Actuarial modeling, built for Julia.

using FinanceModels

maturities = [0.5, 1.0, 1.5, 2.0]
rates = [5.0, 5.8, 6.4, 6.8] ./ 100
quotes = ZCBYield.(rates, maturities)

curve = fit(Spline.Linear(), quotes, Fit.Bootstrap())
discount(curve, 2.0)          # discount factor to year 2
zero(curve, 1.5)              # continuously compounded zero rate
forward(curve, 1.0, 2.0)      # rate between years 1 and 2

julia> using ActuaryUtilities

julia> cashflows = [5, 5, 105]
julia> discount_rate = 0.03

julia> round(present_value(discount_rate, cashflows); digits=2)
105.66

julia> round(duration(Macaulay(), discount_rate, cashflows); digits=2)
2.86

julia> round(duration(discount_rate, cashflows); digits=2)
2.78

julia> round(convexity(discount_rate, cashflows); digits=2)
10.63

using LifeContingencies
using MortalityTables
using FinanceCore

# load mortality rates from MortalityTables.jl
vbt2015 = MortalityTables.table("2015 VBT Smoker Distinct Male Non-Smoker ALB")

issue_age = 30
life = SingleLife(                       # The life underlying the risk
    mortality = vbt2015.select[issue_age],    # -- Mortality rates
)

yield = Periodic(0.05, 1)            # Using a flat 5% interest rate

lc = LifeContingency(life, yield)        # combine the life with the yield
ins = Insurance(lc)                      # alternate way to construct: Insurance(life, yield)

# Summary Scalars
present_value(ins)                       # The actuarial present value
premium_net(lc)                          # Net whole life premium
LifeContingencies.V(lc,5)                # Net premium reserve for whole life insurance at time 5