[Returnanalytics-commits] r2197 - pkg/PerformanceAnalytics/sandbox/Meucci/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Jul 23 04:42:08 CEST 2012
Author: mkshah
Date: 2012-07-23 04:42:08 +0200 (Mon, 23 Jul 2012)
New Revision: 2197
Added:
pkg/PerformanceAnalytics/sandbox/Meucci/man/RIEfficientFrontier.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/gaussHermiteMesh.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/hermitePolynomial.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/integrateSubIntervals.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/kernelbw.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/kernelcdf.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/kernelinv.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/kernelpdf.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/normalizeProb.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/private_fun.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/subIntervals.Rd
Modified:
pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAcombination.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAseparation.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Central2Raw.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMVE.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMoments.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/CondProbViews.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Cumul2Raw.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/DetectOutliersViaMVE.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/EntropyProg.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/GenerateLogNormalDistribution.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/MvnRnd.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/NoisyObservations.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/PanicCopula.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/PartialConfidencePosterior.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/PlotDistributions.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Prior2Posterior.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Raw2Central.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Raw2Cumul.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/RejectOutlier.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/StackedBarChart.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/SummStats.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/Tweak.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/ViewRanking.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/efficientFrontier.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/linreturn.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/pHist.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/robustBayesianPortfolioOptimization.Rd
pkg/PerformanceAnalytics/sandbox/Meucci/man/std.Rd
Log:
Adding documentation files for new functions and updating comments for other functions
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAcombination.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAcombination.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAcombination.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,38 +1,38 @@
-\name{CMAcombination}
-\alias{CMAcombination}
-\title{CMA combination. Glues an arbitrary copula and arbitrary marginal distributions into a new joint distribution}
-\usage{
- CMAcombination(x, u, U)
-}
-\arguments{
- \item{x}{a generic x variable. Note: Linearly spaced 'x'
- help for coverage when performing linear interpolation}
-
- \item{u}{The value of the cumulative density function
- associated with x (parametric or non-parametric)}
-
- \item{U}{an aribtrary copula. Can take any copula
- obtained with the separation step (i.e. a set of
- scenario-probabilities)}
-}
-\value{
- X a J x N matrix containing the new joint distribution
- based on the arbitrary copula 'U'
-}
-\description{
- The combination step starts from arbitrary marginal
- distributions, and grades distributed according to a
- chosen arbitrary copula which can, but does not need to,
- be obtained by seperation. Then this function combines
- the marginals and copula into a new joint distribution.
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-\references{
- Meucci A., "New Breed of Copulas for Risk and Portfolio
- Management", Risk, September 2011 Most recent version of
- article and code available at
- \url{http://www.symmys.com/node/335}
-}
-
+\name{CMAcombination}
+\alias{CMAcombination}
+\title{CMA combination. Glues an arbitrary copula and arbitrary marginal distributions into a new joint distribution}
+\usage{
+ CMAcombination(x, u, U)
+}
+\arguments{
+ \item{x}{a generic x variable. Note: Linearly spaced 'x'
+ help for coverage when performing linear interpolation}
+
+ \item{u}{The value of the cumulative density function
+ associated with x (parametric or non-parametric)}
+
+ \item{U}{an aribtrary copula. Can take any copula
+ obtained with the separation step (i.e. a set of
+ scenario-probabilities)}
+}
+\value{
+ X a J x N matrix containing the new joint distribution
+ based on the arbitrary copula 'U'
+}
+\description{
+ The combination step starts from arbitrary marginal
+ distributions, and grades distributed according to a
+ chosen arbitrary copula which can, but does not need to,
+ be obtained by seperation. Then this function combines
+ the marginals and copula into a new joint distribution.
