Since modeling and risk management must be viewed as a tool to improve business performance, data and modeling tools are required to support financial risk quantification and capital allocation. Market developments and enhanced regulations require new techniques in order to improve Asset and Liability Management..Despite the importance of risk evaluation, lack of reliable public information, the singular probabilistic behavior of the return (or the loss) of market and credit risks and the underlying nature of the business leads to a complexity that is difficult to handle without a combination of methods and techniques that could together give a systemic view of the problem. Based on a research over a 10 year data base, a methodology will be detailed to quantify financial risks based on the combination of methods and techniques such as parametric v@r, historical and monte carlo simulations, Bayesian inference and game theory. The aim of the paper is to put together the techniques and describe the usefulness of each one in order to develop a SD policy model that can use many insights and informations from them.

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Date created

• 2012 July 22-2012 July 26

Type

• Document

Processing Activity

• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

License

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

  
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ITEM CONTEXT

Part of

ab062956a014e30aaf02b44e9d33088e

Scope and Contents

Part of

23d738ba88f8333bc39725f9cb5bd0b8

Scope and Contents

Part of

b90fd81032b7dbf0c9f90f3dc3a33f1c

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Collection

System Dynamic Society Records

Scope and Contents

Collecting area

• This collection is part of the University Archives