Entropy as Leading Indicator for Extreme Systemic Risk Events

Radu Lupu (), Iulia Lupu, Tanase Stamule () and Mihai Roman
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Radu Lupu: The Bucharest University of Economic Studies; Institute for Economic Forecasting; Bucharest, Romania
Tanase Stamule: The Bucharest University of Economic Studies, Bucharest, Romania
Mihai Roman: The Bucharest University of Economic Studies, Bucharest, Romania

Journal for Economic Forecasting, 2022, issue 4, 58-73

Abstract: Ensuring financial stability is one of the main objectives of authorities supervising financial markets. Analyses of the extent to which critical destabilising events may materialise fuel their actions. Chief among these investigations is the attempt to identify leading indicators that could set forth early warning systems. This paper focuses on extreme systemic risk situations to document their dependence on market action present in the preceding time intervals. We use the N-BEATS model, which proved to be one of the best neural network tools to predict time series, detect anomalies (jumps) in the dynamics of CoVaR measures for the most liquid banks in the European markets, and measure the Shannon entropy of the power spectral density in samples that lead to these events. Employing several logistic regressions, we document the capacity of entropy to explain the realisation of these anomalies.

Keywords: jumps; anomaly detection tools; early warning systems (search for similar items in EconPapers)
JEL-codes: C45 G15 G32 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rjr:romjef:v::y:2022:i:4:p:58-73

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