Ratio Risk Measure

PortfolioOptimisers.RiskRatio Type

struct RiskRatio{__T_r1, __T_r2} <: HierarchicalRiskMeasure

Represents a risk ratio risk measure for hierarchical portfolio optimisation.

RiskRatio computes the ratio of two risk measures, enabling the construction of risk-adjusted performance metrics for use in hierarchical optimisation routines.

Mathematical definition

$$\begin{array}{rl}\mathrm{RiskRatio}(\mathit{x})& =\frac{r_1(\mathit{x})}{r_2(\mathit{x})}.\end{array}$$

Where:

• $\mathrm{RiskRatio}(\mathit{x})$: Risk ratio of the portfolio.

• $\mathit{x}$: Portfolio returns vector $T\times 1$.

• $r_1$: First (numerator) optimisation risk measure.

• $r_2$: Second (denominator) optimisation risk measure.

Fields

• r1: First risk measure.

• r2: Second risk measure.

Constructors

RiskRatio(;
    r1::OptimisationRiskMeasure = Variance(),
    r2::OptimisationRiskMeasure = ConditionalValueatRisk()
) -> RiskRatio

Keywords correspond to the struct's fields.

Propagated parameters

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

• r1: Recursively updated via factory.

• r2: Recursively updated via factory.

Examples

julia> RiskRatio()
RiskRatio
  r1 ┼ Variance
     │   settings ┼ RiskMeasureSettings
     │            │   scale ┼ Float64: 1.0
     │            │      ub ┼ nothing
     │            │     rke ┴ Bool: true
     │      sigma ┼ nothing
     │       chol ┼ nothing
     │         rc ┼ nothing
     │        alg ┴ SquaredSOCRiskExpr()
  r2 ┼ ConditionalValueatRisk
     │   settings ┼ RiskMeasureSettings
     │            │   scale ┼ Float64: 1.0
     │            │      ub ┼ nothing
     │            │     rke ┴ Bool: true
     │      alpha ┼ Float64: 0.05
     │          w ┴ nothing

Related

• HierarchicalRiskMeasure

• OptimisationRiskMeasure

• NonOptimisationRiskRatio

source

PortfolioOptimisers.factory Method

factory(a::Union{Nothing, <:AbstractEstimator, <:AbstractAlgorithm,
                 <:AbstractResult}, args...; kwargs...) -> a

No-op factory function for constructing objects with a uniform interface.

Defining methods which dispatch on the first argument allows for a consistent factory interface across different types.

factory and port_opt_view are the two propagation mechanisms in this library. They are duals: factory threads runtime values (prior moments, observation weights, previous portfolio weights) down through a composed struct tree; port_opt_view threads an index selection (a subset of assets or observations) down through the same tree.

Arguments

• a: Indicates no object should be constructed.

• args...: Arbitrary positional arguments (ignored).

• kwargs...: Arbitrary keyword arguments (ignored).

Returns

• a: The input unchanged.

Examples

julia> factory(nothing, 1, 2; x = 3)

julia> factory(MeanValue())
MeanValue
  w ┴ nothing

Related

• port_opt_view

• AbstractEstimator

• AbstractAlgorithm

• AbstractResult

source

factory(
    rs::AbstractBaseRiskMeasure,
    args...;
    kwargs...
) -> VarianceSkewKurtosis{RiskMeasureSettings{__T_scale, __T_ub, __T_rke}, _A, Skewness{__T_settings, __T_ve, __T_sk, __T_w, __T_mu}} where {__T_scale, __T_ub, __T_rke, _A, __T_settings, __T_ve, __T_sk, __T_w, __T_mu}

Return the risk measure rs unchanged.

Identity pass-through used when a risk measure is provided in a context that calls factory.

Related

• AbstractBaseRiskMeasure

• factory

source

PortfolioOptimisers.factory Method

factory(a::Union{Nothing, <:AbstractEstimator, <:AbstractAlgorithm,
                 <:AbstractResult}, args...; kwargs...) -> a

No-op factory function for constructing objects with a uniform interface.

Defining methods which dispatch on the first argument allows for a consistent factory interface across different types.

factory and port_opt_view are the two propagation mechanisms in this library. They are duals: factory threads runtime values (prior moments, observation weights, previous portfolio weights) down through a composed struct tree; port_opt_view threads an index selection (a subset of assets or observations) down through the same tree.

