TimeVaryingNelsonSiegelResult.hump_maturity

property TimeVaryingNelsonSiegelResult.hump_maturity: NDArray[float64]

Where the curvature loading peaks each date, 1.79 / lambda_t.

Example

>>> import numpy as np
>>> res = TimeVaryingNelsonSiegelResult(
...     panel=np.zeros((30, 4)), maturities=np.array([1.0, 2.0, 5.0, 10.0]),
...     mu=np.zeros(3), ar=np.full(3, 0.9), state_innovation_cov=np.eye(3),
...     measurement_var=np.full(4, 0.01), log_decay_mean=np.log(0.6),
...     decay_ar=0.9, decay_sd=0.1, factors=np.zeros((30, 3)),
...     factor_cov=np.tile(np.eye(3), (30, 1, 1)), log_decay=np.full(30, np.log(0.6)),
...     log_decay_std=np.full(30, 0.1), llf=0.0, filter="unscented", nobs=30,
...     n_params=19.0,
... )
>>> res.hump_maturity.shape, round(float(res.hump_maturity[0]), 2)
((30,), 2.99)