fix tests and define YieldCurve for Correlated

JuliaActuary · Aug 8, 2022 · d35aad1 · d35aad1
1 parent 2922515
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diff --git a/src/EconomicScenarioGenerators.jl b/src/EconomicScenarioGenerators.jl
@@ -137,7 +137,7 @@ struct Correlated{T,U,R} <: AbstractScenarioGenerator
         new{T,U,R}(generators,copula,RNG)
     end
 end
-
+Base.Broadcast.broadcastable(x::T) where {T<:Correlated} = Ref(x)
 
 function Base.iterate(sgc::Correlated) 
     n = 1

diff --git a/src/Yields.jl b/src/Yields.jl
@@ -4,6 +4,16 @@ function YieldCurve(sg::ScenarioGenerator{N,T,R}) where {N,T<:InterestRateModel,
 
 end
 
+function YieldCurve(C::T) where {T<:Correlated}
+    timestep = first(C.sg).timestep
+    _, times = __zeros_times(first(C.sg))
+    _zeros(v) = cumsum(v .* timestep) ./ times
+    rates = collect(C)
+    zs = _zeros.(rates)
+    map(r->Yields.Zero(r,times),zs)
+
+end
+
 function __disc_rate_to_fwd(rate,time)
     log(rate) / -time
 end

diff --git a/test/Correlated.jl b/test/Correlated.jl
@@ -2,74 +2,82 @@
 @testset "Correlated" begin
     using EconomicScenarioGenerators, Copulas
 
-    m = BlackScholesMerton(0.01,0.02,.15,100.)
-    s = ScenarioGenerator(
-                        1,  # timestep
-                        30, # projection horizon
-                        m,  # model
-                    )
+    @testset "Equity" begin
+        @testset "BSM" begin
+            m = BlackScholesMerton(0.01, 0.02, 0.15, 100.0)
+            s = ScenarioGenerator(
+                1,  # timestep
+                30, # projection horizon
+                m,  # model
+            )
 
-    ss = [s,s] # these don't have to be the exact same
-    g = GaussianCopula([1. 0.9; 0.9 1.])
-    c = Correlated(ss,g,StableRNG(1))
+            ss = [s, s] # these don't have to be the exact same
+            g = GaussianCopula([1.0 0.9; 0.9 1.0])
+            c = Correlated(ss, g, StableRNG(1))
 
-    x = collect(c)
+            x = collect(c)
 
-    ρ = cor(hcat(ratio.(x)...))
-    @test ρ[1,2] ≈ 0.9 atol = 0.03
+            ρ = cor(hcat(ratio.(x)...))
+            @test ρ[1, 2] ≈ 0.9 atol = 0.03
+        end
+    end
 
     @testset "Interest" begin
         @testset "HullWhite" begin
-            rates =[0.01, 0.01, 0.03, 0.05, 0.07, 0.16, 0.35, 0.92, 1.40, 1.74, 2.31, 2.41] ./ 100
-            mats = [1/12, 2/12, 3/12, 6/12, 1, 2, 3, 5, 7, 10, 20, 30]
-            c = Yields.CMT(rates,mats)
+            rates = [0.01, 0.01, 0.03, 0.05, 0.07, 0.16, 0.35, 0.92, 1.40, 1.74, 2.31, 2.41] ./ 100
+            mats = [1 / 12, 2 / 12, 3 / 12, 6 / 12, 1, 2, 3, 5, 7, 10, 20, 30]
+            c = Yields.CMT(rates, mats)
 
-            m = HullWhite(.1,.001,c)
+            m = HullWhite(0.1, 0.01, c)
 
             s = ScenarioGenerator(
-                .1,                              # timestep
-                30.,                             # projection horizon
-                m
+                0.1,                              # timestep
+                30.0,                             # projection horizon
+                m,
+                StableRNG(1)
             )
 
-            g = GaussianCopula([1. 0.9; 0.9 1.])
-            c = Correlated(ss,g,StableRNG(1))
+            g = GaussianCopula([1.0 0.9; 0.9 1.0])
+            c = Correlated([s, s], g, StableRNG(1))
 
             x = collect(c)
 
-            ρ = cor(hcat(ratio.(x)...))
-            @test ρ[1,2] ≈ 0.9 atol = 0.03
+            ρ = cor(hcat(diff.(map(y->Yields.rate.(y),x))...))
+            @test ρ[1, 2] ≈ 0.9 atol = 0.03
         end
 
         @testset "Vasicek" begin
-            m = Vasicek(0.136,0.0168,0.0119,Yields.Continuous(0.01))
+            m = Vasicek(0.136, 0.0168, 0.0119, Yields.Continuous(0.01))
             s = ScenarioGenerator(
-                    1.,                              # timestep
-                    30.,                             # projection horizon
-                    m
-                )
-            g = GaussianCopula([1. 0.9; 0.9 1.])
-            c = Correlated(ss,g,StableRNG(1))
+                .1,                              # timestep
+                30.0,                             # projection horizon
+                m
+            )
+            g = GaussianCopula([1.0 0.9; 0.9 1.0])
+            c = Correlated([s, s], g, StableRNG(1))
 
             x = collect(c)
 
-            ρ = cor(hcat(ratio.(x)...))
-            @test ρ[1,2] ≈ 0.9 atol = 0.03
+            ρ = cor(hcat(diff.(map(y->Yields.rate.(y),x))...))
+            @test ρ[1, 2] ≈ 0.9 atol = 0.03
         end
         @testset "CIR" begin
-            m = CoxIngersollRoss(0.136,0.0168,0.0119,Yields.Continuous(0.01))
+            m = CoxIngersollRoss(0.136, 0.0168, 0.0119, Yields.Continuous(0.01))
             s = ScenarioGenerator(
-                    1.,                              # timestep
-                    30.,                             # projection horizon
-                    m
-                )
-            g = GaussianCopula([1. 0.9; 0.9 1.])
-            c = Correlated(ss,g,StableRNG(1))
+                .1,                              # timestep
+                30.0,                             # projection horizon
+                m
+            )
+            g = GaussianCopula([1.0 0.9; 0.9 1.0])
+            c = Correlated([s, s], g, StableRNG(1))
 
             x = collect(c)
 
-            ρ = cor(hcat(ratio.(x)...))
-            @test ρ[1,2] ≈ 0.9 atol = 0.03
+            ρ = cor(hcat(diff.(map(y->Yields.rate.(y),x))...))
+            @test ρ[1, 2] ≈ 0.9 atol = 0.03
+
+
+            @test first(YieldCurve.(c)) isa Yields.AbstractYield
         end
     end
 end