# Model Assumptions

From Open Risk Manual

## Definition

**Model Assumptions** denotes the large collection of explicitly stated (or implicit premised), conventions, choices and other specifications on which any Risk Model is based. The suitability of those assumptions is a major factor behind the Model Risk associated with a given model.

## Context

In the context of modelling economic, financial or other complex systems, model assumptions are necessary to simplify Model Development, or even make a model feasible / tractable.

## Examples

Assumptions can be conceptual, mathematical or numerical in nature

### Conceptual Assumptions

This class concerns idealizations and simplifications of the underlying entity or system that is being modeled

- Idealizing and simplifying the behavior of complex systems such as markets (e.g. via assumptions on liquidity)
- Simplifying the model of counterparties to legal contracts
- Simplifying the model of an economy by assuming away heterogeneity
- Injecting rational behavior assumptions

### Mathematical Assumptions

This class concerns assumptions around the mathematical representation of the modelled system

- Overall approach: e.g. Bayesian or Frequentist for statistical models
- Choice of univariate distributions among competing choices
- Implicity or explicit choices about multi-variate distributions and dependency
- Choice of parameter fitting / calibration approaches among competing choices

### Numerical Assumptions

- Explicit selection of numerical values

## Mitigation

- Proper Model Documentation is a primary mechanism for recognizing (identifying) and aiming to control the possibly adverse influence of assumptions.
- Model Validation, an independent review of modelling frameworks offers a second opinion.

## Issues and Challenges

- Undocumented / unrecognised model assumptions