[Returnanalytics-commits] r3115 - in pkg/Meucci: R demo man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Sep 16 10:26:10 CEST 2013
Author: xavierv
Date: 2013-09-16 10:26:10 +0200 (Mon, 16 Sep 2013)
New Revision: 3115
Modified:
pkg/Meucci/R/LognormalMoments2Parameters.R
pkg/Meucci/demo/00Index
pkg/Meucci/demo/S_LognormalSample.R
pkg/Meucci/man/BlackScholesCallPrice.Rd
pkg/Meucci/man/Central2Raw.Rd
pkg/Meucci/man/CentralAndStandardizedStatistics.Rd
pkg/Meucci/man/ConvertCompoundedReturns2Price.Rd
pkg/Meucci/man/Cumul2Raw.Rd
pkg/Meucci/man/EfficientFrontierPrices.Rd
pkg/Meucci/man/EfficientFrontierReturns.Rd
pkg/Meucci/man/EfficientFrontierReturnsBenchmark.Rd
pkg/Meucci/man/FitExpectationMaximization.Rd
pkg/Meucci/man/FitMultivariateGarch.Rd
pkg/Meucci/man/FitOrnsteinUhlenbeck.Rd
pkg/Meucci/man/GenerateUniformDrawsOnUnitSphere.Rd
pkg/Meucci/man/HorizonPricing.Rd
pkg/Meucci/man/InterExtrapolate.Rd
pkg/Meucci/man/Log2Lin.Rd
pkg/Meucci/man/LognormalMoments2Parameters.Rd
pkg/Meucci/man/MaxRsqCS.Rd
pkg/Meucci/man/MaxRsqTS.Rd
pkg/Meucci/man/MvnRnd.Rd
pkg/Meucci/man/PerformIidAnalysis.Rd
pkg/Meucci/man/PlotCompositionEfficientFrontier.Rd
pkg/Meucci/man/PlotMarginalsNormalInverseWishart.Rd
pkg/Meucci/man/PlotVolVsCompositionEfficientFrontier.Rd
pkg/Meucci/man/QuantileMixture.Rd
pkg/Meucci/man/RandNormalInverseWishart.Rd
pkg/Meucci/man/Raw2Central.Rd
pkg/Meucci/man/Raw2Cumul.Rd
pkg/Meucci/man/SimulateJumpDiffusionMerton.Rd
pkg/Meucci/man/SummStats.Rd
pkg/Meucci/man/garch1f4.Rd
pkg/Meucci/man/garch2f8.Rd
Log:
- improved description for demo files and generated documentation for last commit
Modified: pkg/Meucci/R/LognormalMoments2Parameters.R
===================================================================
--- pkg/Meucci/R/LognormalMoments2Parameters.R 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/R/LognormalMoments2Parameters.R 2013-09-16 08:26:10 UTC (rev 3115)
@@ -1,16 +1,18 @@
-#' Compute the mean and standard deviation of a lognormal distribution from its parameters, as described in
-#' A. Meucci, "Risk and Asset Allocation", Springer, 2005.
+#' @title Computes the mean and standard deviation of a lognormal distribution from its parameters.
#'
-#' @param e : [scalar] expected value of the lognormal distribution
-#' @param v : [scalar] variance of the lognormal distribution
+#' @description Computes the mean and standard deviation of a lognormal distribution from its parameters, as described in
+#' A. Meucci, "Risk and Asset Allocation", Springer, 2005.
+#'
+#' @param e [scalar] expected value of the lognormal distribution
+#' @param v [scalar] variance of the lognormal distribution
#'
-#' @return mu : [scalar] expected value of the normal distribution
-#' @return sig2 : [scalar] variance of the normal distribution
+#' @return mu [scalar] expected value of the normal distribution
+#' @return sig2 [scalar] variance of the normal distribution
#'
#' @note Inverts the formulas (1.98)-(1.99) in "Risk and Asset Allocation", Springer, 2005.
