Monthly equity returns

Description

A matrix holding time series of monthly returns (calculated from closing prices) for six stocks. The returns span the period from Jaunary 1998 through December 2003.

Usage

monthlyReturns

Format

A matrix with 6 columns and 71 rows. The names of the rows hold the dates of each series entry, and the column names are the names of the six equities from which the return series are taken.

Examples

CAPMList(monthlyReturns, marketIndex = sp500Returns, riskFree = US13wTB)

Global package options

Description

This function can be used to set or get global options for the BLCOP package.

Usage

BLCOPOptions(opt, setting)

Arguments

Details

If setting is omitted, the current setting for opt is returned. If both arguments are omitted, a list with all of the settings is returned. The following settings may be changed: regFunc:Function used to perform the regression in CAPMalphas numSimulations:Number of monte-carlo simulations to perform in copula opinion pooling functions unitTestPath: Path where unit tests are located.

Value

If both arguments omitted, a list. If setting is omitted, value of an individual setting.

Author(s)

Francisco Gochez <fgochez@mango-solutions>

Examples

    BLCOPOptions("numSimulations")

BLposterior

Description

BLposterior

Usage

BLPosterior(returns, views, tau = 1, marketIndex, riskFree = NULL,
  kappa = 0, covEstimator = "cov")

Arguments

Value

An object of class BLResult

Author(s)

Francisco

Class "BLResult": posterior of a market distribution in the Black-Litterman sense

Description

This class holds the posterior market mean and variance-covariance matrix calculated from some prior and set of views. The original views are also returned.

Objects from the Class

Objects can be created by calls of the form new("BLResult", ...). However, it is intended that they be created by the function posteriorEst(or wrappers to that function).

Slots

views:

Object of class "BLViews". These are the original views used to calculate this posterior

tau:

Object of class "numeric". The value of "tau" used

priorMean:

Object of class "numeric": prior vector of market means

priorCovar:

Object of class "matrix": prior of the variance-covariance

posteriorMean:

Object of class "numeric": posterior mean

posteriorCovar:

Object of class "matrix": posterior variance-covariance

kappa:

Object of class "logical": logical flag indicating whether or not confidences-in-views were ignored.

Methods

denityPlots

signature(result = "BLResult"): Plots the marginal distributions of the asset returns under the prior and posterior distributions

show

signature(object = "BLResult"): Displays the contents of a result

optimalPortfolios.fPort

signature(result = "BLResult"): Generates optimal prior and posterior portfolios using fPortfolio package routines

Author(s)

Francisco Gochez

Class "BLViews" (Black-Litterman views)

Description

An object that holds a set of analyst views, in the Black-Litterman sense, on a set of assets

Objects from the Class

Objects can be created by calls of the form new("BLViews", ...) or with the BLViews function.

Slots

P:

Object of class "matrix". The "pick" matrix

qv:

Object of class "numeric". Means of the views

confidences:

Object of class "numeric". Holds the confidence in each of the individual views

assets:

Object of class "character": Name of the asset "universe" to which these views apply

Methods

deleteViews

signature(views = "BLViews", viewsToDel = "numeric"): Deletes a vector of views from the object, where the vector entries correspond to rows of the pick matrix

show

signature(object = "BLViews"): Prints views in a user-friendly manner

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Create or add to a BLViews object

Description

BLViews and COPViews are "constructors" for BLViews and COPViews objects respectively. addBLViews and addCOPViews allow one to easily add more views to a pre-existing views objects. newPMatrix is a utility function for creating pick matrices.

Usage

	
addBLViews(pickMatrix, q, confidences, views)
addCOPViews(pickMatrix, viewDist, confidences, views) 
BLViews(P, q, confidences, assetNames)
COPViews(pickMatrix, viewDist, confidences, assetNames) 
newPMatrix(assetNames, numViews, defaultValue = 0)

Arguments

Value

A BLViews or COPViews class object as appropriate. newPMatrix creates a matrix.

