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A proximity based macro stress testing framework


Wälchli, Boris (2016). A proximity based macro stress testing framework. Dependence Modeling, 4(1):251-276.

Abstract

In this a paper a non-linear macro stress testing methodology with focus on early warning is developed. The methodology builds on a variant of Random Forests and its proximity measures. It is embedded in a framework, in which naturally defined contagion and feedback effects transfer the impact of stressing a relatively small part of the observations on the whole dataset, allowing to estimate a stressed future state. It will be shown that contagion can be directly derived from the proximities while iterating the proximity based contagion leads to naturally defined feedback effects. Since the methodology is Random Forests based the framework can be estimated on large numbers of risk indicators up to big data dimensions, fostering the stability of the results while reducing inaccuracies in estimated stress scenarios by only stressing a small part of the observations. This procedure allows accurate forecasting of events under stress and the emergence of a potential macro crisis. The framework also estimates a set of the most influential economic indicators leading to the potential crisis, which can then be used as indications of remediation or prevention.

Abstract

In this a paper a non-linear macro stress testing methodology with focus on early warning is developed. The methodology builds on a variant of Random Forests and its proximity measures. It is embedded in a framework, in which naturally defined contagion and feedback effects transfer the impact of stressing a relatively small part of the observations on the whole dataset, allowing to estimate a stressed future state. It will be shown that contagion can be directly derived from the proximities while iterating the proximity based contagion leads to naturally defined feedback effects. Since the methodology is Random Forests based the framework can be estimated on large numbers of risk indicators up to big data dimensions, fostering the stability of the results while reducing inaccuracies in estimated stress scenarios by only stressing a small part of the observations. This procedure allows accurate forecasting of events under stress and the emergence of a potential macro crisis. The framework also estimates a set of the most influential economic indicators leading to the potential crisis, which can then be used as indications of remediation or prevention.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Physical Sciences > Modeling and Simulation
Physical Sciences > Applied Mathematics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:November 2016
Deposited On:02 Feb 2017 08:44
Last Modified:17 May 2024 03:48
Publisher:De Gruyter Open
ISSN:2300-2298
OA Status:Gold
Publisher DOI:https://doi.org/10.1515/demo-2016-0015
Related URLs:https://www.degruyter.com/view/j/demo.2016.4.issue-1/demo-2016-0015/demo-2016-0015.xml (Publisher)
Other Identification Number:merlin-id:14483
  • Content: Published Version
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)