PortfolioClass: Portfolio Class

Description

A collection and description of functions allowing to gain information about optimal portfolios. Generally, optimization is done via three arguments, data, specification of the portfolio, and constraints, while function portfolioFrontier() has two additional arguments for title and description. Additionally to the optimization functions there are two sliders and a plot function available. The functions are: ll{ feasiblePortfolio Returns a feasible portfolio, cmlPortfolio returns the capital market line portfolio, tangencyPortfolio returns the tangency portfolio, minvariancePortfolio returns the minimum variance portfolio, efficientPortfolio returns an efficient portfolio, portfolioFrontier returns the efficient frontier, show print method for 'fPPORTFOLIO' objects, plot plot method for objects of class fPORTFOLIO, weightsSlider starts the weights slider, frontierSlider starts the efficient frontier Slider. }

Usage

portfolioFrontier(data, spec = portfolioSpec(), constraints = NULL, 
    title = NULL, description = NULL)
feasiblePortfolio(data, spec = portfolioSpec(), constraints = NULL)
cmlPortfolio(data, spec = portfolioSpec(), constraints = NULL)
tangencyPortfolio(data, spec = portfolioSpec(), constraints = NULL)
minvariancePortfolio(data, spec = portfolioSpec(), constraints = NULL)
efficientPortfolio(data, spec = portfolioSpec(), constraints = NULL)
show.fPORTFOLIO(object)
## S3 method for class 'fPORTFOLIO':
plot(x, which = "ask", control = list(), ...)
## S3 method for class 'fPORTFOLIO':
summary(object, \dots)
weightsSlider(object, control = list(), ...)     
frontierSlider(object, control = list(), ...)

Arguments

constraints

a character string vector, containing the constraints of the form "minW[asset]=percentage" for box constraints resp. "maxsumW[assets]=percentage" for sector constraints.

control

[plot, *Sliders] - a list, defining the plotting parameters. The list modifies amongst others the color, e.g. minvariance.col, type of point, e.g. tangency.pch, or the dimension of the point, e.g. cml.cex

data

a multivariate time series described by an S4 object of class
        timeSeries. If your timeSerie is not a timeSeries
        object, consult the generic function as.timeSeries to 
        convert your time series.

description

[portfolioFrontier] - 
a character string, allowing for a brief project description,
        by default NULL, i.e. Date and User.

object

[show, *Sliders] - 
an S4 object of class fPORTFOLIO, containing slots call, data,
         specification, constraints, portfolio, title, description, see Value 
         for a full slot description.

spec

an S4 object of class fPFOLIOSPEC, containing slots call, model,
        portfolio, title, description, see PortfolioSpec
        for a full slot description.

title

[portfolioFrontier] - 
a character string, containing the title for the object, by default NULL.

which

[plot] - 
which of the plots should be displayed? which can be either a 
        character string, "all" (displays all plots) or "ask" 
        (interactively asks which one to display), or a vector of integer

x

[plot] - 
an S4 object of class fPORTFOLIO.

...

[plot, *Sliders] - 
optional arguments to be passed.

`Value`

portfolioFrontier()
returns an S4 object of class "fPORTFOLIO", with the following 
    slots:
@calla call, returning the matched function call.
@dataa list with two named elements, series holding the time series
        data if available, otherwise NA, and statistics, itself a
        named list with two named elements mu and Sigma
        holding the vector of means and the matrix of covariances.
@descriptiona character string, allowing for a brief project description.
@portfolioa list, containing parameter specifications for the portfolio:
weights a numeric vector specifying the portfolio 
            weights,
targetReturn a numeric value specifying the target 
            return,
targetRisk a numeric value specifying the target 
            risk,
targetMean a numeric value specifying the target 
            return determinated with function mean(),
targetStdev a numeric value specifying the target risk in 
            standart deviation as risk measure.
@specificationa list with one named element spec which represents an object 
        of class fPFOLIOSPEC, including all information about
        the portfolio specifications, see PortfolioSpec
        for further details.
@titlea title string.
feasiblePortfolio
cmlPortfolio
tangencyPortfolio
minvariancePortfolio
efficientPortfolio
return an S4 object of class fPORTFOLIO having information only 
    about one portfolio.

