[Uwgarp-commits] r153 - in pkg/GARPFRM: . R man sandbox vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Mar 30 23:40:52 CEST 2014
Author: tfillebeen
Date: 2014-03-30 23:40:51 +0200 (Sun, 30 Mar 2014)
New Revision: 153
Modified:
pkg/GARPFRM/DESCRIPTION
pkg/GARPFRM/NAMESPACE
pkg/GARPFRM/R/capm.R
pkg/GARPFRM/R/garch11.R
pkg/GARPFRM/man/CAPM.Rd
pkg/GARPFRM/man/EWMA.Rd
pkg/GARPFRM/man/backTestVaR.Rd
pkg/GARPFRM/man/backtestVaR.GARCH.Rd
pkg/GARPFRM/man/bootCor.Rd
pkg/GARPFRM/man/bootCov.Rd
pkg/GARPFRM/man/bootES.Rd
pkg/GARPFRM/man/bootFUN.Rd
pkg/GARPFRM/man/bootMean.Rd
pkg/GARPFRM/man/bootSD.Rd
pkg/GARPFRM/man/bootSimpleVolatility.Rd
pkg/GARPFRM/man/bootStdDev.Rd
pkg/GARPFRM/man/bootVaR.Rd
pkg/GARPFRM/man/chartSML.Rd
pkg/GARPFRM/man/efficientFrontier.Rd
pkg/GARPFRM/man/efficientFrontierTwoAsset.Rd
pkg/GARPFRM/man/endingPrices.Rd
pkg/GARPFRM/man/estimateLambdaCor.Rd
pkg/GARPFRM/man/estimateLambdaCov.Rd
pkg/GARPFRM/man/estimateLambdaVol.Rd
pkg/GARPFRM/man/forecast.Rd
pkg/GARPFRM/man/forecast.uvEWMAvol.Rd
pkg/GARPFRM/man/forecast.uvGARCH.Rd
pkg/GARPFRM/man/getAlphas.Rd
pkg/GARPFRM/man/getBetas.Rd
pkg/GARPFRM/man/getCor.Rd
pkg/GARPFRM/man/getCov.Rd
pkg/GARPFRM/man/getEstimate.Rd
pkg/GARPFRM/man/getStatistics.Rd
pkg/GARPFRM/man/getVaREstimates.Rd
pkg/GARPFRM/man/getVaRViolations.Rd
pkg/GARPFRM/man/hypTest.Rd
pkg/GARPFRM/man/minVarPortfolio.Rd
pkg/GARPFRM/man/monteCarlo.Rd
pkg/GARPFRM/man/plot.EWMA.Rd
pkg/GARPFRM/man/plot.backtestVaR.Rd
pkg/GARPFRM/man/plot.capm_mlm.Rd
pkg/GARPFRM/man/plot.capm_uv.Rd
pkg/GARPFRM/man/plot.efTwoAsset.Rd
pkg/GARPFRM/man/plot.efficient.frontier.Rd
pkg/GARPFRM/man/plotEndingPrices.Rd
pkg/GARPFRM/man/portReturnTwoAsset.Rd
pkg/GARPFRM/man/portSDTwoAsset.Rd
pkg/GARPFRM/man/realizedCor.Rd
pkg/GARPFRM/man/realizedCov.Rd
pkg/GARPFRM/man/realizedVol.Rd
pkg/GARPFRM/man/rollCor.Rd
pkg/GARPFRM/man/rollCov.Rd
pkg/GARPFRM/man/rollSD.Rd
pkg/GARPFRM/man/rollSimpleVolatility.Rd
pkg/GARPFRM/man/simpleVolatility.Rd
pkg/GARPFRM/man/tangentPortfolio.Rd
pkg/GARPFRM/sandbox/test_EWMA_GARCH.R
pkg/GARPFRM/vignettes/CAPM_TF.Rnw
pkg/GARPFRM/vignettes/CAPM_TF.pdf
pkg/GARPFRM/vignettes/DelineatingEfficientPortfolios.pdf
pkg/GARPFRM/vignettes/EstimatingVolatilitiesCorrelation.pdf
Log:
CAPM vignette update
Modified: pkg/GARPFRM/DESCRIPTION
===================================================================
--- pkg/GARPFRM/DESCRIPTION 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/DESCRIPTION 2014-03-30 21:40:51 UTC (rev 153)
@@ -17,15 +17,3 @@
PortfolioAnalytics,
foreach (>= 1.4.1)
License: GPL
-Collate:
- 'backTestVaR.R'
- 'capm.R'
- 'EWMA.R'
- 'garch11.R'
- 'monte_carlo.R'
- 'efficient_frontier.R'
- 'rollFUN.R'
- 'volatility.R'
- 'boot.R'
- 'utils.R'
- 'generic_forecast.R'
Modified: pkg/GARPFRM/NAMESPACE
===================================================================
--- pkg/GARPFRM/NAMESPACE 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/NAMESPACE 2014-03-30 21:40:51 UTC (rev 153)
@@ -1,5 +1,32 @@
-export(backtestVaR.GARCH)
+S3method(fcstGarch11,DCCfit)
+S3method(forecast,uvEWMAvol)
+S3method(forecast,uvGARCH)
+S3method(getAlphas,capm_mlm)
+S3method(getAlphas,capm_uv)
+S3method(getBetas,capm_mlm)
+S3method(getBetas,capm_uv)
+S3method(getCor,mvEWMAcor)
+S3method(getCov,mvEWMAcov)
+S3method(getEstimate,EWMA)