Author(s)

Francisco Gochez

See Also

createBLViews, updateBLViews

Examples

    ### example from T. M. Idzorek's paper "A STEP-BY-STEP GUIDE TO THE 
	### BLACK-LITTERMAN MODEL"
 	## Not run: 
	    pick <- newPMatrix(letters[1:8], 3)
	    pick[1,7] <- 1
	    pick[2,1] <- -1 
	    pick[2,2] <- 1
	    pick[3, 3:6] <- c(0.9, -0.9, .1, -.1)
	    confidences <- 1 / c(0.00709, 0.000141, 0.000866)
	    myViews <- BLViews(pick, q = c(0.0525, 0.0025, 0.02), confidences, letters[1:8])
	    myViews
	    
	    ### Modified COP example from Meucci's "Beyond Black-Litterman: Views on
		### non-normal markets" 
	    dispersion <- c(.376,.253,.360,.333,.360,.600,.397,.396,.578,.775) / 1000
	    sigma <- BLCOP:::.symmetricMatrix(dispersion, dim = 4)
	    caps <- rep(1/4, 4)
	    mu <- 2.5 * sigma 
	    dim(mu) <- NULL
	    marketDistribution <- mvdistribution("mt", mean = mu, S = sigma, df = 5 )
	    pick <- newPMatrix(c("SP", "FTSE", "CAC", "DAX"), 1)
	    pick[1,4] <- 1
	    vdist <- list(distribution("unif", min = -0.02, max = 0))
	    views <- COPViews(pick, vdist, 0.2, c("SP", "FTSE", "CAC", "DAX"))
    
## End(Not run)

Compute CAPM alphas for a set of assets

Description

CAPMList is a helper function that computes the "alphas" and "betas" in the sense of the CAPM for series of asset returns. It is meant to be used for computing "prior" means for the Black-Litterman model.

Usage

CAPMList(returns, marketIndex, riskFree = NULL, regFunc = BLCOPOptions("regFunc"), 
         coeffExtractFunc = NULL, ...)

Arguments

Details

coeffExtractFun is needed because some regression functions such as gls from the nlme package don't return their results in the same format as lm does. If it is not supplied, a default that works with lm results is used.

Value

A data.frame with one column for the "alphas" and another for the "betas"

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Examples

    library(MASS)
    CAPMList(monthlyReturns, marketIndex = sp500Returns, riskFree = US13wTB, regFunc = "rlm")

Calculate the posterior distribution of the market using copula opinion pooling

Description

COPPosteior uses Attilio Meucci's copula opinion pooling method to incorporate an analyst's subjective views with a prior "official" market distribution. Both the views and the market may have an arbitrary distribution as long as it can be sampled in R. Calculations are done with monte-carlo simulation, and the object returned will hold samples drawn from the market posterior distribution.

Usage

    COPPosterior(marketDist, views, numSimulations = BLCOPOptions("numSimulations"))

Arguments

Value

An object of class COPResult.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

References

Attilio Meucci, "Beyond Black-Litterman:Views on Non-normal Markets". See also Attilio Meucci, "Beyond Black-Litterman in Practice: a Five-Step Recipe to Input Views on non-Normal Markets."

See Also

BLPosterior

Examples

    
    ## Not run:     
        # An example based on one found in "Beyond Black-Litterman:Views on Non-normal Markets"
        dispersion <- c(.376,.253,.360,.333,.360,.600,.397,.396,.578,.775) / 1000
        sigma <- BLCOP:::.symmetricMatrix(dispersion, dim = 4)
        caps <- rep(1/4, 4)
        mu <- 2.5 * sigma 
        dim(mu) <- NULL
        marketDistribution <- mvdistribution("mt", mean = mu, S = sigma, df = 5 )
        pick <- matrix(0, ncol = 4, nrow = 1, dimnames = list(NULL, c("SP", "FTSE", "CAC", "DAX")))
        pick[1,4] <- 1
        vdist <- list(distribution("unif", min = -0.02, max = 0))
    
        views <- COPViews(pick, vdist, 0.2, c("SP", "FTSE", "CAC", "DAX"))
        posterior <- COPPosterior(marketDistribution, views)
    
## End(Not run)

Class "COPResult"

Description

A class that holds the posterior distribution produced with the COP framework

Objects from the Class

Objects can be created by calls of the form new("COPResult", ...). In general however they are created by the function COPPosterior

Slots

views:

Object of class "COPViews". These are the views that led to the result

marketDist:

Object of class "mvdistribution". Prior distribution of the market

posteriorSims:

Object of class "matrix". Matrices holding the simulations of the posteriors with a column for each asset.

