================================
--- pkg/Dowd/R/BootstrapESFigure.R (rev 0)
+++ pkg/Dowd/R/BootstrapESFigure.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,72 @@
+#' Plots figure of bootstrapped ES
+#'
+#' Plots figure for the bootstrapped ES, for confidence
+#' level and holding period implied by data frequency.
+#'
+#' @param Ra Vector corresponding to profit and loss distribution
+#' @param number.resample Number of samples to be taken in bootstrap procedure
+#' @param cl Number corresponding to Expected Shortfall confidence level
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # To be modified with appropriate data.
+#' # Estimates 90% confidence interval for bootstrapped ES for 95%
+#' # confidence interval
+#' Ra <- rnorm(1000)
+#' BootstrapESFigure(Ra, 500, 0.95)
+#'
+#' @export
+BootstrapESFigure <- function(Ra, number.resamples, cl){
+
+ # Determine if there are three arguments
+ if (nargs() < 3){
+ stop("Too few arguments")
+ }
+ if (nargs() > 3){
+ stop("Too many arguments")
+ }
+
+ profit.loss.data <- as.vector(Ra)
+
+ # Preprocess data
+ unsorted.loss.data <- -profit.loss.data # Derives L/P data from input P/L data
+ losses.data <- sort(unsorted.loss.data) # Puts losses in ascending order
+ n <- length(losses.data)
+
+ # Check that inputs have correct dimensions
+ if (is.vector(cl) & (length(cl) != 1) ) {
+ error("Confidence level must be a scalar")
+ }
+ if (length(number.resamples) != 1) {
+ error("Number of resamples must be a scalar")
+ }
+ # Check that inputs obey sign and value restrictions
+ if (cl >= 1){
+ stop("Confidence level must be less that 1")
+ }
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
+ if (number.resamples <= 0){
+ stop("Number of resamples must be at least 0")
+ }
+
+ library(bootstrap)
+
+ # ES Estimation
+ es <- bootstrap(losses.data, number.resamples, HSES, cl)$thetastar
+ mean.es <- mean(es)
+ std.es <- sd(es)
+ min.es <- min(es)
+ max.es <- max(es)
+ ninety.five.perc.conf.interval <- quantile(es, c(.05, .95))
+
+ # Histogram
+ cl.for.label <- 100*cl
+ hist(es, 30, xlab="ES", ylab="Frequency", main=paste("Bootstrapped Historical Simulation ES at", cl, "% Confidence Level"))
+
+}
\ No newline at end of file
Added: pkg/Dowd/R/BootstrapVaR.R
===================================================================
--- pkg/Dowd/R/BootstrapVaR.R (rev 0)
+++ pkg/Dowd/R/BootstrapVaR.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,61 @@
+#' Bootstrapped VaR for specified confidence level
+#'
+#' Estimates the bootstrapped VaR for confidence level and holding period
+#' implied by data frequency.
+#'
+#' @param Ra Vector corresponding to profit and loss distribution
+#' @param number.sample Number of samples to be taken in bootstrap procedure
+#' @return cl Number corresponding to Value at Risk confidence level
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # Estimates bootstrapped VaR for given parameters
+#' a <- rnorm(100) # generate a random profit/loss vector
+#' BootstrapES(a, 50, 0.95)
+#'
+#' @export
+BootstrapVaR <- function(Ra, number.sample, cl){
+
+ if (nargs() < 3){
+ error("Too few arguments")
+ }
+ if (nargs() > 3){
+ error("Too many arguments")
+ }
+
+ profit.loss.data <- as.vector(Ra)
+ # Preprocess data
+ unsorted.loss.data <- -profit.loss.data
+ losses.data <- sort(unsorted.loss.data)
+ n <- length(losses.data)
+
+ # Check that inputs have correct dimensions
+ if (length(cl) != 1) {
+ error("Confidence level must be a scalar")
+ }
+ if (length(number.samples) != 1){
+ error("Number of resamples must be a scalar");
+ }
+
+ # Check that inputs obey sign and value restrictions
+ if (cl >= 1){
+ stop("Confidence level must be less that 1")
+ }
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
+ if (number.resamples <= 0){
+ stop("Number of resamples must be at least 0")
+ }
+
+ # Load bootstrap package
+ library(bootstrap)
+ # ES estimation
+ VaR <- bootstrap(losses.data, number.resamples, HSVaR, cl)$thetastar
+ y <- mean(VaR)
+ return (y)
+}
\ No newline at end of file
Added: pkg/Dowd/R/BootstrapVaRConfInterval.R
===================================================================
--- pkg/Dowd/R/BootstrapVaRConfInterval.R (rev 0)
+++ pkg/Dowd/R/BootstrapVaRConfInterval.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,66 @@
+#' Bootstrapped VaR Confidence Interval
+#'
+#' Estimates the 90% confidence interval for bootstrapped VaR, for confidence
+#' level and holding period implied by data frequency.
