Detone covariance

PortfolioOptimisers.DetoneCovariance Type

julia

struct DetoneCovariance{__T_ce, __T_dt, __T_pdm} <: AbstractCovarianceEstimator

A covariance estimator that applies a detoning algorithm and positive definite projection to the output of another covariance estimator. This type enables robust estimation of covariance matrices by first computing a base covariance, then applying detoning and positive definiteness corrections in sequence.

Fields

ce: Covariance estimator.
dt: Matrix detoning estimator.
pdm: Positive definite matrix estimator.

Constructors

julia

DetoneCovariance(;
    ce::StatsBase.CovarianceEstimator = Covariance(),
    dt::Detone = Detone(),
    pdm::Option{<:Posdef} = Posdef(),
) -> DetoneCovariance

Keywords correspond to the struct's fields.

Examples

julia

julia> DetoneCovariance()
DetoneCovariance
   ce ┼ Covariance
      │    me ┼ SimpleExpectedReturns
      │       │   w ┴ nothing
      │    ce ┼ GeneralCovariance
      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
      │       │    w ┴ nothing
      │   alg ┴ Full()
   dt ┼ Detone
      │   pdm ┼ Posdef
      │       │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
      │       │   kwargs ┴ @NamedTuple{}: NamedTuple()
      │     n ┴ Int64: 1
  pdm ┼ Posdef
      │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
      │   kwargs ┴ @NamedTuple{}: NamedTuple()

Related

source

PortfolioOptimisers.factory Method

julia

factory(ce::DetoneCovariance, w::ObsWeights) -> DetoneCovariance

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

Arguments

ce: Covariance estimator.
w: Observation weights vector observations × 1.

Returns

ce: New covariance estimator of the same type as the argument, with the new weights applied.

Examples

julia

julia> ce = DetoneCovariance();

julia> ce2 = factory(ce, StatsBase.Weights([0.2, 0.3, 0.5]));

julia> ce2.ce.me.w
3-element Weights{Float64, Float64, Vector{Float64}}:
 0.2
 0.3
 0.5

Related

source

Statistics.cov Method

julia

Statistics.cov(ce::DetoneCovariance, X::MatNum; dims = 1, kwargs...)

Compute the detoned and positive definite projected covariance matrix for the data matrix X using the specified DetoneCovariance estimator.

Arguments

ce: The DetoneCovariance estimator specifying the base covariance estimator, detoning algorithm, and positive definite projection.
X: The data matrix (observations × assets).
dims: Dimension along which to perform the computation.
kwargs...: Additional keyword arguments passed to the underlying covariance estimator.

Returns

sigma::MatNum: detoned covariance matrix.

Validation

dims in (1, 2).

Details

Computes the covariance matrix using the base estimator in ce.
Transposes X if dims == 2 to ensure variables are in columns.
Ensures the covariance matrix is mutable.
Applies positive definite projection using the method in ce.pdm.
Applies the detoning algorithm in ce.dt.
Returns the processed covariance matrix.

Related

source

Statistics.cor Method

julia

Statistics.cor(ce::DetoneCovariance, X::MatNum; dims = 1, kwargs...)

Compute the detoned and positive definite projected correlation matrix for the data matrix X using the specified DetoneCovariance estimator.

Arguments

ce: The DetoneCovariance estimator specifying the base covariance estimator, detoning algorithm, and positive definite projection.
X: The data matrix (observations × assets).
dims: Dimension along which to perform the computation.
kwargs...: Additional keyword arguments passed to the underlying correlation estimator.

Returns

rho::MatNum: detoned correlation matrix.

Validation

dims in (1, 2).

Details

Computes the correlation matrix using the base estimator in ce.
Transposes X if dims == 2 to ensure variables are in columns.
Ensures the correlation matrix is mutable.
Applies positive definite projection using the method in ce.pdm.
Applies the detoning algorithm in ce.dt.
Returns the processed correlation matrix.

Related

source

PortfolioOptimisers.port_opt_view Method

julia

port_opt_view(
    ce::DetoneCovariance,
    i,
    args...
) -> Union{DetoneCovariance{var"#s179", Detone{__T_pdm, __T_n}, Nothing} where {var"#s179"<:CovarianceEstimator, __T_pdm, __T_n}, DetoneCovariance{var"#s179", Detone{__T_pdm, __T_n}, Posdef{__T_alg, __T_kwargs}} where {var"#s179"<:CovarianceEstimator, __T_pdm, __T_n, __T_alg, __T_kwargs}}

Gets the view of the covariance estimator for the i-th element(s).

Arguments

ce: Covariance estimator.
i: Index or indices to view.

Returns

ce: New covariance estimator of the same type as the argument, for the new view.

Related

DetoneCovariance

source

Detone covariance ​

Detone covariance