Error message

Image resize threshold of 10 remote images has been reached. Please use fewer remote images.

20 Key Financial Modelling Definitions

By Rob Trippe, MBA, Financial Modelling Veteran

Financial model definitions can be tricky. Financial models are often dependent upon numerous functional areas and academic disciplines, such as accounting, finance and statistics. These disciplines may have differing uses of the same terminology. Model risk management has also drawn on numerous disciplines in its evolution. The result can be communicating at cross purposes.
No one academic discipline may lay claim to how a financial model’s terminology is defined. Financial model's output is often either a corporate finance concept or an accounting concept, while a driving calculation process may be statistical. Therefore, terminology should be defined among developers, owners and users as early as possible. A data dictionary may be a required element to financial models which fall under regulatory scrutiny. Add a definition of model terms dictionary too. This effort can be spear-headed by the data manager. When financial model output will be used in comparison to other figures, their definition, both numerically and non-numerically, should be identical. Non-narrative depiction of a definition can be extremely useful. Show builds and flows when possible. Here are twenty financial modelling definitions worth memorizing and employing:
1 Back-test 
Use of historic data as a test to model output validity.
2 Benchmark 
The comparison of model output to the output of an outside and independent source.
3 Emerging Risk
Unforeseeable risk arising further in time and model execution.
Set of rules for financial model design. Flexible, Appropriate, Structured and Transparent.
5 Impact Analysis
Assessment of cost, timing, scope and quality of a model - consequence.
6 In-Sample
Historical data used in model development.
7 Model
Quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates. Models provide an explanatory framework for real world observations.
8 Out of Sample
Historical data not used in model development.
9 Outcomes Analysis
The comparison of model output to actual outcomes. Back-testing is one example. 
10 Parameter
Numerical characteristic of a set or population of numbers.
11 Re-calibration
Adjustment of data and/or assumptions.
12 Residual Risk
Remaining risk after a risk mitigation action has been performed.
13 Risk Appetite
Largest tolerable degree of uncertainty acceptable.
14 Scenario
Multiple changes to inputs to reflect a given set of circumstances.
15 Secondary Risk
Risk arising from a risk response.
16 Sensitivity Analysis
Impact of a change to an input relative to the change in output.
17 Stress Test
Assessment of model stability by employing hypothetical data inputs or drivers.
18 Threshold
Measure of uncertainty or impact worthy of attention.
19 Tolerance
Degree of deviation within which a model still functions properly.
20 Validation
A set of processes and activities intended to verify that a model performs as intended and as expected.