+#'
+#' @param Ra Vector corresponding to profit and loss distribution
+#' @param number.sample Number of samples to be taken in bootstrap procedure
+#' @param cl Number corresponding to Value at Risk confidence level
+#' @return 90% Confidence interval for bootstrapped VaR
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # To be modified with appropriate data.
+#' # Estimates 90% confidence interval for bootstrapped Var for 95%
+#' # confidence interval
+#' Ra <- rnorm(1000)
+#' BootstrapVarConfInterval(Ra, 500, 0.95)
+#'
+#' @export
+BootstrapVarConfInterval <- function(Ra, number.resamples, cl){
+
+ # Determine if there are three arguments
+ if (nargs() < 3){
+ stop("Too few arguments")
+ }
+ if (nargs() > 3){
+ stop("Too many arguments")
+ }
+
+ profit.loss.data <- as.vector(Ra)
+
+ # Preprocess data
+ unsorted.loss.data <- -profit.loss.data # Derives L/P data from input P/L data
+ losses.data <- sort(unsorted.loss.data) # Puts losses in ascending order
+ n <- length(losses.data)
+
+ # Check that inputs have correct dimensions
+ if (is.vector(cl) & (length(cl) != 1) ) {
+ error("Confidence level must be a scalar")
+ }
+ if (length(number.resamples) != 1) {
+ error("Number of resamples must be a scalar")
+ }
+ # Check that inputs obey sign and value restrictions
+ if (cl >= 1){
+ stop("Confidence level must be less that 1")
+ }
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
+ if (number.resamples <= 0){
+ stop("Number of resamples must be at least 0")
+ }
+
+ library(bootstrap)
+
+ # VaR estimation
+ VaR <- bootstrap(losses.data, number.resamples, HSVaR, cl)$thetastar
+ y <- quantile(VaR, c(.05, .95))
+ return(y)
+
+}
\ No newline at end of file
Added: pkg/Dowd/R/BootstrapVaRFigure.R
===================================================================
--- pkg/Dowd/R/BootstrapVaRFigure.R (rev 0)
+++ pkg/Dowd/R/BootstrapVaRFigure.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,72 @@
+#' Plots figure of bootstrapped VaR
+#'
+#' Plots figure for the bootstrapped VaR, for confidence
+#' level and holding period implied by data frequency.
+#'
+#' @param Ra Vector corresponding to profit and loss distribution
+#' @param number.sample Number of samples to be taken in bootstrap procedure
+#' @param cl Number corresponding to Value at Risk confidence level
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # To be modified with appropriate data.
