Introduction to Financial Modelling

Principles of Excel as a Modelling Tool

Chapter 2: Excel Foundations

8 Topics | 1 Quiz
Chapter 3: Using Sensitivity Analysis

10 Topics | 1 Quiz
Chapter 4: Core Operations and Shortcuts

7 Topics | 1 Quiz
Common Modeling Structures (I)

Chapter 5: Common Structures in Financial Models (I)

7 Topics | 2 Quizzes
Further Operations and Shortcuts

Chapter 7: Formatting, Presentation and Graphs

7 Topics | 1 Quiz
Excel Functions (I): Information and Numerical Aggregation

Excel Functions (II): Conditionality, Aggregations and Arrays

Chapter 11: Logic and Conditionality

7 Topics
Chapter 12: Conditional Aggregation

4 Topics | 1 Quiz
Chapter 13: Array Functions and Dynamic Arrays

5 Topics | 1 Quiz
Common Modelling Structures (II)

Excel Functions (II): Lookups and Referencing

Chapter 15: MATCH and XMATCH

5 Topics
Chapter 16: CHOOSE and SWITCH

6 Topics
Chapter 17: INDEX and XLOOKUPs

6 Topics | 1 Quiz
Model Planning and Best Practices

Chapter 18: Model Planning

11 Topics
CUTZ or ... intrducton to rest of program....and clisoing remarks

The following description is broken into the stages of concept, implementation, and conclusions.

From a conceptual point of view, a simple model for the volume of water could be:

- Assume that the earth is a perfect sphere (with a fixed radius or diameter).
- Multiply the surface area of the sphere by the percentage of the surface that is covered in ocean, to give the surface are of the ocean.
- Multiply this area by the average depth of the ocean to give the volume.

The core result of this model is that the estimated volume is approximately 1323 million cubic kilometers of ocean water. From the model as presented, it is difficult to draw further relevant conclusions, since we have not explained the context or need for this specific estimate sufficiently (e.g. in terms of decisions or its impact on climate models or measures to control or limit the effects of a changing climate).

However, in general, one can see that models (and modelling processes) can provide a framework to:

- Make estimates that are grounded in logic and use the available data.
- Work in a structured way, which exposes the assumptions used.
- Identify areas of uncertainty or lack of knowledge.
- Identify areas where a better understanding or better data could be used to enhance the accuracy of the results.
- Potentially generate insights or highlight issues that one may not have otherwise considered.

The additional comments below briefly discuss some of these points

There may be some uncertainty in the input values. Whilst the diameter of the earth and the proportion covered by ocean water may be known quite precisely, these values may not be exact. In particular, the average ocean depth is likely to be most uncertain: First, at the time of writing, the oceans having not been fully mapped (notably in parts of the southern hemisphere). Second, the calculation of the average is not straightforward: Each ocean is quite different in size and general form (the Pacific Ocean having an average of over 4km, whilst the Arctic Ocean is around 1km on average), and each has a sub-surface structure which is not level.

The following image shows the range of impact for the total estimated volume, as the assumption values are varied across a range of various published estimates for each parameter (that were obtained through simple internet research at the time of writing some of which may be more credible than others!):

When building any model, assumptions are made that are of an inherent or structural nature, and which are therefore intrinsic to the model.

For example, in the model being considered, one key assumption is that the world is spherical. Another assumption is that the ocean volume contains only water, as well as that ocean volume is constant over time (the volume could vary in accordance with the time of day as well as the seasons, due to temperature differences), and so on.

In principle, such issues can be dealt with by building more robust and detailed models, at least providing that the issues are identified and can be understood or that relevant data can be found or collected. For example, one could adapt the current model so that the surface area uses a formula for non-spherical shapes (e.g. ellipsoids), and so on: Whether this is worth doing will depend on the complexity and benefit of doing so.

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