[Returnanalytics-commits] r3922 - pkg/Dowd/R

[noreply at r-forge.r-project.org]

Author: dacharya
Date: 2015-08-07 10:04:59 +0200 (Fri, 07 Aug 2015)
New Revision: 3922

Added:
   pkg/Dowd/R/tVaRFigure.R
Log:
Function tVaRFigure added

Added: pkg/Dowd/R/tVaRFigure.R
===================================================================
--- pkg/Dowd/R/tVaRFigure.R	                        (rev 0)
+++ pkg/Dowd/R/tVaRFigure.R	2015-08-07 08:04:59 UTC (rev 3922)
@@ -0,0 +1,152 @@
+#' Figure of t- VaR and pdf against L/P
+#'
+#' Gives figure showing the VaR and probability distribution function against L/P
+#'  of a portfolio assuming P/L are normally distributed, for specified 
+#'  confidence level and holding period.
+#' 
+#' @param ... The input arguments contain either return data or else mean and 
+#'  standard deviation data. Accordingly, number of input arguments is either 4 
+#'  or 5. In case there 4 input arguments, the mean and standard deviation of 
+#'  data is computed from return data. See examples for details.
+#' 
+#'  returns Vector of daily geometric return data
+#' 
+#'  mu Mean of daily geometric return data
+#' 
+#'  sigma Standard deviation of daily geometric return data
+#'  
+#'  df Number of degrees of freedom
+#' 
+#'  cl VaR confidence level and should be scalar
+#' 
+#'  hp VaR holding period in days and should be scalar
+#'  
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Plots normal VaR and pdf against L/P data for given returns data
+#'    data <- runif(5, min = 0, max = .2)
+#'    tVaRFigure(returns = data, df = 7, cl = .95, hp = 90)
+#'    
+#'    # Plots normal VaR and pdf against L/P data with given parameters
+#'    tVaRFigure(mu = .012, sigma = .03, df=7, cl = .95, hp = 90)
+#'
+#'
+#' @export
+tVaRFigure <- function(...){
+  # Determine if there are four or five arguments and ensure that arguments are
+  # read as intended
+  if (nargs() < 4) {
+    stop("Too few arguments")
+  }
+  if (nargs() > 5) {
+    stop("Too many arguments")
+  }
+  args <- list(...)
+  if (nargs() == 5) {
+    mu <- args$mu
+    cl <- args$cl
+    sigma <- args$sigma
+    hp <- args$hp
+    df <- args$df
+  }
+  if (nargs() == 4) {
+    mu <- mean(args$returns)
+    cl <- args$cl
+    sigma <- sd(args$returns)
+    hp <- args$hp
+    df <- args$df
+  }
+  
+  # Check that inputs have correct dimensions
+  mu <- as.matrix(mu)
+  mu.row <- dim(mu)[1]
+  mu.col <- dim(mu)[2]
+  if (max(mu.row, mu.col) > 1) {
+    stop("Mean must be a scalar")
+  }
+  sigma <- as.matrix(sigma)
+  sigma.row <- dim(sigma)[1]
+  sigma.col <- dim(sigma)[2]
+  if (max(sigma.row, sigma.col) > 1) {
+    stop("Standard deviation must be a scalar")
+  }
+  cl <- as.matrix(cl)
+  cl.row <- dim(cl)[1]
+  cl.col <- dim(cl)[2]
+  if (min(cl.row, cl.col) > 1) {
+    stop("Confidence level must be a scalar or a vector")
+  }
+  hp <- as.matrix(hp)
+  hp.row <- dim(hp)[1]
+  hp.col <- dim(hp)[2]
+  if (min(hp.row, hp.col) > 1) {
+    stop("Holding period must be a scalar or a vector")
+  }
+  df <- as.matrix(df)
+  df.row <- dim(df)[1]
+  df.col <- dim(df)[2]
+  if (max(df.row, df.col) > 1) {
+    stop("Number of degrees of freedom must be a scalar")
+  }
+  
+  # Check that cl and hp are read as row and column vectors respectively
+  if (cl.row > cl.col) {
+    cl <- t(cl)
+  }
+  if (hp.row > hp.col) {
+    hp <- t(hp)
+  }
+  
+  # Check that inputs obey sign and value restrictions
+  if (sigma < 0) {
+    stop("Standard deviation must be non-negative")
+  }
+  if (df < 3) {
+    stop("Number of degrees of freedom must be at least 3 for first two moments
+         of distribution to be defined")
+  }
+  if (max(cl) >= 1){
+    stop("Confidence level(s) must be less than 1")
+  }
+  if (min(cl) <= 0){
+    stop("Confidence level(s) must be greater than 0")
+  }
+  if (min(hp) <= 0){
+    stop("Holding Period(s) must be greater than 0")
+  }
+  
+  # Message to indicate how matrix of results is to be interpreted, if cl and hp
+  # both vary and results are given in matrix form
+  if (max(cl.row, cl.col) > 1 & max(hp.row, hp.col) > 1) {
+    print('VaR results with confidence level varying across row and holding 
+          period down column')
+  }
+  
+  # VaR estimation
+  cl.row <- dim(cl)[1]
+  cl.col <- dim(cl)[2]
+  VaR <- - sigma[1,1] * sqrt(hp) %*% qnorm(1 - cl, 0, 1)  - mu[1,1] * hp %*% matrix(1,cl.row,cl.col) # VaR
+  
+  # Plotting
+  x.min <- -mu - 5 * sigma
+  x.max <- -mu + 5 * sigma
+  delta <- (x.max-x.min) / 100
+  x <- seq(x.min, x.max, delta)
+  p <- dt((x-mu)/sigma, df)
+  plot(x, p, type = "l", xlim = c(x.min, x.max), ylim = c(0, max(p)*1.1), 
+       xlab = "Loss (+) / Profit (-)", ylab = "Probability", main = "t- VaR")
+  u <- c(VaR, VaR)
+  v <- c(0, .6*max(p))
+  lines(0,0,2,.6,type="l")
+  lines(u, v, type = "l", col = "blue")
+  cl.for.label <- 100*cl
+  text(1,.95*max(p), pos = 1, 'Input parameters', cex=.75, font = 2)
+  text(1, .875*max(p),pos = 1, paste('Daily mean L/P = ', round(mu,2)), cex=.75)
+  text(1, .8*max(p),pos = 1, paste('St. dev. of daily L/P = ',round(sigma,2)), cex=.75)
+  text(1, .725*max(p),pos = 1, paste('Holding period = ', hp,' day(s)'), cex=.75)
+  text(VaR, .7*max(p),pos = 2, paste('VaR at ', cl.for.label,'% CL'), cex=.75)
+  text(VaR, .64 * max(p),pos = 2, paste('= ',VaR), cex=.75)
+}