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Systemic Risk Identification, Modelling, Analysis, and Monitoring: An Integrated Approach

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Is a
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Academic paper
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Academic Paper attributes

arXiv ID
1310.64860
arXiv Classification
Computer science
Computer science
0
Publication URL
arxiv.org/pdf/1310.6...86.pdf0
Publisher
ArXiv
ArXiv
0
DOI
doi.org/10.48550/ar...10.64860
Paid/Free
Free0
Academic Discipline
Finance
Finance
0
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Quantitative finance
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Computer science
Computer science
0
Submission Date
October 24, 2013
0
Author Names
Antoaneta Sergueiva0
Paper abstract

Research capacity is critical in understanding systemic risk and informing new regulation. Banking regulation has not kept pace with all the complexities of financial innovation. The academic literature on systemic risk is rapidly expanding. The majority of papers analyse a single source or a consolidated source of risk and its effect. A fraction of publications quantify systemic risk measures or formulate penalties for systemically important financial institutions that are of practical regulatory relevance. The challenges facing systemic risk evaluation and regulation still persist, as the definition of systemic risk is somewhat unsettled and that affects attempts to provide solutions. Our understanding of systemic risk is evolving and the awareness of data relevance is rising gradually; this challenge is reflected in the focus of major international research initiatives. There is a consensus that the direct and indirect costs of a systemic crisis are enormous as opposed to preventing it, and that without regulation the externalities will not be prevented; but there is no consensus yet on the extent and detail of regulation, and research expectations are to facilitate the regulatory process. This report outlines an integrated approach for systemic risk evaluation based on multiple types of interbank exposures through innovative modelling approaches as tensorial multilayer networks, suggests how to relate underlying economic data and how to extend the network to cover financial market information. We reason about data requirements and time scale effects, and outline a multi-model hypernetwork of systemic risk knowledge as a scenario analysis and policy support tool. The argument is that logical steps forward would incorporate the range of risk sources and their interrelated effects as contributions towards an overall systemic risk indicator, would perform an integral analysis of ...

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