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+\references{
+ Meucci A., "New Breed of Copulas for Risk and Portfolio
+ Management", Risk, September 2011 Most recent version of
+ article and code available at
+ \url{http://www.symmys.com/node/335}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAseparation.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAseparation.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/CMAseparation.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,50 +1,50 @@
-\name{CMAseparation}
-\alias{CMAseparation}
-\title{CMA separation. Decomposes arbitrary joint distributions (scenario-probabilities) into their copula and marginals}
-\usage{
- CMAseparation(X, p)
-}
-\arguments{
- \item{X}{A matrix where each row corresponds to a
- scenario/sample from a joint distribution. Each column
- represents the value from a marginal distribution}
-
- \item{p}{A 1-column matrix of probabilities of the
- Jth-scenario joint distribution in X}
-}
-\value{
- xdd a JxN matrix where each column consists of each
- marginal's generic x values in ascending order
-
- udd a JxN matrix containing the cumulative probability
- (cdf) for each marginal by column - it is rescaled by 'l'
- to be <1 at the far right of the distribution can
- interpret 'udd' as the probability weighted grade
- scenarios (see formula 11 in Meucci)
-
- U a copula (J x N matrix) - the joint distribution of
- grades defined by feeding the original variables X into
- their respective marginal CDF
-}
-\description{
- The CMA separation step attains from the cdf "F" for the
- marginal "X", the scenario-probabilities representation
- of the copula (cdf of U: "F") and the inter/extrapolation
- representation of the marginal CDF's. It seperates this
- distribution into the pure "individual" information
- contained in the marginals and the pure "joint"
- information contained in the copula.
-}
-\details{
- Separation step of Copula-Marginal Algorithm (CMA)
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-\references{
- Meucci A., "New Breed of Copulas for Risk and Portfolio
- Management", Risk, September 2011 Most recent version of
- article and code available at
- \url{http://www.symmys.com/node/335}
-}
-
+\name{CMAseparation}
+\alias{CMAseparation}
+\title{CMA separation. Decomposes arbitrary joint distributions (scenario-probabilities) into their copula and marginals}
+\usage{
+ CMAseparation(X, p)
+}
+\arguments{
+ \item{X}{A matrix where each row corresponds to a
+ scenario/sample from a joint distribution. Each column
+ represents the value from a marginal distribution}
+
+ \item{p}{A 1-column matrix of probabilities of the
+ Jth-scenario joint distribution in X}
+}
+\value{
+ xdd a JxN matrix where each column consists of each
+ marginal's generic x values in ascending order
+
+ udd a JxN matrix containing the cumulative probability
+ (cdf) for each marginal by column - it is rescaled by 'l'
+ to be <1 at the far right of the distribution can
+ interpret 'udd' as the probability weighted grade
+ scenarios (see formula 11 in Meucci)
+
+ U a copula (J x N matrix) - the joint distribution of
+ grades defined by feeding the original variables X into
+ their respective marginal CDF
+}
+\description{
+ The CMA separation step attains from the cdf "F" for the
+ marginal "X", the scenario-probabilities representation
+ of the copula (cdf of U: "F") and the inter/extrapolation
+ representation of the marginal CDF's. It seperates this
+ distribution into the pure "individual" information
+ contained in the marginals and the pure "joint"
+ information contained in the copula.
+}
+\details{
+ Separation step of Copula-Marginal Algorithm (CMA)
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+\references{
+ Meucci A., "New Breed of Copulas for Risk and Portfolio
+ Management", Risk, September 2011 Most recent version of
+ article and code available at
+ \url{http://www.symmys.com/node/335}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/Central2Raw.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/Central2Raw.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/Central2Raw.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,33 +1,33 @@
-\name{Central2Raw}
-\alias{Central2Raw}
-\title{Transforms first n central moments into first n raw moments (first central moment defined as expectation)}