Arguments

• a: Indicates no object should be constructed.

• args...: Arbitrary positional arguments (ignored).

• kwargs...: Arbitrary keyword arguments (ignored).

Returns

• a: The input unchanged.

Examples

julia> factory(nothing, 1, 2; x = 3)

julia> factory(MeanValue())
MeanValue
  w ┴ nothing

Related

• port_opt_view

• AbstractEstimator

• AbstractAlgorithm

• AbstractResult

source

factory(
    rs::AbstractBaseRiskMeasure,
    args...;
    kwargs...
) -> VarianceSkewKurtosis{RiskMeasureSettings{__T_scale, __T_ub, __T_rke}, _A, Skewness{__T_settings, __T_ve, __T_sk, __T_w, __T_mu}} where {__T_scale, __T_ub, __T_rke, _A, __T_settings, __T_ve, __T_sk, __T_w, __T_mu}

Return the risk measure rs unchanged.

Identity pass-through used when a risk measure is provided in a context that calls factory.

Related

• AbstractBaseRiskMeasure

• factory

source

PortfolioOptimisers.NonOptimisationRiskRatio Type

struct NonOptimisationRiskRatio{__T_r1, __T_r2} <: NonOptimisationRiskMeasure

Represents a non-optimisation risk ratio measure.

NonOptimisationRiskRatio computes the ratio of two risk measures for analysis or reporting purposes. Unlike RiskRatio, it is not intended for use as an objective or constraint in optimisation routines.

Mathematical definition

$$\begin{array}{rl}\mathrm{RiskRatio}(\mathit{x})& =\frac{r_1(\mathit{x})}{r_2(\mathit{x})}.\end{array}$$

Where:

• $\mathrm{RiskRatio}(\mathit{x})$: Risk ratio of the portfolio.

• $\mathit{x}$: Portfolio returns vector $T\times 1$.

• $r_1$: First (numerator) base risk measure.

• $r_2$: Second (denominator) base risk measure.

Fields

• r1: First risk measure.

• r2: Second risk measure.

Constructors

NonOptimisationRiskRatio(;
    r1::AbstractBaseRiskMeasure = Variance(),
    r2::AbstractBaseRiskMeasure = ConditionalValueatRisk()
) -> NonOptimisationRiskRatio

Keywords correspond to the struct's fields.

Propagated parameters

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

• r1: Recursively updated via factory.

• r2: Recursively updated via factory.

Related

• NonOptimisationRiskMeasure

• AbstractBaseRiskMeasure

• RiskRatio

source

PortfolioOptimisers.factory Method

factory(a::Union{Nothing, <:AbstractEstimator, <:AbstractAlgorithm,
                 <:AbstractResult}, args...; kwargs...) -> a

No-op factory function for constructing objects with a uniform interface.

Defining methods which dispatch on the first argument allows for a consistent factory interface across different types.

factory and port_opt_view are the two propagation mechanisms in this library. They are duals: factory threads runtime values (prior moments, observation weights, previous portfolio weights) down through a composed struct tree; port_opt_view threads an index selection (a subset of assets or observations) down through the same tree.

Arguments

• a: Indicates no object should be constructed.

• args...: Arbitrary positional arguments (ignored).

• kwargs...: Arbitrary keyword arguments (ignored).

Returns

• a: The input unchanged.

Examples

julia> factory(nothing, 1, 2; x = 3)

julia> factory(MeanValue())
MeanValue
  w ┴ nothing

Related

• port_opt_view

• AbstractEstimator

• AbstractAlgorithm

• AbstractResult

source

factory(
    rs::AbstractBaseRiskMeasure,
    args...;
    kwargs...
) -> VarianceSkewKurtosis{RiskMeasureSettings{__T_scale, __T_ub, __T_rke}, _A, Skewness{__T_settings, __T_ve, __T_sk, __T_w, __T_mu}} where {__T_scale, __T_ub, __T_rke, _A, __T_settings, __T_ve, __T_sk, __T_w, __T_mu}

Return the risk measure rs unchanged.

Identity pass-through used when a risk measure is provided in a context that calls factory.

Related

• AbstractBaseRiskMeasure

• factory

source