+#' # Estimates 90% confidence interval for bootstrapped VaR for 95%
+#' # confidence interval
+#' Ra <- rnorm(1000)
+#' BootstrapESFigure(Ra, 500, 0.95)
+#'
+#' @export
+BootstrapESFigure <- function(Ra, number.resamples, cl){
+
+ # Determine if there are three arguments
+ if (nargs() < 3){
+ stop("Too few arguments")
+ }
+ if (nargs() > 3){
+ stop("Too many arguments")
+ }
+
+ profit.loss.data <- as.vector(Ra)
+
+ # Preprocess data
+ unsorted.loss.data <- -profit.loss.data # Derives L/P data from input P/L data
+ losses.data <- sort(unsorted.loss.data) # Puts losses in ascending order
+ n <- length(losses.data)
+
+ # Check that inputs have correct dimensions
+ if (is.vector(cl) & (length(cl) != 1) ) {
+ error("Confidence level must be a scalar")
+ }
+ if (length(number.resamples) != 1) {
+ error("Number of resamples must be a scalar")
+ }
+ # Check that inputs obey sign and value restrictions
+ if (cl >= 1){
+ stop("Confidence level must be less that 1")
+ }
+ if (cl <= 0){
+ stop("Confidence level must be at least 0")
+ }
+ if (number.resamples <= 0){
+ stop("Number of resamples must be at least 0")
+ }
+
+ library(bootstrap)
+
+ # ES Estimation
+ VaR <- bootstrap(losses.data, number.resamples, HSVaR, cl)$thetastar
+ mean.VaR <- mean(VaR)
+ std.VaR <- sd(VaR)
+ min.VaR <- min(VaR)
+ max.VaR <- max(VaR)
+ ninety.five.perc.conf.interval <- quantile(VaR, c(.05, .95))
+
+ # Histogram
+ cl.for.label <- 100*cl
+ hist(VaR[1], 30, xlab="VaR", ylab="Frequency", main=paste("Bootstrapped Historical Simulation VaR at", cl, "% Confidence Level"))
+
+}
\ No newline at end of file
Modified: pkg/Dowd/R/HSES.R
===================================================================
--- pkg/Dowd/R/HSES.R 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/R/HSES.R 2015-05-29 21:20:41 UTC (rev 3658)
@@ -41,7 +41,6 @@
stop("Too many arguments")
}
-
if (nargs() == 2) {
profit.loss.data <- as.vector(Ra)
unsorted.loss.data <- -profit.loss.data # Derives L/P data from input P/L
@@ -54,7 +53,6 @@
stop('Confidence level must be scalar (length-1 vector in R)')
}
-
# Check that inputs obey sign and value restrictions
if (cl >= 1) {
stop("Confidence level must be less than 1.")
@@ -63,14 +61,15 @@
stop("Confidence level must be positive")
}
+ # VaR and ES estimation
index <- n*cl # This putative index value may or may not be an integer
# Each case needs to be considered in turn
# If index value is an integegr, VaR follows immediately and then we
# estimate ES
if (index-round(index)==0){
- var <- losses.data[index] # Historical Value at Risk
- k <- which[var <= losses.data] # Finds indices of tail loss data
+ VaR <- losses.data[index] # Historical Value at Risk
+ k <- which(VaR <= losses.data) # Finds indices of tail loss data
tail.losses <- losses.data[k] # Creates data set of tail loss observations
es <- mean(tail.losses) # Expected Shortfall
y <- es
@@ -82,27 +81,32 @@
if (index-round(index) != 0){
# Deal with loss
upper.index <- ceiling(index)
- upper.var <- losses.data(upper.index) # Upper VaR
- upper.k <- which(upper.var<=losses.data) # Finds indices of upper tail loss data
- upper.tail.losses <- losses.data(upper.k) # Creates data set of upper tail loss obs.
+ upper.VaR <- losses.data[upper.index] # Upper VaR
+ upper.k <- which(upper.VaR<=losses.data) # Finds indices of upper tail loss data
+ upper.tail.losses <- losses.data[upper.k] # Creates data set of upper tail loss obs.
+ upper.es <- mean(upper.tail.losses) # Upper ES
+ # Deal with loss observation just below VaR to derive lower ES
+ lower.index <- ceil(index)
+ lower.VaR <- losses.data[lower.index] # Lower VaR
+ lower.k <- which(lower.VaR <= losses.data) # Finds indices of lower tail loss data
+ lower.tail.losses <- losses.data[lower.k] # Creates data set of lower tail loss obs.