#'
#' @references
-#' \url{http://}
+#' A. Meucci - "Exercises in Advanced Risk and Portfolio Management" \url{http://symmys.com/node/170}., "E 25- Simulation of a lognormal random variable"
#' See Meucci's script for "LognormalMoments2Parameters.m"
#'
#' @author Xavier Valls \email{flamejat@@gmail.com}
Modified: pkg/Meucci/demo/00Index
===================================================================
--- pkg/Meucci/demo/00Index 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/demo/00Index 2013-09-16 08:26:10 UTC (rev 3115)
@@ -1,107 +1,106 @@
-AnalyticalvsNumerical This example script compares the numerical and the analytical solution of entropy-pooling
-ButterflyTrading This example script performs the butterfly-trading case study for the Entropy-Pooling approach by Attilio Meucci
-DetectOutliersviaMVE This example script detects outliers in two-asset and multi-asset case
-FullyFlexibleBayesNets This case study uses Entropy Pooling to compute Fully Flexible Bayesian networks for risk management
-FullFlexProbs This script uses Entropy Pooling to compute Fully Flexible Probabilities for historical scenarios
-FullyIntegratedLiquidityAndMarketRisk This script computes the liquidity-risk and funding-risk adjusted P&L distribution
-HermiteGrid_CaseStudy This script estimates the prior of a hedge fund return and processes extreme views on CVaR according to Entropy Pooling
-HermiteGrid_CVaR_Recursion This script illustrates the discrete Newton recursion to process views on CVaR according to Entropy Pooling
-HermiteGrid_demo This script compares the performance of plain Monte Carlo versus grid in applying Entropy Pooling to process extreme views
-InvariantProjection This script projects summary statistics to arbitrary horizons under i.i.d. assumption
-MeanDiversificationFrontier This script computes the mean-diversification efficient frontier
-Prior2Posterior This example script compares the numerical and the analytical solution of entropy-pooling
-RankingInformation This script performs ranking allocation using the Entropy-Pooling approach by Attilio Meucci
-RobustBayesianAllocation This script replicates the example from Meucci's MATLAB script S_SimulationsCaseStudy.M
-S_AnalyzeLognormalCorrelation This script considers a bivariate lognormal market and display the correlation and the condition number of the covariance matrix
-S_AnalyzeNormalCorrelation This script considers a bivariate normal market and display the correlation and the condition number of the covariance matrix
-S_AnalyzeNormalInverseWishart This script familiarizes the users with multivariate Bayesian estimation.
-S_AutocorrelatedProcess This script simulates a Ornstein-Uhlenbeck AR(1) process
-S_BivariateSample This script generates draws from a bivariate distribution with different marginals
-S_BlackLittermanBasic This script describes to basic market-based Black-Litterman approach
-S_BondProjectionPricingNormal This script projects the distribution of the market invariants for the bond markets from the estimation interval to the investment horizon
-S_BondProjectionPricingStudentT This script projects the distribution of the market invariants for the bond markets from the estimation interval to the investment horizon (Student's T assumption)
-S_BuyNHold This script illustrates the buy & hold dynamic strategy
-S_CPPI This script illustrates the CPPI (constant proportion portfolio insurance) dynamic strategy
-S_CallsProjectionPricing This script projects the distribution of the market invariants for the derivatives market and computes the distribution of prices at the investment horizon
-S_CheckDiagonalization This script verifies the correctness of the eigenvalue-eigenvector representation in terms of real matrices for the transition matrix of an OU process
-S_CornishFisher This script compares the Cornish-Fisher estimate of the VaR with the true analytical VaR under the lognormal assumptions
-S_CorrelationPriorUniform This script shows how a jointly uniform prior on the correlations implies that the marginal distribution of each correlation is peaked around zero
-S_CovarianceEvolution This script represents the evolution of the covariance of an OU process in terms of the dispersion ellipsoid
-S_CrossSectionConstrainedIndustries This script fits a cross-sectional linear factor model creating industry factors, where the industry factors are constrained to be uncorrelated with the market
-S_CrossSectionIndustries This script fits a cross-sectional linear factor model creating industry factors
-S_DerivativesInvariants This script performs the quest for invariance in the derivatives market
-S_DeterministicEvolution This script animates the evolution of the determinstic component of an OU process
-S_DisplayLognormalCopulaPdf This script displays the pdf of the copula of a lognormal distribution
-S_DisplayNormalCopulaCdf This script displays the cdf of the copula of a normal distribution
-S_DisplayNormalCopulaPdf This script displays the pdf of the copula of a normal distribution
-S_DisplayStudentTCopulaPdf This script displays the pdf of the copula of a Student t distribution
-S_ESContributionFactors This script computes the expected shortfall and the contributions to ES from each factor in simulations
-S_ESContributionsStudentT This script computes the expected shortfall and the contributions to ES from each security
-S_EigenvalueDispersion This script displays the sample eigenvalues dispersion phenomenon
-S_EllipticalNDim This script decomposes the N-variate normal distribution into its radial and uniform components to generate an elliptical distribution
-S_EquitiesInvariants This file performs the quest for invariance in the stock market
-S_EquityProjectionPricing This script projects the distribution of the market invariants for the stock market from the estimation interval (normal assumption) to the investment horizon. Then it computes the distribution of prices at the investment horizon analytically.