+S3method(getEstimate,mvEWMAvol)
+S3method(getStatistics,capm_mlm)
+S3method(getStatistics,capm_uv)
+S3method(hypTest,capm_mlm)
+S3method(hypTest,capm_uv)
+S3method(plot,EWMA)
+S3method(plot,MonteCarlo)
+S3method(plot,backtestVaR)
+S3method(plot,capm_mlm)
+S3method(plot,capm_uv)
+S3method(plot,efTwoAsset)
+S3method(plot,efficient.frontier)
+S3method(print,EWMA)
+S3method(print,backtestVaR)
+S3method(print,mvEWMAvol)
+export(CAPM)
+export(EWMA)
export(backtestVaR)
+export(backtestVaR.GARCH)
export(bootCor)
export(bootCov)
export(bootES)
@@ -9,7 +36,6 @@
export(bootSimpleVolatility)
export(bootStdDev)
export(bootVaR)
-export(CAPM)
export(chartSML)
export(efficientFrontier)
export(efficientFrontierTwoAsset)
@@ -17,15 +43,14 @@
export(estimateLambdaCor)
export(estimateLambdaCov)
export(estimateLambdaVol)
-export(EWMA)
+export(fcstGarch11)
export(forecast)
+export(garch11)
export(getAlphas)
export(getBetas)
export(getCor)
export(getCov)
export(getEstimate)
-export(getFit)
-export(getSpec)
export(getStatistics)
export(getVaREstimates)
export(getVaRViolations)
@@ -44,30 +69,3 @@
export(rollSimpleVolatility)
export(simpleVolatility)
export(tangentPortfolio)
-export(uvGARCH)
-S3method(forecast,uvEWMAvol)
-S3method(forecast,uvGARCH)
-S3method(getAlphas,capm_mlm)
-S3method(getAlphas,capm_uv)
-S3method(getBetas,capm_mlm)
-S3method(getBetas,capm_uv)
-S3method(getCor,mvEWMAcor)
-S3method(getCov,mvEWMAcov)
-S3method(getEstimate,EWMA)
-S3method(getEstimate,mvEWMAvol)
-S3method(getFit,uvGARCH)
-S3method(getSpec,uvGARCH)
-S3method(getStatistics,capm_mlm)
-S3method(getStatistics,capm_uv)
-S3method(hypTest,capm_mlm)
-S3method(hypTest,capm_uv)
-S3method(plot,backtestVaR)
-S3method(plot,capm_mlm)
-S3method(plot,capm_uv)
-S3method(plot,efficient.frontier)
-S3method(plot,efTwoAsset)
-S3method(plot,EWMA)
-S3method(plot,MonteCarlo)
-S3method(print,backtestVaR)
-S3method(print,EWMA)
-S3method(print,mvEWMAvol)
Modified: pkg/GARPFRM/R/capm.R
===================================================================
--- pkg/GARPFRM/R/capm.R 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/R/capm.R 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,25 +2,25 @@
# Description for CAPM
# @param r risk-free rate
# @param mkrt market return
-# @return the function returns tstat upon default & pvalue when specified
+# @return the function returns tstat upon default & pvalue when spesignificanceLevelfied
# @export
# capm.tstats = function(r,mkrt,type = FALSE) {
-# # Fiting CAPM and retrieve alpha specific tstats or pvalues
+# # Fiting CAPM and retrieve alpha spesignificanceLevelfic tstats or pvalues
# capm.fit = lm(r~mkrt)
# # Extract summary info
# capm.summary = summary(capm.fit)
# if(is.null(type) | type=="pvalue"){
-# # Retrieve p-value if specified
+# # Retrieve p-value if spesignificanceLevelfied
# p.value = coef(capm.summary)[1,4]
# p.value
# }else{
-# # Otherwise retrieve t-stat if specified or on default
+# # Otherwise retrieve t-stat if spesignificanceLevelfied or on default
# t.stat = coef(capm.summary)[1,3]
# t.stat
# }
# }
-#' Capital Asset Pricing Model
+#' Capital Asset PrisignificanceLevelng Model
#'
#' TODO: Need a better description of the CAPM
#'
@@ -119,7 +119,7 @@
#' Extract the standard error, t-values, and p-values from the CAPM object.
