Bondy Tail Sensitivity

import chainladder as cl

This example demonstrates the usage of the TailBondy estimator as well as passing multiple scoring functions to GridSearch. When the earliest_age is set to the last available in the Triangle, the estimator reverts to the traditional Bondy method.

# Fit basic development to a triangle
tri = cl.load_sample("tail_sample")["paid"]
dev = cl.Development(average="simple").fit_transform(tri)

# Return both the tail factor and the Bondy exponent in the scoring function
scoring = {
    "tail_factor": lambda x: x.tail_.values[0, 0],
    "bondy_exponent": lambda x: x.b_.values[0, 0],
}

# Vary the 'earliest_age' assumption in GridSearch
param_grid = dict(earliest_age=list(range(12, 120, 12)))
grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)
results = grid.fit(dev).results_

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import matplotlib.pyplot as plt

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

ax = results.plot(
    x="earliest_age",
    y="bondy_exponent",
    title="Bondy Assumption Sensitivity",
    marker="o",
)
results.plot(
    x="earliest_age", y="tail_factor", grid=True, secondary_y=True, ax=ax, marker="o"
);