-S_EstimateExpectedValueEvaluation This script script familiarizes the user with the evaluation of an estimator replicability, loss, error, bias and inefficiency
-S_EstimateMomentsComboEvaluation This script familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
-S_EstimateQuantileEvaluation This script familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
-S_Estimator This script familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
-S_EvaluationGeneric This script determines the optimal allocation
-S_ExactMeanAndCovariance Generate draws from a multivariate normal with matching mean and covariance
-S_ExpectationMaximizationHighYield This script implements the Expectation-Maximization (EM) algoritm, which estimates the parameters of a multivariate normal distribution when some observations are randomly missing
-S_ExtremeValueTheory This script computes the quantile (VaR) analytically, in simulations and using the extreme value theory approximation
-S_FactorAnalysisNotOk This script illustrates the hidden factor analysis puzzle
-S_FactorResidualCorrelation This script illustrates exogenous loadings and endogenous factors the true analytical VaR under the lognormal assumptions from the estimation interval to the investment horizon
-S_FitProjectRates This script fits the swap rates dynamics to a multivariate Ornstein-Uhlenbeck process and computes and plots the estimated future distribution
-S_FitSwapToStudentT This script demonstrates the recursive ML estimation of the location and scatter parameters of a multivariate Student t distribution
-S_FixedIncomeInvariants This file performs the quest for invariance in the fixed income market
-S_FullCodependence This script illustrates the concept of co-dependence
-S_FxCopulaMarginal This script displays the empirical copula of a set of market variables
-S_GenerateMixtureSample This script generates draws from a univarite mixture
-S_HedgeOptions This script compares hedging based on Black-Scholes deltas with Factors on Demand hedging
-S_HorizonEffect This script studies horizon effect on explicit factors / implicit loadings linear model
-S_InvestorsObjective This script familiarizes the users with the objectives of different investors in a highly non-normal bi-variate market of securities
-S_JumpDiffusionMerton This script simulates a jump-diffusion process
-S_LinVsLogReturn This script project a distribution in the future according to the i.i.d.-implied square-root rule
-S_LognormalSample This script simulate univariate lognormal variables
-S_MarkovChainMonteCarlo This script illustrates the Metropolis-Hastings algorithm
-S_MaxMinVariance This script dispays location-dispersion ellipsoid and statistic
-S_MaximumLikelihood This script performs ML under a non-standard parametric set of distributions
-S_MeanVarianceBenchmark This script projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and translates this distribution into the returns distribution
-S_MeanVarianceCalls This script computes the mean-variance frontier of a set of options
-S_MeanVarianceHorizon This script projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and performs the two-step mean-variance optimization in terms of returns and relative portfolio weights.
-S_MeanVarianceOptimization This script projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and performs the two-step mean-variance optimization.
-S_MultiVarSqrRootRule This script illustrates the multivariate square root rule-of-thumb
-S_NonAnalytical This script generates draws for the sum of random variable
-S_NormalSample This script simulate univariate normal variables
-S_OrderStatisticsPdfLognormal This script script shows that the pdf of the r-th order statistics of a lognormal random variable
-S_OrderStatisticsPdfStudentT This script script shows that the pdf of the r-th order statistics of a tudent t random variable
-S_PasturMarchenko This script illustrate the Marchenko-Pastur limit of runifom matrix theory
-S_ProjectNPriceMvGarch This script fits a multivariate GARCH model and projects the distribution of the compounded returns from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon.