-\usage{
- Central2Raw(mu)
-}
-\arguments{
- \item{mu}{a vector of central moments}
-}
-\value{
- mu_ a vector of non-central moments
-}
-\description{
- step 2 of projection process: From the central moments of
- step 1, we compute the non-central moments. To do so we
- start with the first non-central moment and apply
- recursively an identity (formula 20)
-}
-\details{
- \deqn{ \tilde{ \mu }^{ \big(1\big) }_{X} \equiv \mu
- ^{\big(1\big)}_{X} \\ \tilde{ \mu }^{ \big(n\big) }_{X}
- \equiv \mu ^{n}_{X} \sum_{k=0}^{n-1} \big(-1\big)^{n-k+1}
- \mu ^{n-k}_{X} \tilde{ \mu }^{\big(k\big)}_{X} }
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-\references{
- A. Meucci - "Exercises in Advanced Risk and Portfolio
- Management". See page 10. Symmys site containing original
- MATLAB source code \url{http://www.symmys.com}
-}
-
+\name{Central2Raw}
+\alias{Central2Raw}
+\title{Transforms first n central moments into first n raw moments (first central moment defined as expectation)}
+\usage{
+ Central2Raw(mu)
+}
+\arguments{
+ \item{mu}{a vector of central moments}
+}
+\value{
+ mu_ a vector of non-central moments
+}
+\description{
+ step 2 of projection process: From the central moments of
+ step 1, we compute the non-central moments. To do so we
+ start with the first non-central moment and apply
+ recursively an identity (formula 20)
+}
+\details{
+ \deqn{ \tilde{ \mu }^{ \big(1\big) }_{X} \equiv \mu
+ ^{\big(1\big)}_{X} \\ \tilde{ \mu }^{ \big(n\big) }_{X}
+ \equiv \mu ^{n}_{X} \sum_{k=0}^{n-1} \big(-1\big)^{n-k+1}
+ \mu ^{n-k}_{X} \tilde{ \mu }^{\big(k\big)}_{X} }
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+\references{
+ A. Meucci - "Exercises in Advanced Risk and Portfolio
+ Management". See page 10. Symmys site containing original
+ MATLAB source code \url{http://www.symmys.com}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMVE.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMVE.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMVE.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,45 +1,45 @@
-\name{ComputeMVE}
-\alias{ComputeMVE}
-\title{Compute the minimum volume ellipsoid for a given (multi-variate) time-series}
-\usage{
- ComputeMVE(data)
-}
-\arguments{
- \item{data}{a matrix time-series of data. Each row is a
- observation (date). Each column is an asset}
-}
-\value{
- list a list with MVE_Location a numeric with the location
- parameter of minimum volume ellipsoid MVE_Dispersion a
- numeric with the covariance matrix of the minimum volume
- ellipsoid
-}
-\description{
- Function computes the minimum volume ellipsoid for a
- given time series
-}
-\details{
- via the expectations-minimization algorithm
-
- \deqn{ w_{t} = \frac{1}{T} , t = 1,...,T \\ m \equiv
- \frac{1}{ \sum_{s=1}^T w_{s} } \sum_{t=1}^T w_{t} x_{t}
- \\ S \equiv \sum_{t=1}^T w_{t} \big(x_{t} - m\big)
- \big(x_{t} - m\big)' \\ Ma_{t}^{2} \equiv \big(x-m\big)'
- S^{-1} \big(x-m\big), t=1,...,T \\ w_{t} \mapsto w_{t}
- Ma_{t}^{2} \\ U = \big(x_{1}' - \hat{E}',...,x_{T}' -
- \hat{E}' \big) \\ \hat{Cov} \equiv \frac{1}{T} U'U }
-
- The location and scatter parameters that define the
- ellipsoid are multivariate high-breakdown estimators of
- location and scatter
-}
-\author{
- Ram Ahluwalia \email{ram at wingedfootcapital.com}
-}
-\references{
- \url{http://www.symmys.com/sites/default/files/Risk\%20and\%20Asset\%20Allocation\%20-\%20Springer\%20Quantitative\%20Finance\%20-\%20Estimation.pdf}
- See Sec. 4.6.1 of "Risk and Asset Allocation" - Springer
- (2005), by A. Meucci for the theory and the routine
- implemented below See Meucci script for "ComputeMVE.m"
-}
-
+\name{ComputeMVE}
+\alias{ComputeMVE}
+\title{Compute the minimum volume ellipsoid for a given (multi-variate) time-series}
+\usage{
+ ComputeMVE(data)
+}
+\arguments{
+ \item{data}{a matrix time-series of data. Each row is a
+ observation (date). Each column is an asset}
+}
+\value{
+ list a list with MVE_Location a numeric with the location
+ parameter of minimum volume ellipsoid MVE_Dispersion a
+ numeric with the covariance matrix of the minimum volume
+ ellipsoid
+}
+\description{
+ Function computes the minimum volume ellipsoid for a
+ given time series
+}
+\details{
+ via the expectations-minimization algorithm
+
+ \deqn{ w_{t} = \frac{1}{T} , t = 1,...,T \\ m \equiv
