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braverock
GitHub Repository: braverock/portfolioanalytics
 Path: blob/master/sandbox/symposium2013/R/chart.VaRSensitivity.R
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#' show the sensitivity of Value-at-Risk or Expected Shortfall estimates

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#' 

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#' Creates a chart of Value-at-Risk and/or Expected Shortfall estimates by

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#' confidence interval for multiple methods.

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#' 

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#' This chart shows estimated VaR along a series of confidence intervals for

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#' selected calculation methods.  Useful for comparing a method to the

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#' historical VaR calculation.

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#' 

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#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of

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#' asset returns

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#' @param methods one or more calculation methods indicated "GaussianVaR",

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#' "ModifiedVaR", "HistoricalVaR", "GaussianES", "ModifiedES", "HistoricalES".

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#' See \code{\link{VaR}} or \code{\link{ES}} for more detail.

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#' @param clean method for data cleaning through \code{\link{Return.clean}}.

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#' Current options are "none" or "boudt" or "geltner".

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#' @param elementcolor the color used to draw chart elements. The default is

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#' "darkgray"

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#' @param reference.grid if true, draws a grid aligned with the points on the x

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#' and y axes

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#' @param ylab set the y-axis label, same as in \code{\link{plot}}

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#' @param xlab set the x-axis label, same as in \code{\link{plot}}

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#' @param ylim set the y-axis dimensions, same as in \code{\link{plot}}

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#' @param type set the chart type, same as in \code{\link{plot}}

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#' @param lty set the line type, same as in \code{\link{plot}}

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#' @param lwd set the line width, same as in \code{\link{plot}}

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#' @param colorset color palette to use, set by default to rational choices

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#' @param pch symbols to use, see also \code{\link{plot}}

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#' @param legend.loc places a legend into one of nine locations on the chart:

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#' bottomright, bottom, bottomleft, left, topleft, top, topright, right, or

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#' center.

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#' @param cex.legend The magnification to be used for sizing the legend

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#' relative to the current setting of 'cex'.

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#' @param main set the chart title, same as in \code{\link{plot}}

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#' @param \dots any other passthru parameters

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#' @author Peter Carl

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#' @seealso \code{\link{VaR}} \cr \code{\link{ES}}

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#' @references Boudt, K., Peterson, B. G., Croux, C., 2008. Estimation and

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#' Decomposition of Downside Risk for Portfolios with Non-Normal Returns.

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#' Journal of Risk, forthcoming.

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#' @keywords ts multivariate distribution

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#' @examples

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#' 

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#' data(managers)

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#' chart.VaRSensitivity(managers[,1,drop=FALSE], 

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#' 		methods=c("HistoricalVaR", "ModifiedVaR", "GaussianVaR"), 

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#' 		colorset=bluefocus, lwd=2)

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#' 

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#' @export 

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chart.VaRSensitivity <-

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function (R, methods = c("GaussianVaR", "ModifiedVaR", "HistoricalVaR","GaussianES", "ModifiedES", "HistoricalES"), clean=c("none", "boudt", "geltner"), elementcolor="darkgray", reference.grid=TRUE, xlab = "Confidence Level", ylab="Value at Risk", type = "l", lty = c(1,2,4), lwd = 1, colorset = (1:12), pch = (1:12), legend.loc = "bottomleft", cex.legend = 0.8, main=NULL, ylim=NULL, ...)

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{ # @author Peter Carl

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    R = checkData(R)

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    columnnames = colnames(R)

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    clean = clean[1]

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    legend.txt = NULL

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    if(length(methods) > 1){

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        columns=1

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    }

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    p = seq(0.99,0.89,by=-0.005)

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    risk = matrix(nrow=length(p), ncol=length(methods),dimnames=list(p,methods))

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    for(column in 1:columns) {

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         for(j in 1:length(methods)) {

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            for(i in 1:length(p)){

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                switch(methods[j],

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                    GaussianVaR = {

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                        risk[i, j] =  as.numeric(VaR(na.omit(R[,column,drop=FALSE]), p = p[i], method="gaussian", clean=clean))

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Gaussian VaR")

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                    },

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                    ModifiedVaR = {

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                        risk[i, j] =  as.numeric(VaR(na.omit(R[,column,drop=FALSE]), p = p[i], method="modified", clean=clean))

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Modified VaR")

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                    },

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                    HistoricalVaR = {

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                        risk[i,j] =   as.numeric(VaR(na.omit(R[,column,drop=FALSE]), p = p[i], method="historical", clean=clean)) #hVaR = quantile(x,probs=.01)

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Historical VaR")

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                    },

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                    GaussianES = {

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                        risk[i, j] =  as.numeric(ES(na.omit(R[,column,drop=FALSE]), p = p[i], method="gaussian", clean=clean))

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Gaussian ES")

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                    },

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                    ModifiedES = {

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                        risk[i, j] =  as.numeric(ES(na.omit(R[,column,drop=FALSE]), p = p[i], method="modified", clean=clean))

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Modified ES")

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                    },

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                    HistoricalES = {

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                        risk[i,j] =   as.numeric(ES(na.omit(R[,column,drop=FALSE]), p = p[i], method="historical", clean=clean)) 

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                        if(i==1)

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                            legend.txt = c(legend.txt, "Historical ES")

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                    }

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                ) # end switch

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            }

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         } # end method loop

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    } # end column loop

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# print(risk)

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    risk.columns = ncol(risk)

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    if(is.null(ylim))

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        ylim=c(min(risk),max(risk))

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    xlim=c(min(p), max(p))

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    if(is.null(main))

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        main=paste("Risk Confidence Sensitivity of ", columnnames[1], sep="")

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    if(length(lwd) < risk.columns)

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        lwd = rep(lwd,risk.columns)

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    if(length(lty) < risk.columns)

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        lty = rep(lty,risk.columns)

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    if(length(pch) < risk.columns)

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        pch = rep(pch,risk.columns)

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    plot.new()

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    plot.window(xlim, ylim, xaxs = "r")

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    if (reference.grid) {

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        grid(col = elementcolor)

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    }

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    for(risk.column in risk.columns:1) {

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        lines(p,risk[,risk.column], col = colorset[risk.column], lwd = lwd[risk.column], pch = pch[risk.column], lty = lty[risk.column], type = type, ...)

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    }

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    # draw x-axis

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    axis(1, labels=p, at=p, col = elementcolor) # at= 1/(1:length(p))

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    title(xlab = xlab)

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    # set up y-axis

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    axis(2, col=elementcolor)

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    box(col = elementcolor)

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    if(!is.null(legend.loc)){

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        # There's no good place to put this automatically, except under the graph.

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        # That requires a different solution, but here's the quick fix

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        legend(legend.loc, inset = 0.02, text.col = colorset, col = colorset, cex = cex.legend, border.col = elementcolor, lty = lty, lwd = 2, bg = "white", legend = legend.txt)

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    }

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    # Add the other titles

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    if(is.null(main))

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        main=columnnames[1]

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    title(ylab = ylab)

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    title(main = main)

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}

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###############################################################################

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# R (http://r-project.org/) Econometrics for Performance and Risk Analysis

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#

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# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson

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#

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# This R package is distributed under the terms of the GNU Public License (GPL)

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# for full details see the file COPYING

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#

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# $Id: chart.VaRSensitivity.R 3191 2013-09-26 15:42:06Z peter_carl $

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#

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###############################################################################

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