PortfolioOptimisers.jl

Appearance

13

Standard deviation expected returns ​

PortfolioOptimisers.StandardDeviationExpectedReturns Type
julia
struct StandardDeviationExpectedReturns{__T_ce} <: AbstractExpectedReturnsEstimator

Expected returns estimator that returns the asset standard deviations.

StandardDeviationExpectedReturns computes "expected returns" as the standard deviation of each asset, as estimated by the underlying covariance estimator. This can be useful in certain risk-based portfolio construction approaches where the expected return proxy is the asset's volatility.

Fields

  • ce: Covariance estimator.

Constructors

julia
StandardDeviationExpectedReturns(;
    ce::StatsBase.CovarianceEstimator = PortfolioOptimisersCovariance()
) -> StandardDeviationExpectedReturns

Keywords correspond to the struct's fields.

Examples

julia
julia> StandardDeviationExpectedReturns()
StandardDeviationExpectedReturns
  ce ┼ PortfolioOptimisersCovariance
     │   ce ┼ Covariance
     │      │    me ┼ SimpleExpectedReturns
     │      │       │   w ┴ nothing
     │      │    ce ┼ GeneralCovariance
     │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
     │      │       │    w ┴ nothing
     │      │   alg ┴ Full()
     │   mp ┼ DenoiseDetoneAlgMatrixProcessing
     │      │     pdm ┼ Posdef
     │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
     │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
     │      │      dn ┼ nothing
     │      │      dt ┼ nothing
     │      │     alg ┼ nothing
     │      │   order ┴ DenoiseDetoneAlg()

Related

source
PortfolioOptimisers.factory Method
julia
factory(ce::StandardDeviationExpectedReturns, w::ObsWeights) -> StandardDeviationExpectedReturns

Return a new StandardDeviationExpectedReturns estimator with observation weights w applied to the underlying covariance estimator.

Arguments

  • ce: Standard deviation expected returns estimator.

  • w: Observation weights vector observations × 1.

Returns

  • me::StandardDeviationExpectedReturns: Updated estimator with weights applied.

Related

source
Statistics.mean Method
julia
Statistics.mean(me::StandardDeviationExpectedReturns, X::MatNum;
                dims::Int = 1, kwargs...)

Compute expected returns as the standard deviation of each asset.

This method returns the standard deviation vector of X as estimated by the covariance estimator me.ce.

Arguments

  • me: Standard deviation expected returns estimator.

  • X: Data matrix of asset returns (observations × assets).

  • dims: Dimension along which to perform the computation.

  • kwargs...: Additional keyword arguments passed to the covariance estimator.

Returns

  • mu::Matrix{<:Number}: Standard deviation vector, shaped as (1, N) if dims == 1 or (N, 1) if dims == 2.

Related

source
PortfolioOptimisers.VarianceExpectedReturns Type
julia
struct VarianceExpectedReturns{__T_ce} <: AbstractExpectedReturnsEstimator

Expected returns estimator that returns the asset variances.

VarianceExpectedReturns computes "expected returns" as the variance of each asset, as estimated by the underlying covariance estimator. This can be useful in certain risk-based portfolio construction approaches where the expected return proxy is the asset's variance. Variance is the square of volatility (standard deviation).

Fields

  • ce: Covariance estimator.

Constructors

julia
VarianceExpectedReturns(;
    ce::StatsBase.CovarianceEstimator = PortfolioOptimisersCovariance()
) -> VarianceExpectedReturns

Keywords correspond to the struct's fields.

Examples

julia
julia> VarianceExpectedReturns()
VarianceExpectedReturns
  ce ┼ PortfolioOptimisersCovariance
     │   ce ┼ Covariance
     │      │    me ┼ SimpleExpectedReturns
     │      │       │   w ┴ nothing
     │      │    ce ┼ GeneralCovariance
     │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
     │      │       │    w ┴ nothing
     │      │   alg ┴ Full()
     │   mp ┼ DenoiseDetoneAlgMatrixProcessing
     │      │     pdm ┼ Posdef
     │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
     │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
     │      │      dn ┼ nothing
     │      │      dt ┼ nothing
     │      │     alg ┼ nothing
     │      │   order ┴ DenoiseDetoneAlg()

Related

source
PortfolioOptimisers.factory Method
julia
factory(ce::VarianceExpectedReturns, w::ObsWeights) -> VarianceExpectedReturns

Return a new VarianceExpectedReturns estimator with observation weights w applied to the underlying covariance estimator.

Arguments

  • ce: variance expected returns estimator.

  • w: Observation weights vector observations × 1.

Returns

  • me::VarianceExpectedReturns: Updated estimator with weights applied.

Related

source
Statistics.mean Method
julia
Statistics.mean(me::VarianceExpectedReturns, X::MatNum;
                dims::Int = 1, kwargs...)

Compute expected returns as the variance of each asset.

This method returns the variance vector of X as estimated by the covariance estimator me.ce.

Arguments

  • me: Variance expected returns estimator.

  • X: Data matrix of asset returns (observations × assets).

  • dims: Dimension along which to perform the computation.

  • kwargs...: Additional keyword arguments passed to the covariance estimator.

Returns

  • mu::Matrix{<:Number}: Variance vector, shaped as (1, N) if dims == 1 or (N, 1) if dims == 2.

Related

source