+ \frac{1}{ \sum_{s=1}^T w_{s} } \sum_{t=1}^T w_{t} x_{t}
+ \\ S \equiv \sum_{t=1}^T w_{t} \big(x_{t} - m\big)
+ \big(x_{t} - m\big)' \\ Ma_{t}^{2} \equiv \big(x-m\big)'
+ S^{-1} \big(x-m\big), t=1,...,T \\ w_{t} \mapsto w_{t}
+ Ma_{t}^{2} \\ U = \big(x_{1}' - \hat{E}',...,x_{T}' -
+ \hat{E}' \big) \\ \hat{Cov} \equiv \frac{1}{T} U'U }
+
+ The location and scatter parameters that define the
+ ellipsoid are multivariate high-breakdown estimators of
+ location and scatter
+}
+\author{
+ Ram Ahluwalia \email{ram at wingedfootcapital.com}
+}
+\references{
+ \url{http://www.symmys.com/sites/default/files/Risk\%20and\%20Asset\%20Allocation\%20-\%20Springer\%20Quantitative\%20Finance\%20-\%20Estimation.pdf}
+ See Sec. 4.6.1 of "Risk and Asset Allocation" - Springer
+ (2005), by A. Meucci for the theory and the routine
+ implemented below See Meucci script for "ComputeMVE.m"
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMoments.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMoments.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/ComputeMoments.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,32 +1,32 @@
-\name{ComputeMoments}
-\alias{ComputeMoments}
-\title{Takes a matrix of joint-scenario probability distributions and generates expectations, standard devation, and correlation matrix for the assets}
-\usage{
- ComputeMoments(X, p)
-}
-\arguments{
- \item{X}{a matrix of joint-probability scenarios (rows
- are scenarios, columns are assets)}
-
- \item{p}{a numeric vector containing the probabilities
- for each of the scenarios in the matrix X}
-}
-\value{
- means a numeric vector of the expectations (probability
- weighted) for each asset
-
- sd a numeric vector of standard deviations corresponding
- to the assets in the covariance matrix
-
- correlationMatrix the correlation matrix resulting from
- converting the covariance matrix to a correlation matrix
-}
-\description{
- Takes a matrix of joint-scenario probability
- distributions and generates expectations, standard
- devation, and correlation matrix for the assets
-}
-\author{
- Ram Ahluwalia \email{ram at wingedfootcapital.com}
-}
-
+\name{ComputeMoments}
+\alias{ComputeMoments}
+\title{Takes a matrix of joint-scenario probability distributions and generates expectations, standard devation, and correlation matrix for the assets}
+\usage{
+ ComputeMoments(X, p)
+}
+\arguments{
+ \item{X}{a matrix of joint-probability scenarios (rows
+ are scenarios, columns are assets)}
+
+ \item{p}{a numeric vector containing the probabilities
+ for each of the scenarios in the matrix X}
+}
+\value{
+ means a numeric vector of the expectations (probability
+ weighted) for each asset
+
+ sd a numeric vector of standard deviations corresponding
+ to the assets in the covariance matrix
+
+ correlationMatrix the correlation matrix resulting from
+ converting the covariance matrix to a correlation matrix
+}
+\description{
+ Takes a matrix of joint-scenario probability
+ distributions and generates expectations, standard
+ devation, and correlation matrix for the assets
+}
+\author{
+ Ram Ahluwalia \email{ram at wingedfootcapital.com}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/CondProbViews.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/CondProbViews.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/CondProbViews.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,33 +1,33 @@
-\name{CondProbViews}
-\alias{CondProbViews}
-\title{Input conditional views}
-\usage{
- CondProbViews(View, X)
-}
-\arguments{
- \item{View}{TBD}
-
- \item{X}{TBD}
-}
-\value{
- A TBD
-
- b TBD
-
- g TBD
-}
-\description{
- statement: View(k).Who (e.g. [1 3])= View(k).Equal (e.g.
- {[2 3] [1 3 5]}) optional conditional statement:
- View(k).Cond_Who (e.g. [2])= View(k).Cond_Equal (e.g.
- {[1]}) amount of stress is quantified as Prob(statement)
- <= View(k).v if View(k).sgn = 1; Prob(statement) >=
- View(k).v if View(k).sgn = -1;
-}
-\details{
- confidence in stress is quantified in View(k).c in (0,1)
-}
-\author{
- Ram Ahluwalia \email{ram at wingedfootcapital.com}
-}
-
+\name{CondProbViews}
+\alias{CondProbViews}
+\title{Input conditional views}
+\usage{
+ CondProbViews(View, X)
+}
+\arguments{
+ \item{View}{TBD}
+
+ \item{X}{TBD}
+}
+\value{
+ A TBD
+
+ b TBD
+
+ g TBD
+}
+\description{
+ statement: View(k).Who (e.g. [1 3])= View(k).Equal (e.g.
