PortfolioOptimisers.jl

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Windowed covariance ​

PortfolioOptimisers.WindowedCovariance Type
julia
struct WindowedCovariance{__T_ce, __T_w, __T_window} <: AbstractCovarianceEstimator

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

WindowedCovariance wraps another covariance 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 covariance estimation.

Fields

  • ce: Covariance 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
WindowedCovariance(;
    ce::StatsBase.CovarianceEstimator = PortfolioOptimisersCovariance(),
    w::Option{<:ObsWeights} = nothing,
    window::Option{<:Int_VecInt} = nothing
) -> WindowedCovariance

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.

Related

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

Return a new WindowedCovariance estimator with observation weights w applied to the underlying covariance estimator and stored as the windowed weights.

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.

Related

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Statistics.cov Method
julia
Statistics.cov(ce::WindowedCovariance, X::MatNum; dims::Int = 1, mean = nothing, iv::Option{<:MatNum} = nothing,
               kwargs...)

Compute the covariance matrix using a rolling or indexed observation window.

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

Arguments

  • ce: Windowed covariance 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

  • sigma::MatNum: Covariance matrix features x features.

Related

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Statistics.cor Method
julia
Statistics.cor(ce::WindowedCovariance, X::MatNum; dims::Int = 1, mean = nothing, iv::Option{<:MatNum} = nothing,
               kwargs...)

Compute the correlation matrix using a rolling or indexed observation window.

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

Arguments

  • ce: Windowed covariance 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

  • rho::MatNum: Correlation matrix features x features.

Related

source