Methods

densityPlots

signature(result = "COPResult"): Generates density plots of the marginal prior and posterior distributions of each asset.

show

signature(result = "COPResult"): Displays basic information about the posterior results

optimalPortfolios.fPort

signature(result = "COPResult"): Generates optimal prior and posterior portfolios using fPortfolio package routines

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

COPPosterior, BLResult-class

Class "COPViews" (copula opinion pooling views)

Description

An object that holds a set of analyst views, in the copula opinion pooling sense, on a set of assets

Objects from the Class

Objects can be created by calls of the form new("COPViews", ...) or with the COPViews function.

Slots

pick:

Object of class "matrix". The pick matrix

viewDist:

Object of class "list". List of probability distributions of the views

confidences:

Object of class "numeric".

assets:

Object of class "character". Name of the asset "universe" to which these views apply.

Methods

deleteViews

signature(views = "COPViews", viewsToDel = "numeric"): Deletes a vector of views from the object, where the vector entries correspond to rows of the pick matrix

show

signature(object = "COPViews"): Prints views in a user-friendly manner

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

BLViews, COPViews, addCOPViews, createCOPViews

Examples

    showClass("COPViews")

Create or add to a view object using a graphical interface

Description

These helper functions allow one to easily create or add to an object of class BLViews or COPViews through the use of R's built-in data editor.

Usage

createBLViews(allAssets, numAssetViews = 1, assetSubset = NULL, 
              mode = c("editor", "Window"))
updateBLViews(views, includeNullViews = FALSE, numNewViews = 0, assets = NULL)
createCOPViews (allAssets, numAssetViews = 1, assetSubset = NULL, 
                mode = c("editor", "Window")) 

Arguments

.

Details

createCOPViews does not allow one to specify the distributions of the views at the moment. Such a feature may be added later through another GUI. At the moment the object returned by this function has its distribution set to a default. updateViews allows one to modify pre-existing views

Value

An object of class BLViews or COPViews that holds all of the views created.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

addBLViews, addCOPViews, COPViews, BLViews

Examples

    
    ## Not run: 
        views <- createBLViews(colnames(monthlyReturns), 2)
    
## End(Not run)

Constructors for distribution and mvdistribution class objects

Description

These functions create objects of class distribution and mvdistribution

Usage

mvdistribution(RName, ...)
distribution(RName, ...)

Arguments

Details

In general any distribution with a corresponding sampling function can be used. This function should have the name given in RName but preceded with an "r", e.g. rnorm for the normal distribution. When the constructors are called, they check that the given sampling function exists and that it takes the arguments that were passed in the ....

Value

An object of class distribution or mvdistribution.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

sampleFrom

Examples

    ## Not run: 
	    # create a uniform distribution object and sample from it
	    myUnif <- distribution("unif", min = -0.1, max = 0.1)
	    hist(sampleFrom(myUnif, 1000))
	    
	    mvNormal <- mvdistribution("mnorm", mean = c(1, 5), varcov = diag(c(2, 0.1)))
	    x <- sampleFrom(mvNormal, 1000)
	    plot(x[,1] ~ x[,2])
    
## End(Not run)

Get prior and posterior estimators stored in package scope

Description

These functions are not intended to be called directly by the user but exist to allow third party optimizer routines to access prior and posterior estimators calculated as part of the portfolio optimisation.

Usage

getPriorEstim(x, spec=NULL, ...)
getPosteriorEstim(x, spec=NULL, ...)

Arguments

Value

A list with 2 elements:

Author(s)

Richard Chandler-Mant <rchandler-mant@mango-solutions.com>

Extract various fields of view or posterior objects

Description

A collection of functions to extract several fields of BLViews, COPViews, COPPosterior and BLPosterior objects.

Usage

assetSet(views)
viewMatrix(views, dropZeroColumns = TRUE)
PMatrix(views)
confidences(views)
posteriorMeanCov(posterior)
posteriorSimulations(posterior)
numSimulations(posterior)
priorViews(posterior)

Arguments

Value

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Examples

    pick <- matrix(0, ncol = 4, nrow = 1, dimnames = list(NULL, c("SP", "FTSE", "CAC", "DAX")))
    pick[1,4] <- 1
    vdist <- list(distribution("unif", min = -0.02, max = 0))
    views <- COPViews(pick, vdist, 0.2, c("SP", "FTSE", "CAC", "DAX"))
    assetSet(views)
    confidences(views)
    PMatrix(views)

Various functions for modifying fields of view objects

Description

These functions allow for direct replacement of fields of view objects such as the pick matrix and vector of confidences.

