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');