#'
#' The t-statistic and corresponding two-sided p-value are calculated differently
-#' for the alpha and beta coefficients.
+#' for the alpha and beta coeffisignificanceLevelents.
#' \itemize{
#' \item{alpha}{ the t-statistic and corresponding p-value are calculated to
#' test if alpha is significantly different from 0.
@@ -252,20 +252,20 @@
# Plot Fitted SML
plot(betas,mu.hat,main=main, ...=...)
abline(sml.fit)
- legend("topleft",1, "Estimated SML",1)
+ # legend("topleft",1, "Estimated SML",1)
}
#' CAPM Hypothesis Test
#'
-#' Test the CAPM coefficients for significance.
+#' Test the CAPM coeffisignificanceLevelents for significance.
#'
#' @details
-#' This function tests the significance of the coefficients (alpha and beta)
+#' This function tests the significance of the coeffisignificanceLevelents (alpha and beta)
#' estimated by the CAPM.
#'
#' #' The t-statistic and corresponding two-sided p-value are calculated differently
-#' for the alpha and beta coefficients.
+#' for the alpha and beta coeffisignificanceLevelents.
#' \itemize{
#' \item{alpha}{ the t-statistic and corresponding p-value are calculated to
#' test if alpha is significantly different from 0.
@@ -281,44 +281,44 @@
#' }
#' }
#'
-#' If the p-value is less than the specified confidence level, the null
-#' hypothesis is rejected meaning that the coefficient is significant. If
-#' the p-value is greater than the specified confidence level, the null
+#' If the p-value is less than the spesignificanceLevelfied confidence level, the null
+#' hypothesis is rejected meaning that the coeffisignificanceLevelent is significant. If
+#' the p-value is greater than the spesignificanceLevelfied confidence level, the null
#' hypothesis cannot be rejected.
#'
#' @param object a capm object created by \code{\link{CAPM}}
-#' @param CI confidence level
-#' @return TRUE if the null hypothesis is rejected (i.e. the estimated coefficient is significant)
-#' FALSE if the null hypothesis cannot be rejected (i.e. the estimated coefficient is not significant)
+#' @param significanceLevel confidence level
+#' @return TRUE if the null hypothesis is rejected (i.e. the estimated coeffisignificanceLevelent is significant)
+#' FALSE if the null hypothesis cannot be rejected (i.e. the estimated coeffisignificanceLevelent is not significant)
#' @seealso \code{\link{getStatistics}}
#' @author Thomas Fillebeen
#' @export
-hypTest <- function(object,CI){
+hypTest <- function(object,significanceLevel){
UseMethod("hypTest")
}
#' @method hypTest capm_uv
#' @S3method hypTest capm_uv
-hypTest.capm_uv <- function(object, CI = 0.05){
+hypTest.capm_uv <- function(object, significanceLevel = 0.05){
if(!inherits(object, "capm_uv")) stop("object must be of class capm_uv")
tmp_sm = getStatistics(object)
- # test for alpha p-value < CI
- tmp_A = tmp_sm[1,4] < CI
- # test for beta p-value < CI
- tmp_B = tmp_sm[2,4] < CI
+ # test for alpha p-value < significanceLevel
+ tmp_A = tmp_sm[1,4] < significanceLevel
+ # test for beta p-value < significanceLevel
+ tmp_B = tmp_sm[2,4] < significanceLevel
result = list(alpha = tmp_A, beta = tmp_B)
return(result)
}
#' @method hypTest capm_mlm
#' @S3method hypTest capm_mlm
-hypTest.capm_mlm <- function(object, CI = 0.05){
+hypTest.capm_mlm <- function(object, significanceLevel = 0.05){
if(!inherits(object, "capm_mlm")) stop("object must be of class capm_mlm")
tmp_sm = getStatistics(object)
- # test for alpha p-value < CI
- tmp_A = tmp_sm[seq(1,nrow(tmp_sm),2),4] < CI
- # test for beta p-value < CI
- tmp_B = tmp_sm[seq(2,nrow(tmp_sm),2),4] < CI
+ # test for alpha p-value < significanceLevel
+ tmp_A = tmp_sm[seq(1,nrow(tmp_sm),2),4] < significanceLevel
+ # test for beta p-value < significanceLevel
+ tmp_B = tmp_sm[seq(2,nrow(tmp_sm),2),4] < significanceLevel
result = list(alpha = tmp_A, beta = tmp_B)
return(result)
}
Modified: pkg/GARPFRM/R/garch11.R
===================================================================
--- pkg/GARPFRM/R/garch11.R 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/R/garch11.R 2014-03-30 21:40:51 UTC (rev 153)
@@ -1,204 +1,50 @@
-
-# Because our function is a wrapper around functions in the rugarch and
-# rmgarch packages, we need to give proper credit (i.e. citation("rugarch")
-# and citation("rmgarch"))
-
-# rugarch: univariate garch models
-# rmgarch: multivariate garch models
-
-# we need to support GARCH models for both univariate and multivariate data
-
-## The GARP text does not have any discussion on multivariate GARCH models.