+ lower.es <- mean(lower.tail.losses)# Lower ES
+
lower.es <- mean(lower.tail.losses) # Lower Expected Shortfall (ES)
# If lower and upper indices are the same, ES is upper ES
if (upper.index == lower.index){
y <- upper.es
}
- # If lower and upper indices are different, ES is weighted average of
+ # If lower and upper indices are different, ES is weighted average of
# upper and lower ESs
if (upper.index!=lower.index) {
# Weights attached to upper and lower ESs
- lower.weight <- (upper.index-index)/(upper.index-lower.index)
- upper.weight <- (index-lower.index)/(upper.index-lower.index)
+ lower.weight <- (upper.index-index)/(upper.index-lower.index) # weight on upper_var
+ upper.weight <- (index-lower.index)/(upper.index-lower.index) # weight on upper_var
# Finally, the weighted, ES as a linear interpolation of upper and lower
# ESs
y <- lower.weight*lower.es+upper.weight*upper.es
-
}
- return(y)
}
-
-}
-
+ return(y)
+}
\ No newline at end of file
Modified: pkg/Dowd/man/BootstrapES.Rd
===================================================================
--- pkg/Dowd/man/BootstrapES.Rd 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/man/BootstrapES.Rd 2015-05-29 21:20:41 UTC (rev 3658)
@@ -4,24 +4,24 @@
\alias{BootstrapES}
\title{Bootstrapped ES for specified confidence level}
\usage{
-BootstrapES(Ra, number.sample, cl)
+BootstrapES(Ra, number.resamples, cl)
}
\arguments{
\item{Ra}{Vector corresponding to profit and loss distribution}
-\item{number.sample}{Number of samples to be taken in bootstrap procedure}
+\item{number.resamples}{Number of samples to be taken in bootstrap procedure}
}
\value{
-cl Number corresponding to Value at Risk confidence level
+cl Number corresponding to Expected Shortfall confidence level
}
\description{
Estimates the bootstrapped ES for confidence level and holding period
implied by data frequency.
}
\examples{
-# Estimates bootstrapped Es for given parameters
+# Estimates bootstrapped ES for given parameters
a <- rnorm(100) # generate a random profit/loss vector
- BootstrappedES(a, 50, 0.95)
+ BootstrapVaR(a, 50, 0.95)
}
\author{
Dinesh Acharya
Added: pkg/Dowd/man/BootstrapESConfInterval.Rd
===================================================================
--- pkg/Dowd/man/BootstrapESConfInterval.Rd (rev 0)
+++ pkg/Dowd/man/BootstrapESConfInterval.Rd 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,36 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/BootstrapESConfInterval.R
+\name{BootstrapESConfInterval}
+\alias{BootstrapESConfInterval}
+\title{Bootstrapped ES Confidence Interval}
+\usage{
+BootstrapESConfInterval(Ra, number.resamples, cl)
+}
+\arguments{
+\item{Ra}{Vector corresponding to profit and loss distribution}
+
+\item{cl}{Number corresponding to Expected Shortfall confidence level}
+
+\item{number.resample}{Number of samples to be taken in bootstrap procedure}
+}
+\value{
+90% Confidence interval for bootstrapped ES
+}
+\description{
+Estimates the 90% confidence interval for bootstrapped ES, for confidence
+level and holding period implied by data frequency.
+}
+\examples{
+# To be modified with appropriate data.
+ # Estimates 90\% confidence interval for bootstrapped ES for 95\%
+ # confidence interval
+ Ra <- rnorm(1000)
+ BootstrapESConfInterval(Ra, 50, 0.95)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
Added: pkg/Dowd/man/BootstrapESFigure.Rd
===================================================================
--- pkg/Dowd/man/BootstrapESFigure.Rd (rev 0)
+++ pkg/Dowd/man/BootstrapESFigure.Rd 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,53 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/BootstrapESFigure.R, R/BootstrapVaRFigure.R
+\name{BootstrapESFigure}
+\alias{BootstrapESFigure}
+\title{Plots figure of bootstrapped ES}
+\usage{
+BootstrapESFigure(Ra, number.resamples, cl)
+
+BootstrapESFigure(Ra, number.resamples, cl)
+}
+\arguments{
+\item{Ra}{Vector corresponding to profit and loss distribution}
+
+\item{cl}{Number corresponding to Expected Shortfall confidence level}
+
+\item{number.resample}{Number of samples to be taken in bootstrap procedure}
+
+\item{Ra}{Vector corresponding to profit and loss distribution}
+
+\item{number.sample}{Number of samples to be taken in bootstrap procedure}
+
+\item{cl}{Number corresponding to Value at Risk confidence level}
+}
+\description{
+Plots figure for the bootstrapped ES, for confidence
+level and holding period implied by data frequency.