Usage

PMatrix(views) <- value
confidences(views) <- value
qv(views) <- value

Arguments

Value

The object is modified directly

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Examples

## example from Thomas M. Idzorek's paper "A STEP-BY-STEP GUIDE TO THE BLACK-LITTERMAN MODEL"
x <- c(0.001005,0.001328,-0.000579,-0.000675,0.000121,0.000128,-0.000445,-0.000437 ,
     0.001328,0.007277,-0.001307,-0.000610,-0.002237,-0.000989,0.001442,-0.001535 ,
     -0.000579,-0.001307,0.059852,0.027588,0.063497,0.023036,0.032967,0.048039 ,
    -0.000675,-0.000610,0.027588,0.029609,0.026572,0.021465,0.020697,0.029854 ,
     0.000121,-0.002237,0.063497,0.026572,0.102488,0.042744,0.039943,0.065994 ,
     0.000128,-0.000989,0.023036,0.021465,0.042744,0.032056,0.019881,0.032235 ,
    -0.000445,0.001442,0.032967,0.020697,0.039943,0.019881,0.028355,0.035064 ,
    -0.000437,-0.001535,0.048039,0.029854,0.065994,0.032235,0.035064,0.079958 )

    varCov <- matrix(x, ncol = 8, nrow = 8)
    mu <- c(0.08, 0.67,6.41, 4.08, 7.43, 3.70, 4.80, 6.60) / 100
    pick <- matrix(0, ncol = 8, nrow = 3, dimnames = list(NULL, letters[1:8]))
    pick[1,7] <- 1
    pick[2,1] <- -1; pick[2,2] <- 1
    pick[3, 3:6] <- c(0.9, -0.9, .1, -.1)
    confidences <- 1 / c(0.000709, 0.000141, 0.000866)
    myViews <- BLViews(pick, c(0.0525, 0.0025, 0.02), confidences, letters[1:8])
    myPosterior <- posteriorEst(myViews, tau = 0.025, mu, varCov )
    myPosterior
    # increase confidences
   	confidences(myViews) <-  1 / c(0.0001, 0.0001, 0.0005)
    myPosterior2 <- posteriorEst(myViews, tau = 0.025, mu, varCov )
    myPosterior2

Risk free rate of return

Description

The monthly rate of return of the US 13 week Treasury Bill for the period 30/1/1998 through 30/11/2003.

Usage

US13wTB

Format

A one-column matrix with 71 rows.

Examples

ts.plot(US13wTB)

Delete individual views from view objects

Description

A generic function that allows one to delete individual views from objects of class BLViews or COPViews. The inputs are a view object and a numeric vector of views to delete, where the entires of the vector map to rows of the pick matrix.

Usage

deleteViews(views, viewsToDel)

Arguments

Value

The original object with the indicated views deleted

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

BLViews-class, COPViews-class

Examples

    stocks <- colnames(monthlyReturns)
    pick <- matrix(0, ncol = 6, nrow = 2, dimnames = list(NULL, stocks))
    pick[1,"IBM"] <- 1
    pick[1, "DELL"] <- 0.04
    pick[2, "C"] <- 1
    pick[2, "JPM"] <- 0.6

    confidences <- 1 / c(0.7, 0.1)

    views <- BLViews( P = pick, q = c(0.1,0.1) , confidences = confidences,stocks)
    deleteViews(views, 1)
   

Density plots of prior and posterior distributions

Description

This generic function generates density plots of the marginal posterior and prior distributions of a set of assets in an object of class BLResult or COPResult for comparative purposes.

Usage

densityPlots(result, assetsSel = NULL, numSimulations = BLCOPOptions("numSimulations"), 
             ...)

Arguments

Details

For COPResults objects, density kernel estimates from the samples are used

Value

None

Author(s)

Francisco Gochez, <fgochez@mango-solutions>

Examples

    ## Not run: 
        dispersion <- c(.376,.253,.360,.333,.360,.600,.397,.396,.578,.775) / 1000
        sigma <- BLCOP:::.symmetricMatrix(dispersion, dim = 4)
        caps <- rep(1/4, 4)
        mu <- 2.5 * sigma 
        dim(mu) <- NULL
        marketDistribution <- mvdistribution("mt", mean = mu, S = sigma, df = 5 )
        pick <- matrix(0, ncol = 4, nrow = 1, dimnames = list(NULL, c("SP", "FTSE", "CAC", "DAX")))
        pick[1,4] <- 1
        vdist <- list(distribution("unif", min = -0.02, max = 0))
    
        views <- COPViews(pick, vdist, 0.2, c("SP", "FTSE", "CAC", "DAX"))
        posterior <- COPPosterior(marketDistribution, views)
        densityPlots(posterior, 4)
        
## End(Not run)

Class "distribution"

Description

A class that describes univariate distributions

Objects from the Class

Objects can be created by calls of the form new("distribution", ...). There is also a constructor which is also named distribution.