-## I think we should omit this for phase 1 and maybe reconsider in phase 2
-## or beyond.
-
-# GARCH Models
-#
-# This function is a basic wrapper of functions in the rugarch and rmgarch
-# packages to specify and fit GARCH models. The rugarch and rmgarch packages
-# provide functions to specify and fit a rich set of GARCH models.
-# The purpose of this function is to specify and fit a GARCH model while
-# abstracting away some complexities.
-#
-# The rugarch package implements univariate garch models and the
-# rmgarch package implements multivariate garch models. Univariate or
-# multivariate data is automatically detected and the appropriate GARCH model
-# will be specified and fit.
-#
-# For complete functionality of GARCH models, it is recommended to
-# directly use functions in the rugarch and rmgarch packages.
-#
-# @param R xts object of asset returns
-# @param model “sGARCH”, “fGARCH”, “eGARCH”, “gjrGARCH”, “apARCH” and “iGARCH” and “csGARCH”
-# @param distribution.model. Valid choices are “norm” for the normal distibution, “snorm” for the skew-normal distribution, “std” for the student-t, “sstd” for the skew-student, “ged” for the generalized error distribution, “sged” for the skew-generalized error distribution, “nig” for the normal inverse gaussian distribution, “ghyp” for the Generalized Hyperbolic, and “jsu” for Johnson's SU distribution.
-# @export
-# By default we use UV N~GARCH(1,1) and Bollerslev for each series
-# garch11 <- function(R, model = "sGARCH", distribution.model = "norm"){
-# # if univariate data, load the rugarch package
-# # if multivariate data, load the rmgarch package
-#
-# garch11.spec = ugarchspec(mean.model = list(armaOrder = c(0,0)),
-# variance.model = list(garchOrder = c(1,1), model = model),
-# distribution.model)
-#
-# # DCC specification: GARCH(1,1) for conditional cor
-# nbColumns = ncol(R)
-# dcc.garch11.spec = dccspec(uspec = multispec( replicate(nbColumns, garch11.spec) ),
-# dccOrder = c(1,1), distribution = "mvnorm")
-# dcc.garch11.spec
-#
-# dcc.fit = dccfit(dcc.garch11.spec, data = R)
-# class(dcc.fit)
-# slotNames(dcc.fit)
-# names(dcc.fit at mfit)
-# names(dcc.fit at model)
-# return(dcc.fit)
-# }
-
-# Forecast GARCH(1,1)
-#
-# Description of forecast GARCH(1,1)
-#
-# @param garch11 object created by \code{\link{garch11}}
-# @param window is the forecast window (default is set to window = 100)
-# @export
-# fcstGarch11 <- function(object, window){
-# UseMethod("fcstGarch11")
-# }
-
-# @method fcstGarch11 Dccfit
-# @S3method fcstGarch11 DCCfit
-# fcstGarch11.DCCfit <- function(object, window = 100){
-# #if ((window > nrow(object))) {stop("Window is too large to forecast")}
-# result = dccforecast(object, n.ahead=window)
-# class(result)
-# slotNames(result)
-# class(result at mforecast)
-# names(result at mforecast)
-# return(result)
-# }
-
-
-#' Univariate GARCH Model
+#' GARCH(1,1)
#'
-#' Specify and fit a univariate GARCH model
+#' Description of GARCH(1,1)
#'
-#' @details
-#' This function is a basic wrapper of functions in the rugarch package
-#' to specify and fit GARCH models. The rugarch package
-#' provides functions to specify and fit a rich set of GARCH models.
-#' The purpose of this function is to specify and fit a GARCH model while
-#' abstracting away some complexities.
-#'
-#' @param R xts object of asset returns.
-#' @param model GARCH Model to specify and fit. Valid GARCH models are
-#' "sGARCH", "fGARCH", "eGARCH", "gjrGARCH", "apARCH", "iGARCH", and "csGARCH".
-#' @param garchOrder the ARCH(q) and GARCH(p) orders.
-#' @param armaOrder the autoregressive and moving average orders.
-#' @param distribution conditional density to use for the innovations. Valid
-#' distributions are "norm" for the normal distibution, "snorm" for the
-#' skew-normal distribution, "std" for the student-t,
-#' "sstd for the skew-student, "ged" for the generalized error distribution,
-#' "sged" for the skew-generalized error distribution,
-#' "nig" for the normal inverse gaussian distribution,
-#' "ghyp" for the Generalized Hyperbolic, and "jsu" for Johnson's SU distribution.
-#' @param fixedParams named list of parameters to keep fixed.
