EconomicScenarioGenerators.jl

Help wanted!

This package is in Technical Preview Stage: The API is stabilizing and tests are passing but it has not been used in practice for very long. Please report any issues, provide feedback, and request specific features using the Discussions or Issues in this repository.

Interested in developing economic scenario generators in Julia? Consider contributing to this package. Open an issue, create a pull request, or discuss on the Julia Zulip's #actuary channel.

Usage

EconoicScenarioGenerators.jl is now available via the General Registry. Install and use in the normal way:

Add EconomicScenarioGenerators via Pkg
import EconomicScenarioGenerators or using EconomicScenarioGenerators in your code

Examples

Importing packages

First, import both EconomicScenarioGenerators and Yields:

using EconomicScenarioGenerators
using Yields

Models

Interest Rate Models

Vasicek
CoxIngersolRoss
HullWhite

EquityModels

BlackScholesMerton

Interest Rate Model Examples

Vasicek

m = Vasicek(0.136,0.0168,0.0119,Continuous(0.01)) # a, b, σ, initial Rate
s = ScenarioGenerator(
        1,  # timestep
        30, # projection horizon
        m,  # model
    )

This can be iterated over, or you can collect all of the rates like:

rates = collect(s)

for r in s
    # do something with r
end

And the package integrates with Yields.jl:

YieldCurve(s)

will produce a yield curve object:

              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Yield Curve (Yields.BootstrapCurve)⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀           
              ┌────────────────────────────────────────────────────────────┐           
         0.03 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠤⠔⠒⠉⠉⠒⠒⠒⠒⠒⠤⣄⣀│ Zero rates
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠒⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⠀⠀⣀⡤⠖⠊⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠖⠋⠁⠀⠀⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
   Continuous │⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⡤⠒⠓⠦⠤⠖⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⢰⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⣀⠖⠢⡀⡰⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠉⠉⠁⠀⠀⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
            0 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│           
              └────────────────────────────────────────────────────────────┘           
              ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀time⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀30⠀

CoxIngersolRoss

A CIR model:

m = CoxIngersollRoss(0.136,0.0168,0.0119,Continuous(0.01))

Hull White Model using a Yields.jl YieldCurve

Construct a yield curve and use that as the arbitrage-free forward curve within the Hull-White model.

using Yields, EconomicScenarioGenerators
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,.002,c) # a, σ, curve

s = ScenarioGenerator(
        1/12,  # timestep
        30., # projection horizon
        m,  # model
    )

Create 1000 yield curves from the scenario generator:

n = 1000
curves = [YieldCurve(s) for i in 1:n]
```julia

Plot the result:

```julia
using Plots

times = 1:30
p=plot(title="EconomicScenarioGenerators.jl Hull White Model")
for d in curves
    plot!(p,times,Yields.rate.(Yields.zero.(d,times)),alpha=0.2,label="")
end

plot!(times,Yields.rate.(Yields.zero.(c,times)),line=(:black, 5), label="Given Yield Curve")
p

Equity Model Examples

BlackScholesMerton

m = BlackScholesMerton(0.01,0.02,.15,100.)

s = ScenarioGenerator(
               1,  # timestep
               30, # projection horizon
               m,  # model
           )

Instantiate an array of the projection with collect(s).

Plotted BSM Example

Plot 100 paths:

using Plots
projections = [collect(s) for _ in 1:100]

p = plot()

for p in projections
    plot!(0:30,p,label="",alpha=0.5)
end

p

Correlated Scenarios

Combined with using Copulas, you can create correlated scenarios with a given copula. See ?Correlated for the docstring on creating a correlated set of scenario generators.

Example

Create two equity paths that are 90% correlated:

using EconomicScenarioGenerators, Copulas

m = BlackScholesMerton(0.01,0.02,.15,100.)
s = ScenarioGenerator(
                      1,  # timestep
                      30, # projection horizon
                      m,  # model
                  )

ss = [s,s] # these don't have to be the exact same, but do need same shape
g = ClaytonCopula(2,7) # highly dependendant model
c = Correlated(ss,g)

collect(c)

using Plots
plot(collect(c))

Benchmarks

Generating 10,000 scenarios of daily timesteps for 1 year (252 business days) with a Black-Scholes-Merton model:

library	multi-threaded?	pre-allocated arrays?	language	time (absolute)	time (relative)
EconomicScenarioGenerators	8x	Yes	Julia	5ms	1x
EconomicScenarioGenerators	8x	No	Julia	6ms	1x
EconomicScenarioGenerators	No	Yes	Julia	19ms	4x
EconomicScenarioGenerators	No	No	Julia	20ms	4x
pyesg	No?	No	Python	135ms	27x

Other ESG packages

Python
- https://github.com/jason-ash/pyesg