-S_ProjectSummaryStatistics This script projects summary statistics to arbitrary horizons
-S_PureResidualBonds This script models the joint distribution of the yet-to-be realized key rates of the government curve
-S_ResidualAnalysisTheory This script performs the analysis of residuals
-S_SelectionHeuristics Compute the r-square of selected factors
-S_SemiCircular This script illustrate the semi-circular law of random matrix theory
-S_ShrinkageEstimators This script computes the multivariate shrinkage estimators of location and scatter under the normal assumption
-S_SnPCaseStudy This script replicates the example from Meucci's MATLAB scriptS_SnPCaseStudy.M
-S_StatArbSwaps This script search for cointegrated stat-arb strategies among swap contracts
-S_StudentTSample This script simulate univariate Student-t variables
-S_SwapPca2Dim This script performs the principal component analysis of a simplified two-point swap curve
-S_TStatApprox Simulate invariants for the regression model
-S_TimeSeriesConstrainedIndustries This script fits a time-series linear factor computing the industry factors loadings, where the loadings are bounded and constrained to yield unit exposure
-S_TimeSeriesIndustries This script fits a time-series linear factor computing the industry factors loadings
-S_TimeSeriesVsCrossSectionIndustries This script computes the correlation between explicit, time-series industry factor returns and implicit, cross-section industry factor returns
-S_Toeplitz This script shows that the eigenvectors of a Toeplitz matrix have a Fourier basis structure under t-distribution assumptions
-S_UtilityMax This script illustrates the constant weight dynamic strategy that maximizes power utility
-S_VaRContributionsUniform This script computes the VaR and the contributions to VaR from each security anallitically and in simulations
-S_VolatilityClustering This file generates paths for a volatility clustering
-S_Wishart This script generates a sample from the 2x2 Wishart distribution
-S_WishartCorrelation This script computes the correlation of the first diagonal and off-diagonal elements of a 2x2 Wishart distribution as a function of the inputs
-S_WishartLocationDispersion This script computes the location-dispersion ellipsoid of the normalized first diagonal and off-diagonal elements of a 2x2 Wishart distribution as a function of the inputs
-S_ToyExample This toy example illustrates the use of Entropy Pooling to compute Fully Flexible
-logToArithmeticCovariance This example script generates arithmetric returns and arithmetric covariance matrix given a distribution of log returns
-S_plotGaussHermite This example script displays mesh points based on Gaussian-Hermite quadrature
- Bayesian networks
+AnalyticalvsNumerical compares the numerical and the analytical solution of entropy-pooling
+ButterflyTrading performs the butterfly-trading case study for the Entropy-Pooling approach by Attilio Meucci
+DetectOutliersviaMVE detects outliers in two-asset and multi-asset case
+FullyFlexibleBayesNets uses Entropy Pooling to compute Fully Flexible Bayesian networks for risk management
+FullFlexProbs uses Entropy Pooling to compute Fully Flexible Probabilities for historical scenarios
+FullyIntegratedLiquidityAndMarketRisk computes the liquidity-risk and funding-risk adjusted P&L distribution
+HermiteGrid_CaseStudy estimates the prior of a hedge fund return and processes extreme views on CVaR according to Entropy Pooling
+HermiteGrid_CVaR_Recursion illustrates the discrete Newton recursion to process views on CVaR according to Entropy Pooling
+HermiteGrid_demo compares the performance of plain Monte Carlo versus grid in applying Entropy Pooling to process extreme views
+InvariantProjection projects summary statistics to arbitrary horizons under i.i.d. assumption
+MeanDiversificationFrontier computes the mean-diversification efficient frontier
+Prior2Posterior compares the numerical and the analytical solution of entropy-pooling
+RankingInformation performs ranking allocation using the Entropy-Pooling approach by Attilio Meucci
+RobustBayesianAllocation replicates the example from Meucci's MATLAB script S_SimulationsCaseStudy.M
+S_AnalyzeLognormalCorrelation considers a bivariate lognormal market and display the correlation and the condition number of the covariance matrix
+S_AnalyzeNormalCorrelation considers a bivariate normal market and display the correlation and the condition number of the covariance matrix
+S_AnalyzeNormalInverseWishart familiarizes the users with multivariate Bayesian estimation.
