[Returnanalytics-commits] r3897 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-03 22:11:19 +0200 (Mon, 03 Aug 2015)
New Revision: 3897

Added:
   pkg/Dowd/R/InsuranceVaRES.R
Log:
Function InsuranceVaR added.

Added: pkg/Dowd/R/InsuranceVaRES.R
===================================================================
--- pkg/Dowd/R/InsuranceVaRES.R	                        (rev 0)
+++ pkg/Dowd/R/InsuranceVaRES.R	2015-08-03 20:11:19 UTC (rev 3897)
@@ -0,0 +1,53 @@
+#' VaR and ES of Insurance Portfolio
+#'
+#' Generates Monte Carlo VaR and ES for insurance portfolio.
+#'
+#' @param mu Mean of returns
+#' @param sigma Volatility of returns
+#' @param n Number of contracts
+#' @param p Probability of any loss event
+#' @param theta Expected profit per contract
+#' @param deductible Deductible
+#' @param number.trials Number of simulation trials
+#' @param cl VaR confidence level
+#' @return A list with "VaR" and "ES" of the specified portfolio
+#' 
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#' 
+#' 
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Estimates VaR and ES of Insurance portfolio with given parameters
+#'    y<-InsuranceVaRES(.8, 1.3, 100, .6, 21,  12, 50, .95)
+#'
+#' @export
+InsuranceVaRES<- function(mu, sigma, n, p, theta, deductible, number.trials, cl){
+  M <- number.trials
+  D <- deductible
+  L <- matrix(0, n, M)
+  company.loss <- matrix(0, n, M)
+  total.company.loss <- double(M)
+  for (j in 1:M) {
+    L[1, j] <- rbinom( 1, 1, p) * rlnorm(1, mu, sigma) # Realisation of L
+    company.loss[1, j] <- max(L[1,j] - D, 0) # Adjust for deductible
+    
+    for (i in 2:n) {
+      L[i, j] <- rbinom(1, 1, p) * rlnorm(1, mu, sigma) # Realisation of L
+      company.loss[i, j] <- max(L[i,j] - D, 0) + company.loss[i - 1, j] # Adjust
+      # for deductible
+    }
+    total.company.loss[j] <- company.loss[n,j] # Total company loss for 
+    # given j trial
+  }
+  # Sample of total company losses
+  adjusted.total.company.loss <- total.company.loss - mean(total.company.loss) -
+                    theta * mean(total.company.loss) / n # Adjusts for premium
+  profit.or.loss <- - adjusted.total.company.loss # Convert to P/L
+  hist(adjusted.total.company.loss, col = "blue", 
+       xlab = "Total Company Loss", ylab = "Frequency",
+       main = "Adjusted Total Company Loss")
+  VaR <- HSVaR(profit.or.loss, cl)
+  ES <- HSES(profit.or.loss, cl)
+  return(list("VaR" = VaR, "ES" = ES))
+}
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