[Returnanalytics-commits] r3940 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-12 09:01:09 +0200 (Wed, 12 Aug 2015)
New Revision: 3940

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

Added: pkg/Dowd/R/FilterStrategyLogNormalVaR.R
===================================================================
--- pkg/Dowd/R/FilterStrategyLogNormalVaR.R	                        (rev 0)
+++ pkg/Dowd/R/FilterStrategyLogNormalVaR.R	2015-08-12 07:01:09 UTC (rev 3940)
@@ -0,0 +1,53 @@
+#' Log Normal VaR with filter strategy
+#'
+#' Generates Monte Carlo lognormal VaR with filter portfolio strategy
+#'  
+#' @param mu Mean arithmetic return
+#' @param sigma Standard deviation of arithmetic return
+#' @param number.trials Number of trials used in the simulations
+#' @param alpha Participation parameter
+#' @param cl Confidence Level
+#' @param hp Holding Period
+#' @return Lognormal VaR
+#' 
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Estimates standard error of normal quantile estimate
+#'    FilterStrategyLogNormalVaR(0, .2, 100, 1.2, .95, 10)
+#'
+#' @export
+FilterStrategyLogNormalVaR <- function(mu, sigma, number.trials, alpha, cl, hp){
+  N <- 100 # Number of increments, taken as 100
+  dt <- hp/N # Size of time-increment
+  nudt <- (mu - 0.5 * sigma ^ 2) * dt
+  sigmadt <- sigma * sqrt(dt)
+  stock.price <- 1 # Stock price assumed to be investment assumed to be 1
+  lnS <- log(stock.price)
+  M <- number.trials
+  # Stock price simulation process
+  lnSt <- matrix(0,M,N)
+  new.stock.price <- matrix(0,M,N)
+  equity.proportion <- matrix(0,M,N)
+  investment <- matrix(0,M,N)
+  for (i in 1:M) {
+    lnSt[i, 1] <- rnorm(1, lnS + nudt, sigmadt)
+    new.stock.price[i,1] <- exp(lnSt[i,1])
+    for (j in 2:N) {
+      lnSt[i, j] <- rnorm(1, lnSt[i, j-1] + nudt, sigmadt)
+      new.stock.price[i, j] <- exp(lnSt[i, j]) # New stock price
+      equity.proportion[i, j] <- .5 + alpha * (new.stock.price[i, j] - 
+                                                 stock.price)/stock.price
+      investment[i, j] <- equity.proportion[i, j] * new.stock.price[i, j] + (1 - equity.proportion[i, j])
+    }
+  }
+  # Profit/Loss calculation
+  profit.or.loss <- double(M)
+  for (i in 1:M) {
+    profit.or.loss[i] <- investment[i,j] - stock.price
+  }
+  y <- HSVaR(profit.or.loss, cl)
+  return(y)
+}
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