add.objective {PortfolioAnalytics}

General interface for adding optimization objectives, including risk, return, and risk budget

Description

This function is the main function for adding and updating business objectives in an object of type portfolio.spec.

Usage

add.objective_v1(
  constraints,
  type,
  name,
  arguments = NULL,
  enabled = TRUE,
  ...,
  indexnum = NULL
)

add.objective(
  portfolio,
  constraints = NULL,
  type,
  name,
  arguments = NULL,
  enabled = TRUE,
  ...,
  indexnum = NULL
)

Arguments

constraints

a 'v1_constraint' object for backwards compatibility, see constraint

type

character type of the objective to add or update, currently 'return','risk', 'risk_budget', 'quadratic_utility', or 'weight_concentration'

name

name of the objective, should correspond to a function, though we will try to make allowances

arguments

default arguments to be passed to an objective function when executed

enabled

TRUE/FALSE

...

any other passthru parameters

indexnum

if you are updating a specific objective, the index number in the $objectives list to update

portfolio

an object of type 'portfolio' to add the objective to, specifying the portfolio for the optimization, see portfolio

Details

In general, you will define your objective as one of the following types: 'return', 'risk', 'risk_budget', 'quadratic utility', or 'weight_concentration'. These have special handling and intelligent defaults for dealing with the function most likely to be used as objectives, including mean, median, VaR, ES, etc.

Objectives of type 'turnover' and 'minmax' are also supported.

Author(s)

Brian G. Peterson and Ross Bennett

See Also

objective, portfolio.spec

Examples

data(edhec)
returns <- edhec[,1:4]
fund.names <- colnames(returns)
portf <- portfolio.spec(assets=fund.names)
# Add some basic constraints
portf <- add.constraint(portf, type="full_investment")
portf <- add.constraint(portf, type="long_only")

# Creates a new portfolio object using portf and adds a quadratic utility
# objective. This will add two objectives to the portfolio object; 1) mean and
# 2) var. The risk aversion parameter is commonly referred to as lambda in the
# quadratic utility formulation that controls how much the portfolio variance 
# is penalized.
portf.maxQU <- add.objective(portf, type="quadratic_utility", 
                             risk_aversion=0.25)

# Creates a new portfolio object using portf and adds mean as an objective
portf.maxMean <- add.objective(portf, type="return", name="mean")

# Creates a new portfolio object using portf and adds StdDev as an objective
portf.minStdDev <- add.objective(portf, type="risk", name="StdDev")

# Creates a new portfolio object using portf and adds ES as an objective. 
# Note that arguments to ES are passed in as a named list.
portf.minES <- add.objective(portf, type="risk", name="ES", 
                             arguments=list(p=0.925, clean="boudt"))

# Creates a new portfolio object using portf.minES and adds a risk budget 
# objective with limits on component risk contribution. 
# Note that arguments to ES are passed in as a named list.
portf.RiskBudgetES <- add.objective(portf.minES, type="risk_budget", name="ES", 
                             arguments=list(p=0.925, clean="boudt"),
                             min_prisk=0, max_prisk=0.6)

# Creates a new portfolio object using portf.minES and adds a risk budget 
# objective with equal component risk contribution. 
# Note that arguments to ES are passed in as a named list.
portf.EqRiskES <- add.objective(portf.minES, type="risk_budget", name="ES", 
                                    arguments=list(p=0.925, clean="boudt"),
                                    min_concentration=TRUE)

# Creates a new portfolio object using portf and adds a weight_concentration 
# objective. The conc_aversion parameter controls how much concentration is
# penalized. The portfolio concentration is defined as the Herfindahl Hirschman
# Index of the weights.
portf.conc <- add.objective(portf, type="weight_concentration", 
                            name="HHI", conc_aversion=0.01)