sandbox/R/nonparametricVaRReport.R

In R-Finance/FactorAnalytics: factor analysis

#' compute VaR report via sample quantitle for collection of assets in a
#' portfolio given simulated (bootstrapped) return data.
#' 
#' compute VaR report via sample quantile for collection of assets in a
#' portfolio given simulated (bootstrapped) return data. Report format follows
#' that of Excel VaR report.
#' 
#' 
#' @param bootData B x n matrix of B bootstrap returns on assets in portfolio.
#' @param w n x 1 vector of portfolio weights.
#' @param delta.w scalar, change in portfolio weight for computing numerical
#' derivative. Default value is 0.010.
#' @param tail.prob scalar tail probability.
#' @param method character, method for computing marginal VaR Valid choices are
#' "derivative" for numerical computation of the derivative of portfolio VaR
#' wrt fund portfolio weight; "average" for approximating E[Ri | Rp =VaR]
#' @param nav n x 1 vector of net asset values in each fund.
#' @param nav.p scalar, net asset value of portfolio percentage.
#' @param fundStrategy n x 1 vector of fund strategies.
#' @param i1,i2 if ff object is used, the ffapply functions do apply an
#' EXPRession and provide two indices FROM="i1" and TO="i2", which mark
#' beginning and end of the batch and can be used in the applied expression.
#' @return dataframe with the following columns: Strategy n x 1 strategy.
#' Net.Asset.value n x 1 net asset values.  Allocation n x 1 vector of asset
#' weights. Mean n x 1 mean of each funds. Std.Dev n x 1 standard deviation of
#' each funds.  Assets.VaR n x 1 vector of asset specific VaR values. cVaR n x
#' 1 vector of asset specific component VaR values. cVaR.dollar n x 1 vector of
#' asset specific component VaR values in dollar terms. pcVaR n x 1 vector of
#' asset specific percent contribution to VaR values. iVaR n x 1 vector of
#' asset specific incremental VaR values. iVaR.dollar n x 1 vector of asset
#' specific component VaR values in dollar terms. mVaR n x 1 vector of asset
#' specific marginal VaR values. mVaR.dollar n x 1 vector of asset specific
#' marginal VaR values in dollar terms.
#' @author Eric Zivot and Yi-An Chen.
#' @examples
#' 
#' data(managers.df)
#' ret.assets = managers.df[,(1:6)]
#' nonparametricVaRReport (bootData= ret.assets[,1:3], w=c(1/3,1/3,1/3), delta.w = 0.001, 
#'                         tail.prob = 0.01, 
#'                         method="derivative",nav=c(100,200,100), 
#'                         nav.p=500, fundStrategy=c("S1","S2","S3"))
#' 
nonparametricVaRReport <-
function(bootData, w, delta.w = 0.001, tail.prob = 0.01, 
                              method=c("derivative", "average"),
                              nav, nav.p, fundStrategy, i1,i2) {
## compute VaR report for collection of assets in a portfolio given
## simulated (bootstrapped) return data 
## Report format follows that of Excel VaR report
## inputs:
## bootData       B x n matrix of B bootstrap returns on assets in portfolio. 
## w              n x 1 vector of portfolio weights
## delta.w        scalar, change in portfolio weight for computing numerical derivative.
##                Default value is 0.01.
## tail.prob      scalar tail probability
## method         character, method for computing marginal ES. Valid choices are 
##                "derivative" for numerical computation of the derivative of portfolio
##                ES wrt fund portfolio weight; "average" for approximating E[Ri | Rp =VaR]
## nav            n x 1 vector of net asset values in each fund
## nav.p          scalar, net asset value of portfolio percentage. 
## i1,i2       if ff object is used,  the ffapply functions do apply an EXPRession and 
##                provide two indices FROM="i1" and TO="i2", which mark beginning and end 
##                of the batch and can be used in the applied expression.  
## fundStrategy   n x 1 vector of fund strategies
## output:
## VaRreport.df   dataframe with the following columns
  
##               dollar allocation
##               percent allocation   
## w             n x 1 vector of asset weights
## aES           n x 1 vector of asset specific ES values
## mES           n x 1 vector of asset specific marginal ES values
## iES           n x 1 vector of asset specific incremental ES values
## cES           n x 1 vector of asset specific component ES values
## pcES          n x 1 vector of asset specific percent contribution to ES values
##
 
  require(PerformanceAnalytics)
  require(ff)
  method = method[1]
  if (!is.ff(bootData))
    bootData = as.matrix(bootData)
  w = as.matrix(w)
  nav = as.matrix(nav)
  if ( ncol(bootData) != nrow(w) )
    stop("Columns of bootData and rows of w do not match")
  if ( tail.prob < 0 || tail.prob > 1)
    stop("tail.prob must be between 0 and 1")
  nVaR <- nonparametricPortfolioVaRDecomposition(bootData, w, delta.w, tail.prob,method)
  asset.VaR = as.numeric(-VaR(R=bootData,p=1-tail.prob,method="historical"))
  portfolio.VaR = as.numeric(nVaR$VaR.p)
  marginal.VaR = as.numeric(nVaR$mVaR)
  component.VaR = as.numeric(nVaR$cVaR)
  incremental.VaR = as.numeric(nonparametricIncrementalVaR(bootData, w, tail.prob))
  
  if (is.ff(bootData)) {
    mean.vals = ffrowapply(colMeans(bootData[i1:i2,,drop=FALSE]), 
                           X=bootData, RETURN=TRUE, CFUN="cmean")
    sd.vals = ffrowapply(colMeans(bootData[i1:i2,,drop=FALSE]^2) - colMeans(bootData[i1:i2,,drop=FALSE])^2,
                         X=bootData, RETURN=TRUE, CFUN="cmean")
    sd.vals = sqrt(sd.vals)                                            
  } else {
    mean.vals = colMeans(bootData)
    sd.vals = apply(bootData, 2, sd)
  }
  
  VaRreport.df = data.frame(Strategy = fundStrategy,
                            Net.Asset.Value = nav,
                            Allocation = w,
                            Mean = mean.vals,
                            Std.Dev = sd.vals,
                            Asset.VaR = asset.VaR,
                            cVaR = component.VaR,
                            cVaR.dollar = component.VaR*nav.p, 
                            pcVaR = component.VaR/portfolio.VaR,                                                 
                            iVaR = incremental.VaR,
                            iVaR.dollar = incremental.VaR*nav.p,
                            mVaR = marginal.VaR, 
                            mVaR.dollar = marginal.VaR*nav.p)
  rownames(VaRreport.df) = colnames(bootData)                           
  return(VaRreport.df)
}