+
+Plots figure for the bootstrapped VaR, for confidence
+level and holding period implied by data frequency.
+}
+\examples{
+# To be modified with appropriate data.
+ # Estimates 90\% confidence interval for bootstrapped ES for 95\%
+ # confidence interval
+ Ra <- rnorm(1000)
+ BootstrapESFigure(Ra, 500, 0.95)
+# To be modified with appropriate data.
+ # Estimates 90\% confidence interval for bootstrapped VaR for 95\%
+ # confidence interval
+ Ra <- rnorm(1000)
+ BootstrapESFigure(Ra, 500, 0.95)
+}
+\author{
+Dinesh Acharya
+
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
Added: pkg/Dowd/man/BootstrapVaR.Rd
===================================================================
--- pkg/Dowd/man/BootstrapVaR.Rd (rev 0)
+++ pkg/Dowd/man/BootstrapVaR.Rd 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,32 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/BootstrapVaR.R
+\name{BootstrapVaR}
+\alias{BootstrapVaR}
+\title{Bootstrapped VaR for specified confidence level}
+\usage{
+BootstrapVaR(Ra, number.sample, cl)
+}
+\arguments{
+\item{Ra}{Vector corresponding to profit and loss distribution}
+
+\item{number.sample}{Number of samples to be taken in bootstrap procedure}
+}
+\value{
+cl Number corresponding to Value at Risk confidence level
+}
+\description{
+Estimates the bootstrapped VaR for confidence level and holding period
+implied by data frequency.
+}
+\examples{
+# Estimates bootstrapped VaR for given parameters
+ a <- rnorm(100) # generate a random profit/loss vector
+ BootstrapES(a, 50, 0.95)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
Added: pkg/Dowd/man/BootstrapVarConfInterval.Rd
===================================================================
--- pkg/Dowd/man/BootstrapVarConfInterval.Rd (rev 0)
+++ pkg/Dowd/man/BootstrapVarConfInterval.Rd 2015-05-29 21:20:41 UTC (rev 3658)
@@ -0,0 +1,36 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/BootstrapVaRConfInterval.R
+\name{BootstrapVarConfInterval}
+\alias{BootstrapVarConfInterval}
+\title{Bootstrapped VaR Confidence Interval}
+\usage{
+BootstrapVarConfInterval(Ra, number.resamples, cl)
+}
+\arguments{
+\item{Ra}{Vector corresponding to profit and loss distribution}
+
+\item{cl}{Number corresponding to Value at Risk confidence level}
+
+\item{number.sample}{Number of samples to be taken in bootstrap procedure}
+}
+\value{
+90% Confidence interval for bootstrapped VaR
+}
+\description{
+Estimates the 90% confidence interval for bootstrapped VaR, for confidence
+level and holding period implied by data frequency.
+}
+\examples{
+# To be modified with appropriate data.
+ # Estimates 90\% confidence interval for bootstrapped Var for 95\%
+ # confidence interval
+ Ra <- rnorm(1000)
+ BootstrapVarConfInterval(Ra, 500, 0.95)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
Modified: pkg/Dowd/readme.txt
===================================================================
--- pkg/Dowd/readme.txt 2015-05-29 03:26:24 UTC (rev 3657)
+++ pkg/Dowd/readme.txt 2015-05-29 21:20:41 UTC (rev 3658)
@@ -1,7 +1,5 @@
#***************************************************************
-# most suitable function similar to bootsrtp in matlab is still to be checked
-# original bootstrp VaR so that still needs to be checked.
-# Other functions depending on bootstrp are still only half complete.
+# Bootstrap is almost complete. It is still to be tested/debugged. Tests still remaining.
#***************************************************************
# Jarque-Bera Test:
# It has to be checked Probability of null (H0) or (H1).
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