Python for Finance

Bond and Stock Valuation
We could manually calculate those missing values. First, a Δ is estimated:
……..(13)
Here, Δ is the incremental value between v2(the ending value) and v1 (the beginning
value), and n is the number of internals between those two values. The Δfor the
above case is (6-2)/3=1.33333. Thus, the next value will be v1+Δ=2+1.33333=3.33333.
For the preceding example, related to the term structure of interest rates, from years
6 to 9, there is no data. The code and output are shown here:
>>> import pandas as pd
>>> import numpy as np
>>> nan=np.nan
>>> x=pd.Series([2,nan,nan,nan,nan,2.53])
>>>x.interpolate()
The output is shown here:
>>>
0 2.000
1 2.106
2 2.212
3 2.318
4 2.424
5 2.530
dtype: float64
>>>
The term structure of interest rates is very important since it serves as a benchmark
to estimate Yield to Maturity (YTM) for corporate bonds. YTM is the period return
if the bond holder holds until the bond expires. Technically speaking, YTM is the
same as Internal Rate of Return (IRR). In the financial industry, the spread, defined
as the difference between YTM of a corporate bond over the risk-free rate, is used to
estimate the discount rate for corporate bonds. The spread is a measure of the default
risk. Thus, it should be closely correlated with the credit rating of the company and
of the bond.
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