def curve_params(current_duration, yield_path):
"""Calculate parameters for all 10 years"""
df_yield_params = pd.DataFrame(['NaN', 'NaN', 'NaN'], columns=['Current'])
for i in range(1,11):
df_yield_params[i] = np.nan
tau=1.65
param_list = []
for i in range(0, 11):
t = np.array(current_duration)
y = np.array(yield_path.iloc[:,i])
curve, status = betas_ns_ols(tau, t, y)
curve = str(curve)
B0 = float((curve.split('=')[1]).split(',')[0])
B1 = float((curve.split('=')[2]).split(',')[0])
B2 = float((curve.split('=')[3]).split(',')[0])
param = [B0, B1, B2]
df_yield_params.iloc[:,i] = param
return df_yield_params
def test_nelson_siegel_betas_recovery(self):
'''Test recovery of betas using ordinary least squares
(given fixed tau)
'''
t = np.linspace(0, 30)
y_target = self.y(t)
curve, lstsq_res = betas_ns_ols(self.y.tau, t, y_target)
self.assertAlmostEqual(self.y.beta0, curve.beta0, places=12)
self.assertAlmostEqual(self.y.beta1, curve.beta1, places=12)
self.assertAlmostEqual(self.y.beta2, curve.beta2, places=12)
# +
# Determine parameters for each year
df_yield_params = pd.DataFrame(['NaN', 'NaN', 'NaN'], columns=['Current'])
for i in range(1, 11):
df_yield_params[i] = np.nan
tau = 1.65
param_list = []
for i in range(0, 11):
t = np.array(df_interest_rates['Current_Duration'])
y = np.array(df_yield_10yr.iloc[:, i])
curve, status = betas_ns_ols(tau, t, y)
curve = str(curve)
B0 = float((curve.split('=')[1]).split(',')[0])
B1 = float((curve.split('=')[2]).split(',')[0])
B2 = float((curve.split('=')[3]).split(',')[0])
param = [B0, B1, B2]
df_yield_params.iloc[:, i] = param
# +
# US Yield Curve Over Next 10 Years
term_structure = (np.arange(0.5, 100.5, 0.5))
curve_list = []
for i in range(0, 11):
curve_item = [