-#' @param solver the solver to use to fit the GARCH model. Valid solvers are
-#' "nlminb", "solnp", "lbfgs", "gosolnp", "nloptr", or "hybrid".
-#' @param outSample number of periods of data used to fit the model.
-#' \code{nrow(R) - outSample} number of periods to keep as out of sample data
-#' points.
-#' @param fitControl named list of arguments for the fitting routine
-#' @param solverControl named list of arguments for the solver
-#' @return a list of length two containing GARCH specification and GARCH fit objects
-#' @author Ross Bennett
-#' @seealso \code{\link[rugarch]{ugarchspec}}, \code{\link[rugarch]{ugarchfit}}
+#' @param R GARCH(1,1)
+#' @param model “sGARCH”, “fGARCH”, “eGARCH”, “gjrGARCH”, “apARCH” and “iGARCH” and “csGARCH”
+#' @param distribution.model. Valid choices are “norm” for the normal distibution, “snorm” for the skew-normal distribution, “std” for the student-t, “sstd” for the skew-student, “ged” for the generalized error distribution, “sged” for the skew-generalized error distribution, “nig” for the normal inverse gaussian distribution, “ghyp” for the Generalized Hyperbolic, and “jsu” for Johnson's SU distribution.
#' @export
-uvGARCH <- function(R, model="sGARCH",
- garchOrder=c(1, 1),
- armaOrder=c(1,1),
- distribution="norm",
- fixedParams=NULL,
- solver="hybrid",
- outSample=0,
- fitControl=NULL,
- solverControl=NULL){
- # Function to specify and fit a univariate GARCH model
-
- stopifnot("package:rugarch" %in% search() || require("rugarch", quietly = TRUE))
-
- if(is.null(fixedParams)){
- fixedParams <- list()
- }
-
- # Specify the GARCH model
- # uGARCHspec object
- spec <- ugarchspec(variance.model=list(model=model, garchOrder=garchOrder),
- mean.model=list(armaOrder=armaOrder),
- distribution.model=distribution,
- fixed.pars=fixedParams)
-
- # Fit the GARCH model
- # uGARCHfit object
-
- if(is.null(fitControl)){
- fitControl <- list(stationarity = 1, fixed.se = 0, scale = 0, rec.init = 'all')
- }
-
- if(is.null(solverControl)){
- solverControl <- list()
- }
-
- fit <- ugarchfit(spec=spec, data=R, out.sample=outSample, solver=solver,
- fit.control=fitControl, solver.control=solverControl)
-
- # structure and return the univariate GARCH model specification and fit
- return(structure(list(spec=spec, fit=fit),
- class="uvGARCH"))
-}
+# By default we use UV N~GARCH(1,1) and Bollerslev for each series
+garch11 <- function(R, model = "sGARCH", distribution.model = "norm"){
+garch11.spec = ugarchspec(mean.model = list(armaOrder = c(0,0)),
+ variance.model = list(garchOrder = c(1,1), model = model),
+ distribution.model)
-#' Get GARCH Model Specification
-#'
-#' Function to extract the GARCH model specification object
-#'
-#' @param garch a GARCH model specification and fit created with \code{uvGARCH}
-#' @return an object of class uGARCHspec
-#' @export
-getSpec <- function(garch){
- UseMethod("getSpec")
-}
+# DCC specification: GARCH(1,1) for conditional cor
+nbColumns = ncol(R)
+dcc.garch11.spec = dccspec(uspec = multispec( replicate(nbColumns, garch11.spec) ),
+ dccOrder = c(1,1), distribution = "mvnorm")
+dcc.garch11.spec
-#' @method getSpec uvGARCH
-#' @S3method getSpec uvGARCH
-getSpec.uvGARCH <- function(garch){
- garch$spec
+dcc.fit = dccfit(dcc.garch11.spec, data = R)
+class(dcc.fit)
+slotNames(dcc.fit)
+names(dcc.fit at mfit)
+names(dcc.fit at model)
+return(dcc.fit)
}
-#' Get Fitted GARCH Model
+#' Forecast GARCH(1,1)
#'
-#' Function to extract the fitted GARCH model object
+#' Description of forecast GARCH(1,1)
#'
-#' @param garch a GARCH model specification and fit created with \code{uvGARCH}
-#' @return an object of class uGARCHfit
+#' @param garch11 object created by \code{\link{GARCH(1,1)}}
+#' @param window is the forecast window (default is set to window = 100)
#' @export
-getFit <- function(garch){
- UseMethod("getFit")
+fcstGarch11 <- function(object, window){
+ UseMethod("fcstGarch11")
}
-#' @method getFit uvGARCH
-#' @S3method getFit uvGARCH
-getFit.uvGARCH <- function(garch){
- garch$fit
-}
-
-#' Plot GARCH Model
-#'
-#' Plots for fitted GARCH Models
-#'
-#' @param x uvGARCH object create via \code{uvGARCH}
-#' @param y not used
-#' @param \dots additional parameters passed to plot method for uGARCHfit objects
-#' @param which plot selection
-plot.uvGARCH <- function(x, y, ..., which){
- plot(getFit(x), which=which, ...=...)
