A proximity based macro stress testing framework

• Autorzy: Boris Waelchli
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Random Forests; Machine Learning; Stress Testing; Early Warning Indicators; Big Data

• Wydawca: De Gruyter Open
• Czasopismo: Dependence Modeling
• Rocznik: 2016
• Tom: 4
• Numer: 1

Daty

otrzymano

2016-08-12

zaakceptowano

2016-11-02

online

2016-11-16

Twórcy

autor

Boris Waelchli

• Department of Banking and Finance, University of Zurich,Plattenstrasse 14, CH-8032 Zuerich, Switzerland

Identyfikatory

DOI

10.1515/demo-2016-0015