Hierarchical Equal Risk Contribution

PortfolioOptimisers.HierarchicalEqualRiskContribution Type

struct HierarchicalEqualRiskContribution{__T_opt, __T_ri, __T_ro, __T_scai, __T_scao, __T_ex, __T_fb} <: ClusteringOptimisationEstimator

Hierarchical Equal Risk Contribution (HERC) portfolio optimiser.

HierarchicalEqualRiskContribution implements the Hierarchical Equal Risk Contribution algorithm. It clusters assets, then allocates weights so that each cluster contributes equally to total portfolio risk (using ro), and within each cluster, assets are weighted by inverse intra-cluster risk (using ri).

Fields

• opt: Base hierarchical optimiser configuration.

• ri: Intra-cluster risk measure or vector of risk measures.

• ro: Inter-cluster risk measure or vector of risk measures.

• scai: Scalariser for combining intra-cluster risk measures.

• scao: Scalariser for combining inter-cluster risk measures.

• ex: FLoops executor for parallelism.

• fb: Fallback optimiser.

Constructors

HierarchicalEqualRiskContribution(;
    opt::HierarchicalOptimiser = HierarchicalOptimiser(),
    ri::OptRM_VecOptRM = Variance(),
    ro::OptRM_VecOptRM = ri,
    scai::Scalariser = SumScalariser(),
    scao::Scalariser = scai,
    ex::FLoops.Transducers.Executor = FLoops.ThreadedEx(),
    fb::Option{<:OptE_Opt} = nothing
) -> HierarchicalEqualRiskContribution

Keywords correspond to the struct's fields.

Validation

• If ri or ro is a vector: !isempty(ri) / !isempty(ro).

Examples

julia> HierarchicalEqualRiskContribution()
HierarchicalEqualRiskContribution
   opt ┼ HierarchicalOptimiser
       │       pe ┼ EmpiricalPrior
       │          │        ce ┼ PortfolioOptimisersCovariance
       │          │           │   ce ┼ Covariance
       │          │           │      │    me ┼ SimpleExpectedReturns
       │          │           │      │       │   w ┴ nothing
       │          │           │      │    ce ┼ GeneralCovariance
       │          │           │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
       │          │           │      │       │    w ┴ nothing
       │          │           │      │   alg ┴ Full()
       │          │           │   mp ┼ DenoiseDetoneAlgMatrixProcessing
       │          │           │      │     pdm ┼ Posdef
       │          │           │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
       │          │           │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
       │          │           │      │      dn ┼ nothing
       │          │           │      │      dt ┼ nothing
       │          │           │      │     alg ┼ nothing
       │          │           │      │   order ┴ DenoiseDetoneAlg()
       │          │        me ┼ SimpleExpectedReturns
       │          │           │   w ┴ nothing
       │          │   horizon ┴ nothing
       │      cle ┼ ClustersEstimator
       │          │    ce ┼ PortfolioOptimisersCovariance
       │          │       │   ce ┼ Covariance
       │          │       │      │    me ┼ SimpleExpectedReturns
       │          │       │      │       │   w ┴ nothing
       │          │       │      │    ce ┼ GeneralCovariance
       │          │       │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
       │          │       │      │       │    w ┴ nothing
       │          │       │      │   alg ┴ Full()
       │          │       │   mp ┼ DenoiseDetoneAlgMatrixProcessing
       │          │       │      │     pdm ┼ Posdef
       │          │       │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
       │          │       │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
       │          │       │      │      dn ┼ nothing
       │          │       │      │      dt ┼ nothing
       │          │       │      │     alg ┼ nothing
       │          │       │      │   order ┴ DenoiseDetoneAlg()
       │          │    de ┼ Distance
       │          │       │   power ┼ nothing
       │          │       │     alg ┴ CanonicalDistance()
       │          │   alg ┼ HClustAlgorithm
       │          │       │   linkage ┴ Symbol: :ward
       │          │   onc ┼ OptimalNumberClusters
       │          │       │   max_k ┼ nothing
       │          │       │     alg ┼ SecondOrderDifference
       │          │       │         │   alg ┼ StandardisedValue
       │          │       │         │       │   mv ┼ MeanValue
       │          │       │         │       │      │   w ┴ nothing
       │          │       │         │       │   sv ┼ StdValue
       │          │       │         │       │      │           w ┼ nothing
       │          │       │         │       │      │   corrected ┴ Bool: true
       │      slv ┼ nothing
       │       wb ┼ WeightBounds
       │          │   lb ┼ Float64: 0.0
       │          │   ub ┴ Float64: 1.0
       │     fees ┼ nothing
       │     sets ┼ nothing
       │       wf ┼ IterativeWeightFinaliser
       │          │   iter ┴ Int64: 100
       │      brt ┼ Bool: false
       │   cle_pr ┼ Bool: true
       │   strict ┴ Bool: false
    ri ┼ Variance
       │   settings ┼ RiskMeasureSettings
       │            │   scale ┼ Float64: 1.0
       │            │      ub ┼ nothing
       │            │     rke ┴ Bool: true
       │      sigma ┼ nothing
       │       chol ┼ nothing
       │         rc ┼ nothing
       │        alg ┴ SquaredSOCRiskExpr()
    ro ┼ Variance
       │   settings ┼ RiskMeasureSettings
       │            │   scale ┼ Float64: 1.0
       │            │      ub ┼ nothing
       │            │     rke ┴ Bool: true
       │      sigma ┼ nothing
       │       chol ┼ nothing
       │         rc ┼ nothing
       │        alg ┴ SquaredSOCRiskExpr()
  scai ┼ SumScalariser()
  scao ┼ SumScalariser()
    ex ┼ Transducers.ThreadedEx{@NamedTuple{}}: Transducers.ThreadedEx()
    fb ┴ nothing

Related

• ClusteringOptimisationEstimator

• HierarchicalRiskParity

• SchurComplementHierarchicalRiskParity

• HierarchicalOptimiser

source

PortfolioOptimisers.optimise Function

optimise(hec::HierarchicalEqualRiskContribution{
                 <:Any, <:Any, <:Any, <:Any, <:Any, <:Any, Nothing
             },
        rd::ReturnsResult = ReturnsResult(); dims::Int = 1,
        branchorder::Symbol = :optimal, kwargs...) -> HierarchicalResult

Arguments

• hec: The hierarchical equal risk contribution optimiser to use.

• rd: The returns result to use. If isa(hec.opt.pe, AbstractPriorResult), rd is not necessary if doing a standalone optimisation, but may be required/desired by fallbacks and/or clusterisation.

• dims: The dimension along which observations advance in time.

• branchorder: The branch order to use for the clusterisation, this optimisation can use non-optimal branch orders, which make the clustering faster but the dendrogram won't be as nice.

• kwargs: Additional keyword arguments passed to the optimisation function.

source