-}
+#' @method fcstGarch11 Dccfit
+#' @S3method fcstGarch11 DCCfit
+fcstGarch11.DCCfit <- function(object, window = 100){
+ #if ((window > nrow(object))) {stop("Window is too large to forecast")}
+ result = dccforecast(object, n.ahead=window)
+ class(result)
+ slotNames(result)
+ class(result at mforecast)
+ names(result at mforecast)
+ return(result)
+}
\ No newline at end of file
Modified: pkg/GARPFRM/man/CAPM.Rd
===================================================================
--- pkg/GARPFRM/man/CAPM.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/CAPM.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -1,8 +1,8 @@
\name{CAPM}
\alias{CAPM}
-\title{Capital Asset Pricing Model}
+\title{Capital Asset PrisignificanceLevelng Model}
\usage{
- CAPM(R, Rmkt)
+CAPM(R, Rmkt)
}
\arguments{
\item{R}{asset returns}
@@ -10,12 +10,12 @@
\item{Rmkt}{market returns}
}
\description{
- TODO: Need a better description of the CAPM
+TODO: Need a better description of the CAPM
}
\details{
- Retrieves alphas, betas, as well as pvalue and tstats.
- The CAPM is used to determine a theoretically appropriate
- rate of return of the non-diversifiable risk of an asset.
+Retrieves alphas, betas, as well as pvalue and tstats. The
+CAPM is used to determine a theoretically appropriate rate
+of return of the non-diversifiable risk of an asset.
}
\examples{
data(crsp.short)
@@ -30,6 +30,6 @@
tmp <- CAPM(R=R, Rmkt=MKT)
}
\author{
- Thomas Fillebeen
+Thomas Fillebeen
}
Modified: pkg/GARPFRM/man/EWMA.Rd
===================================================================
--- pkg/GARPFRM/man/EWMA.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/EWMA.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,8 +2,8 @@
\alias{EWMA}
\title{EWMA Model}
\usage{
- EWMA(R, lambda = 0.94, initialWindow = 10, n = 10,
- type = c("volatility", "covariance", "correlation"))
+EWMA(R, lambda = 0.94, initialWindow = 10, n = 10,
+ type = c("volatility", "covariance", "correlation"))
}
\arguments{
\item{R}{xts object of asset returns}
@@ -23,21 +23,21 @@
correlation.}
}
\value{
- an EWMA object with the following elements \itemize{
- \item \code{estimate} EWMA model estimated statistic
- \item \code{model} list with model parameters \item
- \code{data} list with original returns data and realized
- statistic if applicable }
+an EWMA object with the following elements \itemize{ \item
+\code{estimate} EWMA model estimated statistic \item
+\code{model} list with model parameters \item \code{data}
+list with original returns data and realized statistic if
+applicable }
}
\description{
- EWMA model to estimate volatility, covariance, and
- correlation
+EWMA model to estimate volatility, covariance, and
+correlation
}
\details{
- If lambda=NULL, the lambda value can be estimated for
- univariate estimates of volatility, covariance, and
- correlation by minimizing the mean squared error between
- the estimated value and realized value.
+If lambda=NULL, the lambda value can be estimated for
+univariate estimates of volatility, covariance, and
+correlation by minimizing the mean squared error between
+the estimated value and realized value.
}
\examples{
# data and parameters for EWMA estimate
@@ -78,6 +78,6 @@
cor_mv
}
\author{
- Ross Bennett and Thomas Fillebeen
+Ross Bennett and Thomas Fillebeen
}
Modified: pkg/GARPFRM/man/backTestVaR.Rd
===================================================================
--- pkg/GARPFRM/man/backTestVaR.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/backTestVaR.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,9 +2,8 @@
\alias{backtestVaR}
\title{Backtest Value-at-Risk (VaR)}
\usage{
- backtestVaR(R, window = 100, p = 0.95,
- method = "historical", bootstrap = FALSE,
- replications = 1000, bootParallel = FALSE)
+backtestVaR(R, window = 100, p = 0.95, method = "historical",
+ bootstrap = FALSE, replications = 1000, bootParallel = FALSE)
}
\arguments{
\item{R}{xts or zoo object of asset returns}
@@ -27,13 +26,12 @@
parallel, (default FALSE).}
}
\description{
- Backtesting Value-at-Risk estimate over a moving window.
+Backtesting Value-at-Risk estimate over a moving window.
}
\details{
- The size of the moving window is set with the
- \code{window} argument. For example, if the window size
- is 100, periods 1:100 are used to estimate the VaR level
- for period 101.
