# Which curve is better to approximate bond yields (python)

## I would like to approximate bond yields in python. But the question arose which curve describes this better?

import numpy as np
import matplotlib.pyplot as plt

x = [0.02, 0.22, 0.29, 0.38, 0.52, 0.55, 0.67, 0.68, 0.74, 0.83, 1.05, 1.06, 1.19, 1.26, 1.32, 1.37, 1.38, 1.46, 1.51, 1.61, 1.62, 1.66, 1.87, 1.93, 2.01, 2.09, 2.24, 2.26, 2.3, 2.33, 2.41, 2.44, 2.51, 2.53, 2.58, 2.64, 2.65, 2.76, 3.01, 3.17, 3.21, 3.24, 3.3, 3.42, 3.51, 3.67, 3.72, 3.74, 3.83, 3.84, 3.86, 3.95, 4.01, 4.02, 4.13, 4.28, 4.36, 4.4]
y = [3, 3.96, 4.21, 2.48, 4.77, 4.13, 4.74, 5.06, 4.73, 4.59, 4.79, 5.53, 6.14, 5.71, 5.96, 5.31, 5.38, 5.41, 4.79, 5.33, 5.86, 5.03, 5.35, 5.29, 7.41, 5.56, 5.48, 5.77, 5.52, 5.68, 5.76, 5.99, 5.61, 5.78, 5.79, 5.65, 5.57, 6.1, 5.87, 5.89, 5.75, 5.89, 6.1, 5.81, 6.05, 8.31, 5.84, 6.36, 5.21, 5.81, 7.88, 6.63, 6.39, 5.99, 5.86, 5.93, 6.29, 6.07]

a = np.polyfit(np.power(x,0.5), y, 1)
y1 = a[0]*np.power(x,0.5)+a[1]

b = np.polyfit(np.log(x), y, 1)
y2 = b[0]*np.log(x) + b[1]

c = np.polyfit(x, y, 2)
y3 = c[0] * np.power(x,2) + np.multiply(c[1], x) + c[2]

plt.plot(x, y, 'ro', lw = 3, color='black')
plt.plot(x, y1, 'g', lw = 3, color='red')
plt.plot(x, y2, 'g', lw = 3, color='green')
plt.plot(x, y3, 'g', lw = 3, color='blue')
plt.axis([0, 4.5, 2, 8])
plt.rcParams['figure.figsize'] = [10, 5]


The parabolic too goes down at the end (blue), the logarithmic goes too quickly to zero at the beginning (green), and the square root has a strange hump (red). Is there any other ways of more accurate approximation or is it that I'm already getting pretty good?

• To get a feel for your fit, you could plot the residuals. You could also calculate some error metrics to compare the lines. Jul 15, 2020 at 13:14

Now that you want a quantitative method for assessing the performance of the various fits, there are a few different routes you could take. It is important to highlight that these are competing criteria, sometimes contradictory, and at this point it is important to know what your priorities are. (Ironically although I have said it's time for quantitative methods, choosing which method is more of an art than a science). To name a few you could do: Which has the least squared error (minimal $$l^2$$-error), $$l^1$$-error, $$l^\infty$$-error? Which model has the best AIC/BIC? Which performs better on a cross validation? Which is nicer analytically/mathematically for the desired purpose?