+ {[2 3] [1 3 5]}) optional conditional statement:
+ View(k).Cond_Who (e.g. [2])= View(k).Cond_Equal (e.g.
+ {[1]}) amount of stress is quantified as Prob(statement)
+ <= View(k).v if View(k).sgn = 1; Prob(statement) >=
+ View(k).v if View(k).sgn = -1;
+}
+\details{
+ confidence in stress is quantified in View(k).c in (0,1)
+}
+\author{
+ Ram Ahluwalia \email{ram at wingedfootcapital.com}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/Cumul2Raw.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/Cumul2Raw.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/Cumul2Raw.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,36 +1,36 @@
-\name{Cumul2Raw}
-\alias{Cumul2Raw}
-\title{Transforms cumulants of Y-t into raw moments}
-\usage{
- Cumul2Raw(ka)
-}
-\arguments{
- \item{ka}{cumulants of Y}
-}
-\value{
- mu_ the raw non-central moments of Y
-}
-\description{
- step 5 of the projection process:
-}
-\details{
- From the cumulants of Y we compute the raw non-central
- moments of Y
-
- We do so recursively by the identity in formula (24)
- which follows from applying (21) and re-arranging terms
-
- \deqn{ \tilde{ \mu } ^{ \big(n\big) }_{Y} \equiv \kappa^{
- \big(n\big) }_{Y} + \sum_{k=1}^{n-1} (n-1)C_{k-1}
- \kappa_{Y}^{ \big(k\big) } \tilde{ \mu } ^{n-k}_{Y} }
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-\references{
- A. Meucci - "Annualization and General Projection of
- Skewness, Kurtosis and All Summary Statistics" - formula
- (24) Symmys site containing original MATLAB source code
- \url{http://www.symmys.com/node/136}
-}
-
+\name{Cumul2Raw}
+\alias{Cumul2Raw}
+\title{Transforms cumulants of Y-t into raw moments}
+\usage{
+ Cumul2Raw(ka)
+}
+\arguments{
+ \item{ka}{cumulants of Y}
+}
+\value{
+ mu_ the raw non-central moments of Y
+}
+\description{
+ step 5 of the projection process:
+}
+\details{
+ From the cumulants of Y we compute the raw non-central
+ moments of Y
+
+ We do so recursively by the identity in formula (24)
+ which follows from applying (21) and re-arranging terms
+
+ \deqn{ \tilde{ \mu } ^{ \big(n\big) }_{Y} \equiv \kappa^{
+ \big(n\big) }_{Y} + \sum_{k=1}^{n-1} (n-1)C_{k-1}
+ \kappa_{Y}^{ \big(k\big) } \tilde{ \mu } ^{n-k}_{Y} }
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+\references{
+ A. Meucci - "Annualization and General Projection of
+ Skewness, Kurtosis and All Summary Statistics" - formula
+ (24) Symmys site containing original MATLAB source code
+ \url{http://www.symmys.com/node/136}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/DetectOutliersViaMVE.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/DetectOutliersViaMVE.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/DetectOutliersViaMVE.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,32 +1,32 @@
-\name{DetectOutliersViaMVE}
-\alias{DetectOutliersViaMVE}
-\title{Use the minimum volume ellipsoid to detect outliers}
-\usage{
- DetectOutliersViaMVE(corruptSample)
-}
-\arguments{
- \item{corruptSample}{a matrix of returns with outlier
- data. Rows are observations, columns are assets.}
-}
-\value{
- a list containing: plotdata a matrix of data used to plot
- minimum volume ellipsoid as a function of its length
- cutofflist an ordering of observations with the highest
- Mahalanobis distance (i.e. ordering of outliers by their
- index )#' numOutliers returns the number of outliers
- based on the slope of the minimum volume ellipsoid as a
- function of sample data
-}
-\description{
- See Sec. 4.6.1 of "Risk and Asset Allocation" - Springer
- (2005), by A. Meucci for the theory and the routine
- implemented below
-}
-\author{
- Ram Ahluwalia \email{ram at wingedfootcapital.com}
-}
-\references{
- \url{http://www.symmys.com} See Meucci script for
- "S_HighBreakdownMVE.m"
-}
-
+\name{DetectOutliersViaMVE}
+\alias{DetectOutliersViaMVE}
+\title{Use the minimum volume ellipsoid to detect outliers}
+\usage{
+ DetectOutliersViaMVE(corruptSample)
+}
+\arguments{
+ \item{corruptSample}{a matrix of returns with outlier
+ data. Rows are observations, columns are assets.}