+The size of the moving window is set with the \code{window}
+argument. For example, if the window size is 100, periods
+1:100 are used to estimate the VaR level for period 101.
}
\examples{
data(crsp_weekly)
@@ -45,10 +43,10 @@
head(getVaRViolations(backtest))
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[PerformanceAnalytics]{VaR}},
- \code{\link{bootVaR}}
+\code{\link[PerformanceAnalytics]{VaR}},
+\code{\link{bootVaR}}
}
Modified: pkg/GARPFRM/man/backtestVaR.GARCH.Rd
===================================================================
--- pkg/GARPFRM/man/backtestVaR.GARCH.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/backtestVaR.GARCH.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,8 +2,8 @@
\alias{backtestVaR.GARCH}
\title{GARCH Model VaR Backtest}
\usage{
- backtestVaR.GARCH(garch, p = c(0.95, 0.99), nAhead = 1,
- refitEvery = 25, window = 100)
+backtestVaR.GARCH(garch, p = c(0.95, 0.99), nAhead = 1, refitEvery = 25,
+ window = 100)
}
\arguments{
\item{garch}{uvGARCH object create via
@@ -20,13 +20,13 @@
VaR estimate.}
}
\description{
- Function for rolling estimate of GARCH model and VaR
- backtest
+Function for rolling estimate of GARCH model and VaR
+backtest
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[rugarch]{ugarchroll}}
+\code{\link[rugarch]{ugarchroll}}
}
Modified: pkg/GARPFRM/man/bootCor.Rd
===================================================================
--- pkg/GARPFRM/man/bootCor.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootCor.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootCor}
\title{Bootstrap Correlation}
\usage{
- bootCor(R, ..., replications = 1000, parallel = FALSE)
+bootCor(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the correlation of an xts object or matrix of
- asset returns
+Bootstrap the correlation of an xts object or matrix of
+asset returns
}
\examples{
data(crsp_weekly)
@@ -27,9 +27,9 @@
bootCor(R)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[stats]{cor}}
+\code{\link[stats]{cor}}
}
Modified: pkg/GARPFRM/man/bootCov.Rd
===================================================================
--- pkg/GARPFRM/man/bootCov.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootCov.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootCov}
\title{Bootstrap Covariance}
\usage{
- bootCov(R, ..., replications = 1000, parallel = FALSE)
+bootCov(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the covariance of an xts object or matrix of
- asset returns
+Bootstrap the covariance of an xts object or matrix of
+asset returns
}
\examples{
data(crsp_weekly)
@@ -26,9 +26,9 @@
bootCov(R)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[stats]{cov}}
+\code{\link[stats]{cov}}
}
Modified: pkg/GARPFRM/man/bootES.Rd
===================================================================
--- pkg/GARPFRM/man/bootES.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootES.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootES}
\title{Bootstrap Expected Shortfall}
\usage{
- bootES(R, ..., replications = 1000, parallel = FALSE)
+bootES(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the Expected Shortfall (ES) of an xts object or
- matrix of asset returns
+Bootstrap the Expected Shortfall (ES) of an xts object or
+matrix of asset returns
}
\examples{
data(crsp_weekly)
@@ -27,9 +27,9 @@
bootVaR(R, p=0.9, method="historical", invert=FALSE)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[PerformanceAnalytics]{ES}}
+\code{\link[PerformanceAnalytics]{ES}}
}
Modified: pkg/GARPFRM/man/bootFUN.Rd
===================================================================
--- pkg/GARPFRM/man/bootFUN.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootFUN.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,8 +2,7 @@
\alias{bootFUN}
\title{Bootstrap}
\usage{
- bootFUN(R, FUN = "mean", ..., replications = 1000,
- parallel = FALSE)
+bootFUN(R, FUN = "mean", ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of data passed to
@@ -19,41 +18,39 @@
in parallel.}
}
\description{
- Bootstrap a function
+Bootstrap a function
}
\details{
- \code{R} is the data passed to \code{FUN}. \code{FUN}
- must have \code{x} or \code{R} as arguments for the data.
- For example, see the functions linked to in the 'See
- Also' section. Care must be taken when using
- \code{bootFUN} on multivariate data. This function is
- designed to only accept univariate (i.e. ncol(R) = 1)
- data, however is made to work with bivariate data for
- \code{bootCor} and \code{bootCov}. For multivariate data,
- a wrapper function should be written to apply the
- bootstrap function to each column of data.
+\code{R} is the data passed to \code{FUN}. \code{FUN} must
+have \code{x} or \code{R} as arguments for the data. For
+example, see the functions linked to in the 'See Also'
+section. Care must be taken when using \code{bootFUN} on
+multivariate data. This function is designed to only accept
+univariate (i.e. ncol(R) = 1) data, however is made to work
+with bivariate data for \code{bootCor} and \code{bootCov}.
