Finish prediction risk and fix type hierarchy

Author: dcelisgarza

src/13_Prior/01_Base_Prior.jl

@@ -155,7 +155,7 @@ Abstract supertype for all prior result types.
 """
 abstract type AbstractPriorResult <: AbstractResult end
 const PrE_Pr = Union{<:AbstractPriorEstimator, <:AbstractPriorResult}
-const Pr_RR = Union{<:AbstractPriorResult, <:AbstractReturnsResult}
+const Pr_RR = Union{<:AbstractPriorResult, <:ReturnsResult}
 """
     prior(pr::AbstractPriorEstimator, rd::ReturnsResult; kwargs...)
 

src/19_RiskMeasures/01_Base_RiskMeasures.jl

@@ -527,9 +527,10 @@ function solver_selector(::Nothing, ::Nothing)
     throw(ArgumentError("Both risk_solver and prior_solver are nothing, cannot solve JuMP model."))
 end
 function expected_risk end
+function predicted_risk end
 function no_bounds_risk_measure end
 function no_bounds_no_risk_expr_risk_measure end
 
 export Frontier, RiskMeasureSettings, HierarchicalRiskMeasureSettings, SumScalariser,
-       MaxScalariser, MinScalariser, LogSumExpScalariser, expected_risk, RiskMeasure,
-       HierarchicalRiskMeasure
+       MaxScalariser, MinScalariser, LogSumExpScalariser, expected_risk, predicted_risk,
+       RiskMeasure, HierarchicalRiskMeasure

src/19_RiskMeasures/20_RatioRiskMeasure.jl

@@ -1,12 +1,12 @@
 struct RiskRatioRiskMeasure{T1, T2} <: HierarchicalRiskMeasure
     r1::T1
     r2::T2
-    function RiskRatioRiskMeasure(r1::AbstractBaseRiskMeasure, r2::AbstractBaseRiskMeasure)
+    function RiskRatioRiskMeasure(r1::OptimisationRiskMeasure, r2::OptimisationRiskMeasure)
         return new{typeof(r1), typeof(r2)}(r1, r2)
     end
 end
-function RiskRatioRiskMeasure(; r1::AbstractBaseRiskMeasure = Variance(),
-                              r2::AbstractBaseRiskMeasure = ConditionalValueatRisk())
+function RiskRatioRiskMeasure(; r1::OptimisationRiskMeasure = Variance(),
+                              r2::OptimisationRiskMeasure = ConditionalValueatRisk())
     return RiskRatioRiskMeasure(r1, r2)
 end
 function factory(r::RiskRatioRiskMeasure, pr::AbstractPriorResult, args...; kwargs...)
@@ -17,5 +17,27 @@ end
 function factory(r::RiskRatioRiskMeasure, w::VecNum)
     return RiskRatioRiskMeasure(; r1 = factory(r.r1, w), r2 = factory(r.r2, w))
 end
+struct NonOptimisationRiskRatioRiskMeasure{T1, T2} <: NonOptimisationRiskMeasure
+    r1::T1
+    r2::T2
+    function NonOptimisationRiskRatioRiskMeasure(r1::AbstractBaseRiskMeasure,
+                                                 r2::AbstractBaseRiskMeasure)
+        return new{typeof(r1), typeof(r2)}(r1, r2)
+    end
+end
+function NonOptimisationRiskRatioRiskMeasure(; r1::AbstractBaseRiskMeasure = Variance(),
+                                             r2::AbstractBaseRiskMeasure = ConditionalValueatRisk())
+    return NonOptimisationRiskRatioRiskMeasure(r1, r2)
+end
+function factory(r::NonOptimisationRiskRatioRiskMeasure, pr::AbstractPriorResult, args...;
+                 kwargs...)
+    r1 = factory(r.r1, pr, args...; kwargs...)
+    r2 = factory(r.r2, pr, args...; kwargs...)
+    return NonOptimisationRiskRatioRiskMeasure(; r1 = r1, r2 = r2)
+end
+function factory(r::NonOptimisationRiskRatioRiskMeasure, w::VecNum)
+    return NonOptimisationRiskRatioRiskMeasure(; r1 = factory(r.r1, w),
+                                               r2 = factory(r.r2, w))
+end
 