+}
+\value{
+ a list containing: plotdata a matrix of data used to plot
+ minimum volume ellipsoid as a function of its length
+ cutofflist an ordering of observations with the highest
+ Mahalanobis distance (i.e. ordering of outliers by their
+ index )#' numOutliers returns the number of outliers
+ based on the slope of the minimum volume ellipsoid as a
+ function of sample data
+}
+\description{
+ See Sec. 4.6.1 of "Risk and Asset Allocation" - Springer
+ (2005), by A. Meucci for the theory and the routine
+ implemented below
+}
+\author{
+ Ram Ahluwalia \email{ram at wingedfootcapital.com}
+}
+\references{
+ \url{http://www.symmys.com} See Meucci script for
+ "S_HighBreakdownMVE.m"
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/EntropyProg.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/EntropyProg.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/EntropyProg.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,88 +1,88 @@
-\name{EntropyProg}
-\alias{EntropyProg}
-\title{Entropy pooling program for blending views on scenarios with a prior scenario-probability distribution}
-\usage{
- EntropyProg(p, A, b, Aeq, beq)
-}
-\arguments{
- \item{p}{a vector of initial probabilities based on prior
- (reference model, empirical distribution, etc.). Sum of
- 'p' must be 1}
-
- \item{Aeq}{matrix consisting of equality constraints
- (paired with argument 'beq'). Denoted as 'H' in the
- Meucci paper. (denoted as 'H' in the "Meucci - Flexible
- Views Theory & Practice" paper formlua 86 on page 22)}
-
- \item{beq}{vector corresponding to the matrix of equality
- constraints (paired with argument 'Aeq'). Denoted as 'h'
- in the Meucci paper}
-
- \item{A}{matrix consisting of inequality constraints
- (paired with argument 'b'). Denoted as 'F' in the Meucci
- paper}
-
- \item{b}{vector consisting of inequality constraints
- (paired with matrix A). Denoted as 'f' in the Meucci
- paper}
-}
-\value{
- a list with p_ revised probabilities based on entropy
- pooling optimizationPerformance a list with status of
- optimization, value, number of iterations and sum of
- probabilities.
-}
-\description{
- Entropy program will change the initial predictive
- distribution 'p' to a new set 'p_' that satisfies
- specified moment conditions but changes other propoerties
- of the new distribution the least by minimizing the
- relative entropy between the two distributions.
- Theoretical note: Relative Entropy (Kullback-Leibler
- information criterion KLIC) is an asymmetric measure.
-}
-\details{
- We retrieve a new set of probabilities for the
- joint-scenarios using the Entropy pooling method Of the
- many choices of 'p' that satisfy the views, we choose 'p'
- that minimize the entropy or distance of the new
- probability distribution to the prior joint-scenario
- probabilities We use Kullback-Leibler divergence or
- relative entropy dist(p,q): Sum across all scenarios [
- p-t * ln( p-t / q-t ) ] Therefore we define solution as
- p* = argmin (choice of p ) [ sum across all scenarios:
- p-t * ln( p-t / q-t) ], such that 'p' satisfies views.
- The views modify the prior in a cohrent manner
- (minimizing distortion) We forumulate the stress tests of
- the baseline scenarios as linear constraints on yet-to-be
- defined probabilities Note that the numerical
- optimization acts on a very limited number of variables
- equal to the number of views. It does not act directly on
- the very large number of variables of interest, namely
- the probabilities of the Monte Carlo scenarios. This
- feature guarantees the numerical feasability of entropy
- optimization Note that new probabilities are generated in
- much the same way that the state-price density modifies
- objective probabilities of pay-offs to risk-neutral
- probabilities in contingent-claims asset pricing
-
- Compute posterior (=change of measure) with Entropy
- Pooling, as described in
-}
-\author{
- Ram Ahluwalia \email{ram at wingedfootcapital.com}
-}
-\references{
- A. Meucci - "Fully Flexible Views: Theory and Practice".
