**Things to Think of in Model use – part 1 in a series of 2 blogs**

In today’s
financial industry widespread model use has been the standard for years. From
increased competition, cost cuttings, modernization and a general tougher
business climate including tighter regulations from authorities, also other
industries have increasingly been relying on models in their daily work. Over
the past few years there has perhaps been a slight shift back from maybe too
complicated to understand model outputs relying on sometimes non-intuitive
assumptions and to simply “pass the elevator test”. This blog shed some general
light on model use.

What is a model?

Models are an abstraction
and simplification of reality. They are next to never more complex than “the
real thing”. Models attempt to describe the reality, but are NOT the reality.
They should be understood as a guide to make decisions in an uncertain
environment. Never mix up a model with the world itself in other worlds. In
model use, it is thus essential to have a critical reflection about the assumptions
used in model and be aware of model risk

*I will remember that I did not make the world, and it does not satisfy my equations, though I will use model boldly to estimate values, I will not be overly impressed by mathematics I will never sacrifice reality for elegance without explaining why I have done so nor will I give the people who use my model false comfort about its accuracy*.

*Instead, I will make explicit its assumptions and oversights, I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension*

*Emmanuel Derman & Paul Wimott, 07 Jan 2009***A model should in general both offer:**

**Example of a model process:**

The key
question: among the myriad of influencing variables, what are the key
material and primary risk factors and how do they influence the use of the
model ?

**Effective use is an ongoing loop of recurring tasks:**

**A typical model lifecycle:**

… and
eventually: scrap or replacement of model.

Objectives in
the model lifecycle framework are an understanding of model risk sources and
how to measure these. The consequences of using the model need to be assessed
in light of the model risks, prior to use. Again: Never forget that model is
not reality but a conscious simplified version of reality. Simplification
consists in selecting key explanatory variables and identifying limits of the
model.

**Model design:**

The key
components of a model consist of input such as market data, key factors
definitions, methodological and statistical assumptions as well as IT feeding
processes. A calculation engine suited for the complexity and desired frequencies
of the calculations then can output valuation levels, principal component
analysis, probability distributions, mark-to-model vs mark-to-market values as
well as the expected level of uncertainty. There should be a repeating model
testing and reshaping process in place.

**Model risk:**

Different types
of model risk are typically data problems, model misspecifications and/or
inefficiency and wrong use (user risk).

**Data problems:**

For data
problems, key considerations are relevance vs obsolescence of data, data
corruption, data filtering i.e. signal vs noise, selection of appropriate data
to minimize selection bias, data availability vs costs (information cost vs
existence of proxies), appropriate feeding of the data in the calculation
engine an well as uncertainty of data.

**Model misspecification and inefficiency:**

Specification
and efficiency should be based on three key aspects: 1) appropriate assumptions
based on empirical behaviors, 2) selection of variables with identification of
correlations amongst them, 3) robustness over time of the explanatory power
(R2), both in-sample and out-of sample qualities.

**Wrong use:**

Models need to
be used for the right task otherwise its conclusions can be meaningless if it
is not the case (for example using an equity risk model to predict risk in a
fixed income portfolio). Further a review of the outputs and its analysis with
a critical view is crucial. How sensitive is the model to assumptions and how
accurate or correct are our inputted assumptions? The output should rather be a
range than an exact figure. And finally the model output is not the reality but
an estimate of reality should our assumptions hold.

*The information, views, and opinions expressed in this blog are solely those of the author in person and generally do not reflect the views and opinions of SKAGEN Funds.*

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