-export RiskRatioRiskMeasure
+export RiskRatioRiskMeasure, NonOptimisationRiskRatioRiskMeasure

src/19_RiskMeasures/25_ExpectedRisk.jl

@@ -1,4 +1,4 @@
-const MatNum_Pr = Union{<:MatNum, <:AbstractPriorResult}
+const MatNum_Pr = Union{<:MatNum, <:AbstractPriorResult, <:ReturnsResult}
 const ERkNetRet = Union{<:WorstRealisation, <:ValueatRisk, <:ValueatRiskRange,
                         <:ConditionalValueatRisk,
                         <:DistributionallyRobustConditionalValueatRisk,
@@ -20,14 +20,15 @@ const ERkNetRet = Union{<:WorstRealisation, <:ValueatRisk, <:ValueatRiskRange,
 const ERkwXFees = Union{<:LowOrderMoment, <:HighOrderMoment, <:TrackingRiskMeasure,
                         <:RiskTrackingRiskMeasure, <:Kurtosis, <:ThirdCentralMoment,
                         <:Skewness, <:MedianAbsoluteDeviation}
+const ERkX = Union{<:ERkNetRet, <:ERkwXFees}
 const ERkw = Union{<:StandardDeviation, <:NegativeSkewness, <:TurnoverRiskMeasure,
                    <:Variance, <:UncertaintySetVariance, <:EqualRiskMeasure}
 const TnTrRM = Union{<:TurnoverRiskMeasure, <:TrRM}
 const SlvRM = Union{<:EntropicValueatRisk, <:EntropicValueatRiskRange,
                     <:EntropicDrawdownatRisk, <:RelativeEntropicDrawdownatRisk,
                     <:RelativisticValueatRisk, <:RelativisticValueatRiskRange,
                     <:RelativisticDrawdownatRisk, <:RelativeRelativisticDrawdownatRisk}
-const PerfRM = Union{<:MeanReturn, <:RiskRatioRiskMeasure}
+const RkRatioRM = Union{<:RiskRatioRiskMeasure, <:NonOptimisationRiskRatioRiskMeasure}
 function expected_risk(r::ERkNetRet, w::VecNum, X::MatNum, fees::Option{<:Fees} = nothing;
                        kwargs...)
     return r(calc_net_returns(w, X, fees))
@@ -47,20 +48,32 @@ end
 function expected_risk(r::ERkw, w::VecNum, args...; kwargs...)
     return r(w)
 end
-function expected_risk(r::RiskRatioRiskMeasure, w::VecNum, X::MatNum,
-                       fees::Option{<:Fees} = nothing; kwargs...)
+function expected_risk(r::RkRatioRM, w::VecNum, X::MatNum, fees::Option{<:Fees} = nothing;
+                       kwargs...)
     return expected_risk(r.r1, w, X, fees; kwargs...) /
            expected_risk(r.r2, w, X, fees; kwargs...)
 end
-function expected_risk(r::RiskRatioRiskMeasure, w::VecNum,
-                       pr::Union{<:AbstractPriorResult <: AbstractReturnsResult},
-                       fees::Option{<:Fees} = nothing; kwargs...)
+function expected_risk(r::RkRatioRM, w::VecNum, pr::Pr_RR, fees::Option{<:Fees} = nothing;
+                       kwargs...)
     return expected_risk(r.r1, w, pr.X, fees; kwargs...) /
            expected_risk(r.r2, w, pr.X, fees; kwargs...)
 end
 function expected_risk(r::AbstractBaseRiskMeasure, w::VecVecNum, args...; kwargs...)
     return [expected_risk(r, wi, args...; kwargs...) for wi in w]
 end
+function predicted_risk(r::Union{<:ERkNetRet, <:ERkwXFees}, X::VecNum; kwargs...)
+    return r(X)
+end
+function predicted_risk(r::AbstractBaseRiskMeasure, X::VecVecNum; kwargs...)
+    return [r(Xi; kwargs...) for Xi in X]
+end
+function predicted_risk(r::RkRatioRM, X::VecNum; kwargs...)
+    return predicted_risk(r.r1, X; kwargs...) / predicted_risk(r.r2, X; kwargs...)
+end
+function predicted_risk(r::ERkw, args...; kwargs...)
+    throw(MethodError(predicted_risk,
+                      "cannot be computed for $r because the risk measure uses internal quantities and asset weights which are not defined for the result of a prediction of portfolio returns. For:\n- `StandardDeviation`: `LowOrderMoment(; alg = SecondMoment(; alg = Full(), alg2 = SOCRiskExpr())`.\n- Semi `StandardDeviation`: `LowOrderMoment(; alg = SecondMoment(; alg = Semi(), alg2 = SOCRiskExpr())`.\n- Variance: `LowOrderMoment(; alg = SecondMoment(; alg = Full(), alg2 = QuadRiskExpr())`, `alg2` can also be `SquaredSOCRiskExpr()` or `RSOCRiskExpr()`.\n- Semi Variance: `LowOrderMoment(; alg = SecondMoment(; alg = Semi(), alg2 = QuadRiskExpr())`, `alg2` can also be `SquaredSOCRiskExpr()` or `RSOCRiskExpr()`.\n- UncertaintySetVariance: use one of the variance recommendations.\n- NegativeSkewness: the closest is to use `Skewness`.\n- TurnoverRiskMeasure and EqualRiskMeasure: not applicable."))
+end
 function number_effective_assets(w::VecNum)
     return inv(LinearAlgebra.dot(w, w))
 end