+S_AutocorrelatedProcess simulates a Ornstein-Uhlenbeck AR(1) process
+S_BivariateSample generates draws from a bivariate distribution with different marginals
+S_BlackLittermanBasic describes to basic market-based Black-Litterman approach
+S_BondProjectionPricingNormal projects the distribution of the market invariants for the bond markets from the estimation interval to the investment horizon
+S_BondProjectionPricingStudentT projects the distribution of the market invariants for the bond markets from the estimation interval to the investment horizon (Student's T assumption)
+S_BuyNHold illustrates the buy & hold dynamic strategy
+S_CPPI illustrates the CPPI (constant proportion portfolio insurance) dynamic strategy
+S_CallsProjectionPricing projects the distribution of the market invariants for the derivatives market and computes the distribution of prices at the investment horizon
+S_CheckDiagonalization verifies the correctness of the eigenvalue-eigenvector representation in terms of real matrices for the transition matrix of an OU process
+S_CornishFisher compares the Cornish-Fisher estimate of the VaR with the true analytical VaR under the lognormal assumptions
+S_CorrelationPriorUniform shows how a jointly uniform prior on the correlations implies that the marginal distribution of each correlation is peaked around zero
+S_CovarianceEvolution represents the evolution of the covariance of an OU process in terms of the dispersion ellipsoid
+S_CrossSectionConstrainedIndustries fits a cross-sectional linear factor model creating industry factors, where the industry factors are constrained to be uncorrelated with the market
+S_CrossSectionIndustries fits a cross-sectional linear factor model creating industry factors
+S_DerivativesInvariants performs the quest for invariance in the derivatives market
+S_DeterministicEvolution animates the evolution of the determinstic component of an OU process
+S_DisplayLognormalCopulaPdf displays the pdf of the copula of a lognormal distribution
+S_DisplayNormalCopulaCdf displays the cdf of the copula of a normal distribution
+S_DisplayNormalCopulaPdf displays the pdf of the copula of a normal distribution
+S_DisplayStudentTCopulaPdf displays the pdf of the copula of a Student t distribution
+S_ESContributionFactors computes the expected shortfall and the contributions to ES from each factor in simulations
+S_ESContributionsStudentT computes the expected shortfall and the contributions to ES from each security
+S_EigenvalueDispersion displays the sample eigenvalues dispersion phenomenon
+S_EllipticalNDim decomposes the N-variate normal distribution into its radial and uniform components to generate an elliptical distribution
+S_EquitiesInvariants performs the quest for invariance in the stock market
+S_EquityProjectionPricing projects the distribution of the market invariants for the stock market from the estimation interval (normal assumption) to the investment horizon. Then it computes the distribution of prices at the investment horizon analytically.
+S_EstimateExpectedValueEvaluation script familiarizes the user with the evaluation of an estimator replicability, loss, error, bias and inefficiency
+S_EstimateMomentsComboEvaluation familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
+S_EstimateQuantileEvaluation familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
+S_Estimator familiarizes the user with the evaluation of an estimator: replicability, loss, error, bias and inefficiency
+S_EvaluationGeneric determines the optimal allocation
+S_ExactMeanAndCovariance generate draws from a multivariate normal with matching mean and covariance
+S_ExpectationMaximizationHighYield implements the Expectation-Maximization (EM) algoritm, which estimates the parameters of a multivariate normal distribution when some observations are randomly missing
+S_ExtremeValueTheory computes the quantile (VaR) analytically, in simulations and using the extreme value theory approximation
+S_FactorAnalysisNotOk illustrates the hidden factor analysis puzzle
+S_FactorResidualCorrelation illustrates exogenous loadings and endogenous factors the true analytical VaR under the lognormal assumptions from the estimation interval to the investment horizon
+S_FitProjectRates fits the swap rates dynamics to a multivariate Ornstein-Uhlenbeck process and computes and plots the estimated future distribution
+S_FitSwapToStudentT demonstrates the recursive ML estimation of the location and scatter parameters of a multivariate Student t distribution
+S_FixedIncomeInvariants performs the quest for invariance in the fixed income market
+S_FullCodependence illustrates the concept of co-dependence
+S_FxCopulaMarginal displays the empirical copula of a set of market variables
+S_GenerateMixtureSample generates draws from a univarite mixture
+S_HedgeOptions compares hedging based on Black-Scholes deltas with Factors on Demand hedging
+S_HorizonEffect studies horizon effect on explicit factors / implicit loadings linear model