Slots

RName:

Object of class "character". This is the R "suffix" of the distirbution.

parameters:

Object of class "numeric". A named numeric vector that holds the parameters of the distribution

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

distribution, mvdistribution, mvdistribution

Examples

    showClass("distribution")

Class "mvdistribution"

Description

A class that describes multivariate distributions

Objects from the Class

Objects can be created by calls of the form new("distribution", ...). There is also a constructor which is also named mvdistribution.

Slots

RName:

Object of class "character". This is the R "suffix" of the distirbution.

parameters:

A named list of parameters that characterize the distribution

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

See Also

distribution, mvdistribution, distribution-class

Examples

      showClass("mvdistribution")

Calculates optimal portfolios under prior and posterior distributions

Description

These are wrapper functions that calculate optimal portfolios under the prior and posterior return distributions. optimalPortfolios works with a user-supplied optimization function, though simple Markowitz minimum-risk optimization is done with solve.QP from quadprog if none is supplied. optimalPortfolios.fPort is a generic utility function which calculates optimal portfolios using routines from the fPortfolio package.

Usage

optimalPortfolios(result, optimizer = .optimalWeights.simpleMV, ..., doPlot = TRUE, 
                  beside = TRUE)
optimalPortfolios.fPort(result, spec = NULL, constraints = "LongOnly", 
                        optimizer = "minriskPortfolio", inputData = NULL, 
                        numSimulations = BLCOPOptions("numSimulations")) 

Arguments

Details

By default, optimizer is a simple function that performs Markowitz optimization via solve.QP. In addition to a mean and variance, it takes an optional constraints parameter that if supplied should hold a named list with all of the parameters that solve.QP takes.

Value

optimalPortfolios will return a list with the following items:

optimalPortfolios.fPort will return a similar list with 2 elements of class fPORTFOLIO.

Note

It is expected that optimalPortfolios will be deprecated in future releases in favour of optimalPortfolios.fPort.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

References

Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.

Examples

	## Not run: 
	    entries <- c(0.001005,0.001328,-0.000579,-0.000675,0.000121,0.000128,
                    -0.000445, -0.000437, 0.001328,0.007277,-0.001307,-0.000610,
                    -0.002237,-0.000989,0.001442,-0.001535, -0.000579,-0.001307,
                    0.059852,0.027588,0.063497,0.023036,0.032967,0.048039,-0.000675,
                    -0.000610,0.027588,0.029609,0.026572,0.021465,0.020697,0.029854,
                    0.000121,-0.002237,0.063497,0.026572,0.102488,0.042744,0.039943,
                    0.065994 ,0.000128,-0.000989,0.023036,0.021465,0.042744,0.032056,
                    0.019881,0.032235 ,-0.000445,0.001442,0.032967,0.020697,0.039943,
                    0.019881,0.028355,0.035064 ,-0.000437,-0.001535,0.048039,0.029854,
                    0.065994,0.032235,0.035064,0.079958 )
	    
	    varcov <- matrix(entries, ncol = 8, nrow = 8)
	    mu <- c(0.08, 0.67,6.41, 4.08, 7.43, 3.70, 4.80, 6.60) / 100
	    pick <- matrix(0, ncol = 8, nrow = 3, dimnames = list(NULL, letters[1:8]))
	    pick[1,7] <- 1
	    pick[2,1] <- -1; pick[2,2] <- 1
	    pick[3, 3:6] <- c(0.9, -0.9, .1, -.1)
	    confidences <- 1 / c(0.00709, 0.000141, 0.000866)
	    views <- BLViews(pick, c(0.0525, 0.0025, 0.02), confidences, letters[1:8])
	    posterior <- posteriorEst(views, tau = 0.025, mu, varcov )
	    optimalPortfolios(posterior, doPlot = TRUE)
    
    	optimalPortfolios.fPort(posterior, optimizer = "tangencyPortfolio")
    	
