PortfolioOptimisers.jl

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Negative Skewness Constraints ​

PortfolioOptimisers.get_chol_or_V_pm Function
julia
get_chol_or_V_pm(model::Model, pr::HighOrderPrior) -> Any

Retrieve or compute and cache the square-root matrix of the co-skewness matrix V.

If model does not yet contain GV, attempts a Cholesky factorisation of pr.V and falls back to sqrt(pr.V) for positive-semidefinite matrices. Stores the result as model[:GV].

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • pr::HighOrderPrior: High-order prior containing V.

Returns

  • GV::Matrix: Square-root factor of the co-skewness matrix.

Related

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PortfolioOptimisers.set_negative_skewness_risk! Function
julia
set_negative_skewness_risk!(
    model::Model,
    r::NegativeSkewness{<:Any, <:Any, <:Any, <:Any, <:SOCRiskExpr},
    opt::RiskJuMPOptimisationEstimator,
    nskew_risk::AbstractJuMPScalar,
    key::Symbol,
    args...
) -> AbstractJuMPScalar

Finalise the negative-skewness risk expression and apply bounds according to the formulation.

The SOCRiskExpr overload passes the SOC variable directly to set_risk_bounds_and_expression!. The SquaredSOCRiskExpr overload squares it. The QuadRiskExpr overload encodes skewness as the quadratic form w' * V * w.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • r::NegativeSkewness: Negative-skewness risk measure instance.

  • opt::RiskJuMPOptimisationEstimator: Risk-based optimisation estimator.

  • nskew_risk: SOC variable for negative-skewness risk.

  • key::Symbol: Symbol used to name constraints or expressions in the model.

  • V::MatNum: Co-skewness matrix (used only by the Quad overload).

Returns

  • The negative-skewness risk JuMP expression.

Related

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PortfolioOptimisers.set_risk_constraints! Method
julia
set_risk_constraints!(
    model::Model,
    i,
    r::NegativeSkewness,
    opt::RiskJuMPOptimisationEstimator,
    pr::HighOrderPrior,
    args...;
    kwargs...
) -> Any

Add negative-skewness risk constraints to model.

The HighOrderPrior overload selects the co-skewness matrix (from r.V or pr.V), creates a scalar variable, adds the SOC constraint [sc * nskew_risk; sc * G * w] in SOC, and dispatches to set_negative_skewness_risk! for bounding. The LowOrderPrior overload unconditionally throws an ArgumentError.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • i: Constraint index for unique variable and constraint naming.

  • r::NegativeSkewness: Negative-skewness risk measure instance.

  • opt::RiskJuMPOptimisationEstimator: Risk-based optimisation estimator.

  • pr::HighOrderPrior: High-order prior containing V.

Returns

  • nothing.

Related

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PortfolioOptimisers.set_risk_constraints! Method
julia
set_risk_constraints!(
    ::Model,
    ,
    ::NegativeSkewness,
    ::RiskJuMPOptimisationEstimator,
    pr::LowOrderPrior,
    args...;
    kwargs...
)

Throw an ArgumentError indicating that NegativeSkewness requires a HighOrderPrior.

This fall-through overload is triggered when a LowOrderPrior is passed and always raises an error.

Arguments

  • r::NegativeSkewness: The negative skewness risk measure (unused).

  • pr::LowOrderPrior: A low-order prior (not compatible with negative skewness).

Returns

  • Does not return; always throws ArgumentError.

Related

source