

In [ ]:

    
%matplotlib inline

Value at Risk example

This example uses the BootstrapODPSample to simulate new triangles that are then used to simulate an IBNR distribution from which we can do Value at Risk percentile lookups.



In [ ]:

    
import chainladder as cl
import seaborn as sns
sns.set_style('whitegrid')

# Load triangle
triangle = cl.load_dataset('genins')

# Create 1000 bootstrap samples of the triangle
resampled_triangles = cl.BootstrapODPSample().fit_transform(triangle)

# Create 1000 IBNR estimates
sim_ibnr = cl.Chainladder().fit(resampled_triangles).ibnr_.sum('origin')

# X - mu
sim_ibnr = (sim_ibnr - sim_ibnr.mean()).to_frame().sort_values()

# Plot data
sim_ibnr.index = [item/1000 for item in range(1000)]
sim_ibnr.loc[0.90:].plot(
    title='Bootstrap VaR (90% and above)', color='red').set(xlabel='VaR');