src/21_ExpectedReturns.jl

@@ -376,6 +376,19 @@ function expected_risk(r::ReturnRiskMeasure, w::VecNum, pr::AbstractPriorResult,
                        fees::Option{<:Fees} = nothing; kwargs...)
     return expected_return(r.rt, w, pr, fees)
 end
+function predicted_risk(r::ReturnRiskMeasure{<:ArithmeticReturn}, X::AbstractVector;
+                        kwargs...)
+    return Statistics.mean(X)
+end
+function predicted_risk(r::ReturnRiskMeasure{<:LogarithmicReturn}, X::AbstractVector;
+                        kwargs...)
+    w = r.rt.w
+    return if isnothing(w)
+        Statistics.mean(log1p.(X))
+    else
+        Statistics.mean(log1p.(X), w)
+    end
+end
 """
     struct ReturnRiskRatioRiskMeasure{T1, T2, T3} <: NonOptimisationRiskMeasure
         rt::T1
@@ -606,6 +619,10 @@ function expected_risk(r::ReturnRiskRatioRiskMeasure, w::VecNum, pr::AbstractPri
                        fees::Option{<:Fees} = nothing; kwargs...)
     return expected_ratio(r.rk, r.rt, w, pr, fees; rf = r.rf, kwargs...)
 end
+function predicted_risk(r::ReturnRiskRatioRiskMeasure, X::AbstractVector; kwargs...)
+    return (predicted_risk(r.rt, X; kwargs...) - r.rf) / predicted_risk(r.rk, X; kwargs...)
+end
+const PerfRM = Union{<:MeanReturn, <:ReturnRiskMeasure, <:ReturnRiskRatioRiskMeasure}
 """
     brinson_attribution(X::TimeArray, w::VecNum, wb::VecNum,
                         asset_classes::DataFrame, col; date0 = nothing, date1 = nothing)

test/test_24_docs.jl

@@ -10,5 +10,5 @@
             push!(no_docs, sym)
         end
     end
-    @test length(no_docs) == 437
+    @test length(no_docs) == 439
 end