[Returnanalytics-commits] r2113 - pkg/PerformanceAnalytics/sandbox/Meucci/demo

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

Author: mkshah
Date: 2012-07-05 06:21:38 +0200 (Thu, 05 Jul 2012)
New Revision: 2113

Added:
   pkg/PerformanceAnalytics/sandbox/Meucci/demo/HermiteGrid_CaseStudy.R
Log:
Duplicating the S_CaseStudy.m Demo as provided by Meucci for Fully Flexible Extreme Views

Added: pkg/PerformanceAnalytics/sandbox/Meucci/demo/HermiteGrid_CaseStudy.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/demo/HermiteGrid_CaseStudy.R	                        (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/demo/HermiteGrid_CaseStudy.R	2012-07-05 04:21:38 UTC (rev 2113)
@@ -0,0 +1,47 @@
+# This script estimates the prior of a hedge fund return and processes extreme views on CVaR 
+# according to Entropy Pooling
+# This script complements the article
+#  "Fully Flexible Extreme Views"
+#	by A. Meucci, D. Ardia, S. Keel
+#	available at www.ssrn.com
+# The most recent version of this code is available at
+# MATLAB Central - File Exchange
+
+# IMPORTANT - This script is about the methodology, not the input data, which has been modified
+
+xi = as.matrix( 100 * data[, 2] )
+n = nrow(xi)
+
+# bandwidth
+bw = kernelbw(xi)
+
+% weights
+lambda = log(2) / (n / 2)
+wi = as.matrix( exp( -lambda * ( n - seq( from=n, to=1 ) ) )
+wi = as.matrix( apply( wi, 2, rev ) / sum( wi ) )
+                
+# Prior market model
+# kernel density
+
+market.mu  = mean(xi)
+market.pdf = function ( x ) kernelpdf( x, xi, bw, wi )
+market.cdf = function ( x ) kernelcdf( x, xi, bw, wi )
+market.inv = function ( x ) kernelinv( x, xi, bw, wi )
+market.VaR95 = market.inv(0.05)
+market.CVaR95 = integrate( function( x ) (x %*% market.pdf( x ) ), -100, market.VaR95 ) / 0.05;
+
+# numerical (Gauss-Hermite grid) prior 
+tmp = ( ghqx - min( ghqx ) )/( max( ghqx ) - min( ghqx ) ) # rescale GH zeros so they belong to [0,1]
+epsilon = 1e-10
+Lower = market.inv( epsilon )
+Upper = market.inv( 1 - epsilon )
+X  = Lower + tmp * ( Upper - Lower ) # rescale mesh
+                
+p = integrateSubIntervals( X , market.cdf )
+p = normalizeProb( p )
+J = length( X )
+                
+# Entropy posterior from extreme view on mean and CVaR
+                
+                
+                
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