VaR (Value-at-Risk) approaches-based-business risk models and management have
been very popular.Unfortunately , for technical and theoretical reasons, they
fall short on catching critical events with low-probability of occurrence or
phenomena under extreme uncertainty.These "matters of concerns" are precisely
the core business of Risk Managers ! Today, from my personal view, there are
three fast speed momentum gaining approaches for business Risk modelling:
1- IPMU(Information Processsing and Management under Uncertainty) from AI-like
blend of Artificial Neural Networks, Genetic Algorithms,Fuzzy Logic, Formal
Concept Analysis, Rough Sets and more. I may recommend e.g Sophia Langley's
paper "Representations of Risk" in Proceedings Seventh International Conference
IPMU,july 6-10,1998,Paris, la Sorbonne,pp.1068-1075.Several other valuable
models of uncertainty processing are inspirational for business risk modelling
(e.g Bouchon-Meunier, Akdag, Grabisch, Jaffray, Mellouli,...).
2- EVT (EXTREME VALUE THEORY) (Reznick, Davis, Deheuvels, Kluppelberg,
This foundational theory deals precisely with phenomena under extreme
uncertainty/scarcity with for example a potential highly "destructive"
outcome/impact. It shows for example that under appropriate conditions:
-number one: the distribution of extrema series converges asympotically to
the basic Frechet, Weitbull or Gumbel distributions.
-number two:the excess distribution over a given threshold converges to a
generalized Pareto distribution.
I strongly recommend the scientific journal " EXTREMES" to harness the power
and the limitations of this theory.
3- GIONICS ( a computational Intelligence approach to Uncertainty) (work under
completion progress). A Gion is a mathematical complex (non-necessarily real)
solution to the following equation:
X == X + 1 (Anais-Camilia-Aleea's gionic equation)
The derived mathematical ( algebraic, geometric and topological ) properties and
structures of the gion are intended to bring radical new fundamental scientific,
technological and metaphysical insights and foresights .They maybe the missing
mathematical framework of several "principles and postulates" in Mathematics (
e.g "the local change of global properties, structures, functionalities" to be
coined "transpolation"), Physics (a geometrical account of the Heisenberg's
undeterminacy) and in other fields.As a matter of fact, it seems to be the
missing mathematical foundation of the resounding and visionary DUKAS General
Theory of Evolution of any variable parameter ( financial, economic, social,...)
to be represented as the motion of a corresponding wave/particle ! Please visit
with a link to the Technical analysis sites ranking reported in the 06/01/2002
Financial Times. Such an approach is being put to work for business risk model
>Date: Wed, 30 Jan 2002 19:41:20 +0100 (MET)
>From: "Harris Georgiou" <firstname.lastname@example.org>
>To: Multiple recipients of list <email@example.com>
>Subject: Re: Business risk models
>X-Listprocessor-Version: 6.0c -- ListProcessor by Anastasios Kotsikonas
>X-Comment: Fuzzy Distribution List
>"Dave" <firstname.lastname@example.org> wrote in message
>> Hello group,
>> Could someone point me in the right direction.
>> Is fuzzy logic still dominent for business
>> risk or is another model more used today
>> (i.e. neural?)
>> Who is the best in programing these models?
>Actually, traditional risk management is based rather on analytical
>approaches like Game Theory rather than AI heuristics. However, any model
>that is constructed to deal with gain-oriented goals (minimum loss vs
>maximum gain), instead of simple success rate, can be easily applied in
>economics and business management. As the problem becomes more complex and
>includes more contractions in evidence (data), fuzzy rules/sets and
>non-linear clustering (like ANN) are the best choices.
>- 'Malo e lelei ki he pongipongi!'
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Zero is non null !
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