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5 - Semantics in Systemic Risk Management

from PART I - DATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK

Published online by Cambridge University Press:  05 June 2013

Mike Atkin
Affiliation:
Usa
Mike Bennett
Affiliation:
Uk
Jean-Pierre Fouque
Affiliation:
University of California, Santa Barbara
Joseph A. Langsam
Affiliation:
University of Maryland, College Park
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Summary

In order to fully understand and analyze systemic risk, it is first necessary to understand the system and its components. This means having a consistent representation of the ‘stuff’ of which the system is made up – the business entities, the contracts between them and the securities that are traded.

Classification is intimately bound up in semantics as we shall see. To create a formal representation of securities contracts for example, one must start by classifying them into different types.

In order to represent the system as a whole – the system within which systemic risk arises – there are two things which need to be represented formally: the components of the system; and the ways in which these inter-relate. Representing the components is relatively simple: this is a matter of formally representing financial instruments and the entities that issue them, trade them and hold positions in them. This is a prerequisite to modeling the overall system as a web of connections between and among those instruments and entities.

A common fallacy is to think that representing financial instruments is a matter of data. Because instruments have a lot of data about them, and because these are maintained in databases, it is frequently assumed that representing those instruments is a data issue, and that solutions to the problems of representing and understanding systemic risk must be technical solutions.

This is not the case. Technical solutions are exactly that – solutions to some problem. In order to develop the right technical solutions, one must first formally set out the problem to be solved.

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Publisher: Cambridge University Press
Print publication year: 2013

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References

The Enterprise Data Management Council, www.edmcouncil.org.
The Object Management Group www.omg.org.
Financial Industry Business Ontology (FIBO) via www.edmcouncil.org or www.omg.org.
Sowa, J.F. (2000). Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks/Cole.Google Scholar
McGuinness, D.L., van Harmelen, F. (eds). (2004). OWL Web Ontology Language Overview. W3C Recommendation, 10 February. http://www.w3.org/TR/ owl-features/
ISO 1087–1:2000 Terminology – Vocabulary – Part 1: Theory and application.
ISO 1087-2:2000 Terminology work – Vocabulary – Part 2: Computer applications Semantics of Business Vocabulary and Rules (SBVR) http://www.omg.org/spec/SBVR/Current/.
The Financial products Markup Language www.fpml.org.
eXtensible Business Reporting language (XBRL) www.xbrl.org.
McCarthy, W.E. (1982). The REA accounting model: a generalized framework for accounting systems in a shared data environment. The Accounting Review LVII (3) 554–578.Google Scholar
Peirce, C.S., Collected Papers of Charles Sanders Peirce, vols. 1–6, (19311935). Charles, Hartshorne and Paul, Weiss (eds). Vols. 7–8 (1958), Arthur W., Burks (ed). Harvard University Press.Google Scholar
ISO 10962:2011 Securities and related financial instruments – Classification of Financial Instruments (CFI code).
Zachman, J.A. (1987). A framework for information systems architecture. IBM Systems Journal 26 (3), 276.CrossRefGoogle Scholar

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