+For multivariate data, a wrapper function should be written
+to apply the bootstrap function to each column of data.
- To run the bootstrap in parallael, this function uses the
- \code{foreach} pacakge. According to the
- \code{\link[foreach]{foreach}} documentation, the
- parallel computation depends upon a parallel backend that
- must be registered before performing the computation. The
- parallel backends available will be system-specific, but
- include \code{doParallel}, which uses R's built-in
- parallel package, \code{doMC}, which uses the multicore
- package, and \code{doSNOW}. Each parallel backend has a
- specific registration function, such as
- \code{registerDoParallel} or \code{registerDoSNOW}.
+To run the bootstrap in parallael, this function uses the
+\code{foreach} pacakge. According to the
+\code{\link[foreach]{foreach}} documentation, the parallel
+computation depends upon a parallel backend that must be
+registered before performing the computation. The parallel
+backends available will be system-specific, but include
+\code{doParallel}, which uses R's built-in parallel
+package, \code{doMC}, which uses the multicore package, and
+\code{doSNOW}. Each parallel backend has a specific
+registration function, such as \code{registerDoParallel} or
+\code{registerDoSNOW}.
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link{bootMean}}, \code{\link{bootSD}},
- \code{\link{bootStdDev}},
- \code{\link{bootSimpleVolatility}},
- \code{\link{bootCor}}, \code{\link{bootCov}},
- \code{\link{bootVaR}}, \code{\link{bootES}},
- \code{\link[foreach]{foreach}}
+\code{\link{bootMean}}, \code{\link{bootSD}},
+\code{\link{bootStdDev}},
+\code{\link{bootSimpleVolatility}}, \code{\link{bootCor}},
+\code{\link{bootCov}}, \code{\link{bootVaR}},
+\code{\link{bootES}}, \code{\link[foreach]{foreach}}
}
Modified: pkg/GARPFRM/man/bootMean.Rd
===================================================================
--- pkg/GARPFRM/man/bootMean.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootMean.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootMean}
\title{Bootstrap Mean}
\usage{
- bootMean(R, ..., replications = 1000, parallel = FALSE)
+bootMean(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the mean of an xts object or matrix of asset
- returns
+Bootstrap the mean of an xts object or matrix of asset
+returns
}
\examples{
data(crsp_weekly)
@@ -26,9 +26,9 @@
bootMean(R)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[base]{mean}}
+\code{\link[base]{mean}}
}
Modified: pkg/GARPFRM/man/bootSD.Rd
===================================================================
--- pkg/GARPFRM/man/bootSD.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootSD.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootSD}
\title{Bootstrap Standard Deviation}
\usage{
- bootSD(R, ..., replications = 1000, parallel = FALSE)
+bootSD(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the standard deviation of an xts object or
- matrix of asset returns
+Bootstrap the standard deviation of an xts object or matrix
+of asset returns
}
\examples{
data(crsp_weekly)
@@ -26,9 +26,9 @@
bootSD(R)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link[stats]{sd}}
+\code{\link[stats]{sd}}
}
Modified: pkg/GARPFRM/man/bootSimpleVolatility.Rd
===================================================================
--- pkg/GARPFRM/man/bootSimpleVolatility.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootSimpleVolatility.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,8 +2,7 @@
\alias{bootSimpleVolatility}
\title{Bootstrap Simple Volatility}
\usage{
- bootSimpleVolatility(R, ..., replications = 1000,
- parallel = FALSE)
+bootSimpleVolatility(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -17,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the simple volatility of an xts object or
- matrix of asset returns
+Bootstrap the simple volatility of an xts object or matrix
+of asset returns
}
\examples{
data(crsp_weekly)
@@ -27,9 +26,9 @@
bootSimpleVolatility(R)
}
\author{
- Ross Bennett
+Ross Bennett
}
\seealso{
- \code{\link{simpleVolatility}}
+\code{\link{simpleVolatility}}
}
Modified: pkg/GARPFRM/man/bootStdDev.Rd
===================================================================
--- pkg/GARPFRM/man/bootStdDev.Rd 2014-03-30 17:51:50 UTC (rev 152)
+++ pkg/GARPFRM/man/bootStdDev.Rd 2014-03-30 21:40:51 UTC (rev 153)
@@ -2,7 +2,7 @@
\alias{bootStdDev}
\title{Bootstrap StdDev}
\usage{
- bootStdDev(R, ..., replications = 1000, parallel = FALSE)
+bootStdDev(R, ..., replications = 1000, parallel = FALSE)
}
\arguments{
\item{R}{xts object or matrix of asset returns}
@@ -16,8 +16,8 @@
bootstrap in parallel.}
}
\description{
- Bootstrap the StdDev of an xts object or matrix of asset
- returns
+Bootstrap the StdDev of an xts object or matrix of asset
+returns
}
\examples{
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/uwgarp -r 153
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