Windowed Coskewness

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: Observation window. An integer selects the last window observations, and a vector of indices selects those observations.

Constructors

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:

• ske: Recursively updated via factory.
• w: Replaced with the incoming ObsWeights.

View parameters

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

• ske: Recursively viewed via port_opt_view.

Observation weight parameters

When obs_weights_view is called on this type, the following fields are automatically indexed to the selected observations:

• ske: Recursively indexed via obs_weights_view.
• w: Indexed to the selected observations via obs_weights_view.

Examples

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         │   cache ┴ nothing       w ┼ nothing  window ┴ nothing

Related

• CoskewnessEstimator
• Coskewness
• coskewness(ske::WindowedCoskewness, X::MatNum)
• factory
• port_opt_view
• obs_weights_view
• windowed_preamble

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.

Algorithm

1. Resolve the window and the observation weights with windowed_preamble, giving inner, a copy of ske.ske that carries the windowed weights, together with the windowed X and the windowed iv.
2. Call coskewness on inner and the windowed X, and return its result. The inner estimator alone decides the value, so the window and the weights are the whole of this method's contribution.

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 assets x assets².
• V::MatNum: Processed coskewness matrix assets x assets.

Related

• WindowedCoskewness
• windowed_preamble