PortfolioOptimisers.jl

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Windowed Coskewness ​

PortfolioOptimisers.WindowedCoskewness Type
julia
struct WindowedCoskewness{__T_ske, __T_w, __T_window} <: CoskewnessEstimator

Coskewness estimator that restricts computation to a rolling or indexed observation window.

WindowedCoskewness wraps another coskewness estimator and applies it to a subset of observations defined by a window and/or custom observation weights. This enables time-varying or recency-weighted coskewness estimation.

Fields

  • ske: Coskewness estimator.

  • w: Optional observation weights vector observations × 1, or a concrete subtype of DynamicAbstractWeights. If nothing, the computation is unweighted.

  • window: Window specification: an integer (last window observations) or a vector of indices.

Constructors

julia
WindowedCoskewness(;
    ske::CoskewnessEstimator = Coskewness(),
    w::Option{<:ObsWeights} = nothing,
    window::Option{<:Int_VecInt} = nothing
) -> WindowedCoskewness

Keywords correspond to the struct's fields.

Validation

  • If w is not nothing, !isempty(w).

  • If window is provided, it must be nonempty, nonnegative, and finite.

Propagated parameters

When factory is called on this type, the following @fprop-tagged fields are automatically propagated:

View parameters

When port_opt_view is called on this type, the following @vprop-tagged fields are automatically subset to the selected indices:

Examples

julia
julia> WindowedCoskewness()
WindowedCoskewness
     ske ┼ Coskewness
         │    me ┼ SimpleExpectedReturns
         │       │   w ┴ nothing
         │    mp ┼ MatrixProcessing
         │       │     pdm ┼ Posdef
         │       │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
         │       │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
         │       │      dn ┼ nothing
         │       │      dt ┼ nothing
         │       │     alg ┼ nothing
         │       │   order ┴ NTuple{4, Symbol}: (:pdm, :dn, :dt, :alg)
         │   alg ┼ FullMoment()
         │     w ┴ nothing
       w ┼ nothing
  window ┴ nothing

Related

source
PortfolioOptimisers.coskewness Method
julia
coskewness(ske::WindowedCoskewness, X::MatNum; dims::Int = 1, mean = nothing, iv::Option{<:MatNum} = nothing, kwargs...)

Compute coskewness over a rolling or indexed observation window (matrix input).

This method selects a window of observations from X (and applies observation weights if specified), then delegates to the underlying coskewness estimator.

Arguments

  • ske: Windowed coskewness estimator.

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

  • dims: Dimension along which to perform the computation.

  • mean: Optional pre-computed mean passed to the underlying estimator.

  • iv: Optional implied volatility matrix. Used if any internal covariance estimator is an instance of ImpliedVolatility.

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

Returns

  • cskew::MatNum: Coskewness tensor features x features².

  • V::MatNum: Processed coskewness matrix features x features.

Related

source