+S_InvestorsObjective familiarizes the users with the objectives of different investors in a highly non-normal bi-variate market of securities
+S_JumpDiffusionMerton simulates a jump-diffusion process
+S_LinVsLogReturn project a distribution in the future according to the i.i.d.-implied square-root rule
+S_LognormalSample simulate univariate lognormal variables
+S_MarkovChainMonteCarlo illustrates the Metropolis-Hastings algorithm
+S_MaxMinVariance dispays location-dispersion ellipsoid and statistic
+S_MaximumLikelihood performs ML under a non-standard parametric set of distributions
+S_MeanVarianceBenchmark projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and translates this distribution into the returns distribution
+S_MeanVarianceCalls computes the mean-variance frontier of a set of options
+S_MeanVarianceHorizon projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and performs the two-step mean-variance optimization in terms of returns and relative portfolio weights.
+S_MeanVarianceOptimization projects the distribution of the market invariants for the bond and stock markets from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon and performs the two-step mean-variance optimization.
+S_MultiVarSqrRootRule illustrates the multivariate square root rule-of-thumb
+S_NonAnalytical generates draws for the sum of random variable
+S_NormalSample simulate univariate normal variables
+S_OrderStatisticsPdfLognormal script shows that the pdf of the r-th order statistics of a lognormal random variable
+S_OrderStatisticsPdfStudentT script shows that the pdf of the r-th order statistics of a tudent t random variable
+S_PasturMarchenko illustrate the Marchenko-Pastur limit of runifom matrix theory
+S_ProjectNPriceMvGarch fits a multivariate GARCH model and projects the distribution of the compounded returns from the estimation interval to the investment horizon. Then it computes the distribution of prices at the investment horizon.
+S_ProjectSummaryStatistics projects summary statistics to arbitrary horizons
+S_PureResidualBonds models the joint distribution of the yet-to-be realized key rates of the government curve
+S_ResidualAnalysisTheory performs the analysis of residuals
+S_SelectionHeuristics computes the r-square of selected factors
+S_SemiCircular illustrate the semi-circular law of random matrix theory
+S_ShrinkageEstimators computes the multivariate shrinkage estimators of location and scatter under the normal assumption
+S_SnPCaseStudy replicates the example from Meucci's MATLAB scriptS_SnPCaseStudy.M
+S_StatArbSwaps search for cointegrated stat-arb strategies among swap contracts
+S_StudentTSample simulate univariate Student-t variables
+S_SwapPca2Dim performs the principal component analysis of a simplified two-point swap curve
+S_TStatApprox simulates invariants for the regression model
+S_TimeSeriesConstrainedIndustries fits a time-series linear factor computing the industry factors loadings, where the loadings are bounded and constrained to yield unit exposure
+S_TimeSeriesIndustries fits a time-series linear factor computing the industry factors loadings
+S_TimeSeriesVsCrossSectionIndustries computes the correlation between explicit, time-series industry factor returns and implicit, cross-section industry factor returns
+S_Toeplitz shows that the eigenvectors of a Toeplitz matrix have a Fourier basis structure under t-distribution assumptions
+S_UtilityMax illustrates the constant weight dynamic strategy that maximizes power utility
+S_VaRContributionsUniform computes the VaR and the contributions to VaR from each security anallitically and in simulations
+S_VolatilityClustering generates paths for a volatility clustering
+S_Wishart generates a sample from the 2x2 Wishart distribution
+S_WishartCorrelation computes the correlation of the first diagonal and off-diagonal elements of a 2x2 Wishart distribution as a function of the inputs
+S_WishartLocationDispersion computes the location-dispersion ellipsoid of the normalized first diagonal and off-diagonal elements of a 2x2 Wishart distribution as a function of the inputs
+S_ToyExample illustrates the use of Entropy Pooling to compute Fully Flexible probabilities
+logToArithmeticCovariance generates arithmetric returns and arithmetric covariance matrix given a distribution of log returns
+S_plotGaussHermite displays mesh points based on Gaussian-Hermite quadrature Bayesian networks
Modified: pkg/Meucci/demo/S_LognormalSample.R
===================================================================
--- pkg/Meucci/demo/S_LognormalSample.R 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/demo/S_LognormalSample.R 2013-09-16 08:26:10 UTC (rev 3115)
@@ -1,8 +1,9 @@
-#' This script simulate univariate lognormal variables, as described in
+#' This script simulates univariate lognormal variables, as described in
#' A. Meucci, "Risk and Asset Allocation", Springer, 2005, Chapter 1.