    	# An example based on one found in "Beyond Black-Litterman:Views on Non-normal Markets"
        dispersion <- c(.376,.253,.360,.333,.360,.600,.397,.396,.578,.775) / 1000
        sigma <- BLCOP:::.symmetricMatrix(dispersion, dim = 4)
        caps <- rep(1/4, 4)
        mu <- 2.5 * sigma 
        dim(mu) <- NULL
        marketDistribution <- mvdistribution("mt", mean = mu, S = sigma, df = 5 )
        pick <- matrix(0, ncol = 4, nrow = 1, dimnames = list(NULL, c("SP", "FTSE", "CAC", "DAX")))
        pick[1,4] <- 1
        vdist <- list(distribution("unif", min = -0.02, max = 0))
    
        views <- COPViews(pick, vdist, 0.2, c("SP", "FTSE", "CAC", "DAX"))
        posterior <- COPPosterior(marketDistribution, views)
    
        optimalPortfolios.fPort(myPosterior, spec = NULL, optimizer = "minriskPortfolio", 
		                        inputData = NULL, numSimulations  = 100	)
	
    
## End(Not run)

This function performs the "core" calculation of the Black-Litterman model.

Description

This function performs the "core" calculation of the Black-Litterman model.

Usage

posteriorEst(views, mu, tau = 0.5, sigma, kappa = 0)

Arguments

Value

An object of class BLResult holding the updated Black-Litterman posterior

Author(s)

Francisco

Calculate the "feasibility" of the (Black-Litterman) posterior mean

Description

Attilio Meucci and Gianluca Fusai have suggested using the Mahalanobis distance to assess the feasibility of a set of Black-Litterman views. This function calculates this distance, along with a "feasibility" measure based on this distance and the sensitivity of the measure to changes in the "q" vector.

Usage

posteriorFeasibility(result)

Arguments

Details

The feasibility measure proposed by Meucci and Fusai (see the references below) is 1 - F(m), where m is the Mahalanobis distance from from the prior mean calculated with respect to the prior distribution. F is the chi-squared CDF of n-degrees of freedom, where n is the number assets in one's universe. It should be noted that in Meucci and Fusai's paper, a version of Black-Litterman is used in which the tau parameter is always set to 1.

Value

Warning

It is not clear that the results produced by this routine are entirely sensible, though the calculation is very straightforward and seems to match the one discussed in the source paper. Use with caution.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

References

Fusai, Gianluca and Meucci, Attilio. "Assessing Views", 2002. http://www.symmys.com/AttilioMeucci/Research/PublFinance/PublFinance.html

Examples

    pickMatrix <- matrix(c(rep(1/2, 2), -1,  rep(0, 3)), nrow = 1, ncol = 6 )
    views <- BLViews(P = pickMatrix, q = 0.08,confidences =  100, 
	                 assetNames = colnames(monthlyReturns))
    marketPosterior <- BLPosterior(monthlyReturns, views, marketIndex = sp500Returns, 
        riskFree = US13wTB)
    posteriorFeasibility(marketPosterior)

Execute the BLCOP unit tests

Description

Uses the RUnit package to execute a series of unit tests.

Usage

runBLCOPTests(testPath = BLCOPOptions("unitTestPath"), protocolFile = "BLCOPTests.html", 
    writeProtocol = FALSE) 

Arguments

Value

The summary of an object returned by RUnit's runTestSuite

Warning

These unit tests are in need of additional test cases, and should not be regarded as exhaustive in their current state.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Examples

## Not run: 
    runBLCOPTests()
    
## End(Not run)

Sample from a distribution object

Description

Generates samples from a distribution held by an object of class distribution or mvdistribution. Intended mainly for internal use.

Usage

sampleFrom(dstn, n = 1)

Arguments

Value

A vector or matrix of samples.

Author(s)

Francisco Gochez <fgochez@mango-solutions.com>

Examples

    x <- distribution("pois", lambda = 5)
    hist(sampleFrom(x, 1000), col = "blue", prob = TRUE)

S\&P500 Returns

Description

Monthly returns of the S&P 500 index for the period 2/2/1998 through 1/12/2003

Usage

sp500Returns

Format

A matrix with 1 column and 71 rows.

Examples

    ts.plot(sp500Returns)