- See page 22 for illustration of numerical implementation
- Symmys site containing original MATLAB source code
- \url{http://www.symmys.com} NLOPT open-source
- optimization site containing background on algorithms
- \url{http://ab-initio.mit.edu/wiki/index.php/NLopt} We
- use the information-theoretic estimator of Kitamur and
- Stutzer (1997). Reversing 'p' and 'p_' leads to the
- empirical likelihood" estimator of Qin and Lawless
- (1994). See Robertson et al, "Forecasting Using Relative
- Entropy" (2002) for more theory
-}
-
+\name{EntropyProg}
+\alias{EntropyProg}
+\title{Entropy pooling program for blending views on scenarios with a prior scenario-probability distribution}
+\usage{
+ EntropyProg(p, A, b, Aeq, beq)
+}
+\arguments{
+ \item{p}{a vector of initial probabilities based on prior
+ (reference model, empirical distribution, etc.). Sum of
+ 'p' must be 1}
+
+ \item{Aeq}{matrix consisting of equality constraints
+ (paired with argument 'beq'). Denoted as 'H' in the
+ Meucci paper. (denoted as 'H' in the "Meucci - Flexible
+ Views Theory & Practice" paper formlua 86 on page 22)}
+
+ \item{beq}{vector corresponding to the matrix of equality
+ constraints (paired with argument 'Aeq'). Denoted as 'h'
+ in the Meucci paper}
+
+ \item{A}{matrix consisting of inequality constraints
+ (paired with argument 'b'). Denoted as 'F' in the Meucci
+ paper}
+
+ \item{b}{vector consisting of inequality constraints
+ (paired with matrix A). Denoted as 'f' in the Meucci
+ paper}
+}
+\value{
+ a list with p_ revised probabilities based on entropy
+ pooling optimizationPerformance a list with status of
+ optimization, value, number of iterations and sum of
+ probabilities.
+}
+\description{
+ Entropy program will change the initial predictive
+ distribution 'p' to a new set 'p_' that satisfies
+ specified moment conditions but changes other propoerties
+ of the new distribution the least by minimizing the
+ relative entropy between the two distributions.
+ Theoretical note: Relative Entropy (Kullback-Leibler
+ information criterion KLIC) is an asymmetric measure.
+}
+\details{
+ We retrieve a new set of probabilities for the
+ joint-scenarios using the Entropy pooling method Of the
+ many choices of 'p' that satisfy the views, we choose 'p'
+ that minimize the entropy or distance of the new
+ probability distribution to the prior joint-scenario
+ probabilities We use Kullback-Leibler divergence or
+ relative entropy dist(p,q): Sum across all scenarios [
+ p-t * ln( p-t / q-t ) ] Therefore we define solution as
+ p* = argmin (choice of p ) [ sum across all scenarios:
+ p-t * ln( p-t / q-t) ], such that 'p' satisfies views.
+ The views modify the prior in a cohrent manner
+ (minimizing distortion) We forumulate the stress tests of
+ the baseline scenarios as linear constraints on yet-to-be
+ defined probabilities Note that the numerical
+ optimization acts on a very limited number of variables
+ equal to the number of views. It does not act directly on
+ the very large number of variables of interest, namely
+ the probabilities of the Monte Carlo scenarios. This
+ feature guarantees the numerical feasability of entropy
+ optimization Note that new probabilities are generated in
+ much the same way that the state-price density modifies
+ objective probabilities of pay-offs to risk-neutral
+ probabilities in contingent-claims asset pricing
+
+ Compute posterior (=change of measure) with Entropy
+ Pooling, as described in
+}
+\author{
+ Ram Ahluwalia \email{ram at wingedfootcapital.com}
+}
+\references{
+ A. Meucci - "Fully Flexible Views: Theory and Practice".
+ See page 22 for illustration of numerical implementation
+ Symmys site containing original MATLAB source code
+ \url{http://www.symmys.com} NLOPT open-source
+ optimization site containing background on algorithms
+ \url{http://ab-initio.mit.edu/wiki/index.php/NLopt} We
+ use the information-theoretic estimator of Kitamur and
+ Stutzer (1997). Reversing 'p' and 'p_' leads to the
+ empirical likelihood" estimator of Qin and Lawless
+ (1994). See Robertson et al, "Forecasting Using Relative
+ Entropy" (2002) for more theory
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/GenerateLogNormalDistribution.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/GenerateLogNormalDistribution.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/GenerateLogNormalDistribution.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,28 +1,28 @@
-\name{GenerateLogNormalDistribution}
-\alias{GenerateLogNormalDistribution}
-\title{Generate arbitrary distribution of a shifted-lognormal invariant}
-\usage{
- GenerateLogNormalDistribution(J, a, m, s)
-}
-\arguments{
- \item{J}{a numeric with the number of scenarios}
-
- \item{a}{a numeric with the location shift parameter.
