Out[66]: 15.424277577732667
There is further potential to generalize the steps of the previous example. One option is to
define a new class that provides a method for calculating the NPV for different short rates
— i.e., a sensitivity analysis. We use, of course, the cash_flow_series class to inherit
from:
In [ 67 ]: class cfs_sensitivity(cash_flow_series):
def npv_sensitivity(self, short_rates):
npvs = []
for rate in short_rates:
sr.rate = rate
npvs.append(self.net_present_value())
return np.array(npvs)
In [ 68 ]: cfs_sens = cfs_sensitivity(‘cfs’, time_list, cash_flows, sr)
For example, defining a list containing different short rates, we can easily compare the
resulting NPVs:
In [ 69 ]: short_rates = [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.2]
In [ 70 ]: npvs = cfs_sens.npv_sensitivity(short_rates)
npvs
Out[70]: array([ 23.01739219, 20.10770244, 15.42427758, 10.94027255,
6.64667738, 2.53490386, -1.40323463, -8.78945889])
Figure 13-3 shows the result graphically. The thicker horizontal line (at 0) shows the
cutoff point between a profitable investment and one that should be dismissed given the
respective (short) rate:
In [ 71 ]: plt.plot(short_rates, npvs, ‘b’)
plt.plot(short_rates, npvs, ‘ro’)
plt.plot(( 0 , max(short_rates)), ( 0 , 0 ), ‘r’, lw= 2 )
plt.grid(True)
plt.xlabel(‘short rate’)
plt.ylabel(‘net present value’)
Figure 13-3. Net present values of cash flow list for different short rates
