TailCurve Basics

import chainladder as cl
import pandas as pd

This example demonstrates how the inverse_power curve generally produces more conservative tail factors than the exponential fit.

clrd = cl.load_sample("clrd").groupby("LOB").sum()["CumPaidLoss"]
cdf_ip = cl.TailCurve(curve="inverse_power").fit(clrd)
cdf_xp = cl.TailCurve(curve="exponential").fit(clrd)

result = pd.concat(
    (cdf_ip.tail_.rename("Inverse Power"), cdf_xp.tail_.rename("Exponential")), axis=1
)

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import matplotlib.pyplot as plt

plt.style.use("ggplot")
%config InlineBackend.figure_format = 'retina'

ax = result.plot(
    kind="bar", title="Curve Fit Comparison", xlabel="Industry", ylabel="Tail Factor"
);