`Details`

Portfolio Class:
    

This S4 class contains all information about the portfolio. Basically this
    are risk measure, mean and covariance estimation, target return, risk free
    rate, number of frontier points, ranges for calculation, see the "Value"
    section  for a detailed description of the slots. 
    
Portfolio Frontier:
    

The function portfolioFrontier calculates the whole efficient frontier.
    The portfolio information consists of five arguments: data, specifications,
    constraints, title and description. The data can be either the mean vector
    and the covariance matrix in a equivalently named list or a time series
    table. The specifications are done by the portfolioSpec function,
    see PortfolioSpec. The constrains have the following
    syntax. For bound constraints: "minW[asset]=percentage" and for
    sector constraints "maxsumW[assets]=percentage". The function chooses
    from this information the right algorithm to calculate the efficient frontier.
    The function returns an S4 object of class fPortfolio containing
    amongst others risk, return and weights of the whole efficient frontier,
    see Value for further details.
    
Individual Portfolios:
    

Individual portfolio functions allow a direct computation of portfolios 
    with specific properties. The functions are:
    
    
    ll{
    feasiblePortfolio 	a feasible portfolio, 
cmlPortfolio 	the capital market line portfolio, 
tangencyPortfolio 	the tangency portfolio, 
minvariancePortfolio 	the minimum variance portfolio, 
efficientPortfolio 	an efficient portfolio. } 
    
    The functions require three arguments: data, spec 
    (specifications), and constraints, see above. The functions 
    are named according to their specific property and return the result
    of the computed or optimized portfolio as an S4 object of class 
    fPortfolio.
    
    FeasiblePortfolio: The weights are retrieved from the 
    specification structure, by default they are undefined, NULL,
    and in this case the function feasiblePortfolio assumes
    equal weights. To set user specified weights in the specification 
    structure, use the function setWeights. 
    
    
    Portfolio Sliders:
    

The sliders have illustrative objectives. The functions expect an S4 object
    of class fPortfolio.
frontierSlider: With the frontier slider it is possible to slide 
    over the efficient frontier. Risk and return of the current portfolio is 
    plotted in the title. The frontier slider has several addable plot features 
    listed in the following. Little pies at the current portfolio showing the 
    weights (left pie) and/or the attributed weights (right pie), legend, 
    minimum variance portfolio, tangency portfolio, market portfolio with 
    capital market line (risk free rate is slidable), Sharpe ratio plot, equal 
    weights portfolio, single asset portfolios, two assets short selling 
    efficient frontier, Monte Carlo portfolios (number is sliderable in a 
    range from 0  to 25000).
    
weightsSlider: The weights slider gives an overview of the weights 
    on the efficient frontier. Three weight plots weightsPlot, 
    piePlot and the not stacked weights and a frontier plot with the
    single assets, the tangency portfolio and a legend are provided. In the 
    two weights plots the vertical line indicates the current portfolio and a 
    dotted one indicates the minimum variance portfolio. The number in the 
    pie plot stands for the asset and the sign shows whether this asset is 
    short or long. In all plots colors represent the same asset.

`See Also`

PortfolioData,
   PortfolioSpec,
   PortfolioConstraints,
   PortfolioPlots.

`Examples`

Run this code## minvariancePortfolio -
   # Load Data and Convert to timeSeries Object:
   Data = as.timeSeries(data(smallcap.ts))
   Data = Data[, c("BKE", "GG", "GYMB", "KRON")]
   Data
   # Set Default Specifications:
   Spec = portfolioSpec()
   Spec
   # Allow for unlimited Short Selling:
   Constraints = "Short"
   # Compute Short Selling Minimum Variance Portfolio
   minvariancePortfolio(Data, Spec, Constraints)
    
## portfolioFrontier -
   # Modify Constraints - Now Long Only Constraints:
   Constraint = c("minW[1:nAssets]=0")
   # Calculation of the Efficient Frontier
   frontier = portfolioFrontier(Data, Spec, Constraint)
   print(frontier)
    
## plot -
   # Plot Efficient Frontier with Minimum Variance Portfolio
   # plot(frontier, which = c(1, 3))
    
## frontierSlider -
   # Try Frontier Slider:
   # frontierSlider(frontier)

## weightsSlider -
   # Try Weights Slider:
   # weightsSlider(frontier)
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