- Mean of distribution will be exp(a)}
-
- \item{m}{log of the mean of the distribution}
-
- \item{s}{log of the standard deviation of the
- distribution}
-}
-\value{
- X a numeric vector with i.i.d. lognormal samples based on
- parameters J, a, m, and s where X = a + exp( m + s * Z )
-}
-\description{
- %\deqn{X-t + a ~ LogN(m,s^2)} (formula 14)
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-
+\name{GenerateLogNormalDistribution}
+\alias{GenerateLogNormalDistribution}
+\title{Generate arbitrary distribution of a shifted-lognormal invariant}
+\usage{
+ GenerateLogNormalDistribution(J, a, m, s)
+}
+\arguments{
+ \item{J}{a numeric with the number of scenarios}
+
+ \item{a}{a numeric with the location shift parameter.
+ Mean of distribution will be exp(a)}
+
+ \item{m}{log of the mean of the distribution}
+
+ \item{s}{log of the standard deviation of the
+ distribution}
+}
+\value{
+ X a numeric vector with i.i.d. lognormal samples based on
+ parameters J, a, m, and s where X = a + exp( m + s * Z )
+}
+\description{
+ \deqn{X = a + e^{ m + sZ }} (formula 14)
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/MvnRnd.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/MvnRnd.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/MvnRnd.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,33 +1,33 @@
-\name{MvnRnd}
-\alias{MvnRnd}
-\title{Generates normal simulations whose sample moments match the population moments}
-\usage{
- MvnRnd(M, S, J)
-}
-\arguments{
- \item{M}{a numeric indicating the sample first moment of
- the distribution}
-
- \item{S}{a covariance matrix}
-
- \item{J}{a numeric indicating the number of trials}
-}
-\description{
- Adapted from file 'MvnRnd.m'. Most recent version of
- article and code available at
- http://www.symmys.com/node/162 see A. Meucci -
- "Simulations with Exact Means and Covariances", Risk,
- July 2009
-}
-\author{
- Ram Ahluwalia \email{rahluwalia at gmail.com}
-}
-\references{
- \url{http://www.symmys.com} TODO: Add Schur
- decomposition. Right now function is only sampling from
- mvrnorm so sample moments do no match population moments
- I have sample code commented out below to implement this
- correctly but I require a function that returns the
- unitaryMatrix from a Schur decomposition
-}
-
+\name{MvnRnd}
+\alias{MvnRnd}
+\title{Generates normal simulations whose sample moments match the population moments}
+\usage{
+ MvnRnd(M, S, J)
+}
+\arguments{
+ \item{M}{a numeric indicating the sample first moment of
+ the distribution}
+
+ \item{S}{a covariance matrix}
+
+ \item{J}{a numeric indicating the number of trials}
+}
+\description{
+ Adapted from file 'MvnRnd.m'. Most recent version of
+ article and code available at
+ http://www.symmys.com/node/162 see A. Meucci -
+ "Simulations with Exact Means and Covariances", Risk,
+ July 2009
+}
+\author{
+ Ram Ahluwalia \email{rahluwalia at gmail.com}
+}
+\references{
+ \url{http://www.symmys.com} TODO: Add Schur
+ decomposition. Right now function is only sampling from
+ mvrnorm so sample moments do no match population moments
+ I have sample code commented out below to implement this
+ correctly but I require a function that returns the
+ unitaryMatrix from a Schur decomposition
+}
+
Modified: pkg/PerformanceAnalytics/sandbox/Meucci/man/NoisyObservations.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/man/NoisyObservations.Rd 2012-07-23 02:41:05 UTC (rev 2196)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/man/NoisyObservations.Rd 2012-07-23 02:42:08 UTC (rev 2197)
@@ -1,28 +1,31 @@
-\name{NoisyObservations}
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 2197
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