#'
#' @references
-#' \url{http://}
+#' @references
+#' A. Meucci - "Exercises in Advanced Risk and Portfolio Management" \url{http://symmys.com/node/170}., "E 25- Simulation of a lognormal random variable"
#' See Meucci's script for "S_LognormalSample.m"
#'
#' @author Xavier Valls \email{flamejat@@gmail.com}
Modified: pkg/Meucci/man/BlackScholesCallPrice.Rd
===================================================================
--- pkg/Meucci/man/BlackScholesCallPrice.Rd 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/man/BlackScholesCallPrice.Rd 2013-09-16 08:26:10 UTC (rev 3115)
@@ -45,7 +45,8 @@
Xavier Valls \email{flamejat at gmail.com}
}
\references{
- \url{http://symmys.com/node/170} See Meucci's script for
- "BlackScholesCallPrice.m"
+ A. Meucci - "Exercises in Advanced Risk and Portfolio
+ Management" \url{http://symmys.com/node/170}. See
+ Meucci's script for "BlackScholesCallPrice.m"
}
Modified: pkg/Meucci/man/Central2Raw.Rd
===================================================================
--- pkg/Meucci/man/Central2Raw.Rd 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/man/Central2Raw.Rd 2013-09-16 08:26:10 UTC (rev 3115)
@@ -13,7 +13,7 @@
corresponding raw moments
}
\description{
- step 2 of projection process: From the central moments of
+ 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)
@@ -32,7 +32,8 @@
Management". See page 10. Symmys site containing original
MATLAB source code \url{http://www.symmys.com}
- \url{http://symmys.com/node/170} See Meucci's script for
- "Central2Raw.m"
+ A. Meucci - "Exercises in Advanced Risk and Portfolio
+ Management" \url{http://symmys.com/node/170}. See
+ Meucci's script for "Central2Raw.m"
}
Modified: pkg/Meucci/man/CentralAndStandardizedStatistics.Rd
===================================================================
--- pkg/Meucci/man/CentralAndStandardizedStatistics.Rd 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/man/CentralAndStandardizedStatistics.Rd 2013-09-16 08:26:10 UTC (rev 3115)
@@ -25,7 +25,8 @@
Xavier Valls \email{flamejat at gmail.com}
}
\references{
- \url{http://symmys.com/node/170} See Meucci's script for
- "CentralAndStandardizedStatistics.m"
+ A. Meucci - "Exercises in Advanced Risk and Portfolio
+ Management" \url{http://symmys.com/node/170}. See
+ Meucci's script for "CentralAndStandardizedStatistics.m"
}
Modified: pkg/Meucci/man/ConvertCompoundedReturns2Price.Rd
===================================================================
--- pkg/Meucci/man/ConvertCompoundedReturns2Price.Rd 2013-09-16 08:06:30 UTC (rev 3114)
+++ pkg/Meucci/man/ConvertCompoundedReturns2Price.Rd 2013-09-16 08:26:10 UTC (rev 3115)
@@ -29,9 +29,10 @@
Xavier Valls \email{flamejat at gmail.com}
}
\references{
- \url{http://symmys.com/node/170} See (6.77)-(6.79) in
- "Risk and Asset Allocation"-Springer (2005), by A. Meucci
- See Meucci's script for
+ A. Meucci - "Exercises in Advanced Risk and Portfolio
+ Management" \url{http://symmys.com/node/170}. See
[TRUNCATED]
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