Interpolation is a technique in finance, though sounds complex it correlates me to the game of joining the dots we use to play. But in a more mathematical manner of course..
This technique is most commonly used for financial analysis in my experience so far, this technique core context is to find the missing/intermediate points for the series of data.
For example:
| X Value | Y Value |
|---|---|
| 1 | 23 |
| 3 | 32 |
| 4 | 45 |
| 5 | 12 |
Question: What is the Y Value for the X Value of “2”?
This is the most common scenario for the usage of interpolation techniques. Now having mathematicians involved in the process of finding the methodology we can never have one way of doing things…
Thus, I ended up with the most commonly used techniques in the industry for the same which I have provided the implementation in the following workbook.
1. Linear
2. Log-Linear
3. Cubic
4. Cubic Spline
Feel free to explore the source code for the same; with this post I am not suggesting that I am a mathematician as all the logic has been pre-invented and written in multiple languages before, I am just trying to collate in one place and contrast between the same illustrating which methdology could suit better to your problem…
References:
