Recurrence quantification analysis of energy market crises: a nonlinear approach to risk management

- Bielinskyi, Andrii (orcid.org/0000-0002-2821-2895), Soloviev, V.N. (orcid.org/0000-0002-4945-202X), Solovieva, Victoria (orcid.org/0000-0002-8090-9569), Semerikov, Serhiy O. (orcid.org/0000-0003-0789-0272) and Radin, Michael (orcid.org/0000-0001-9951-7955) (2023) Recurrence quantification analysis of energy market crises: a nonlinear approach to risk management M3E2-MLPEED 2022: The 10th International Conference on Monitoring, Modeling & Management of Emergent Economy, November 17-18, 2022 (3465). pp. 110-131. ISSN 1613-0073

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Abstract

The energy market is characterized by unstable price dynamics, which challenge the quantitative models of pricing processes and result in abnormal shocks and crashes. We use recurrence quantification analysis (RQA) to analyze and construct indicators of intermittent events in energy indices, where regular patterns are interrupted by chaotic fluctuations, which could signal the onset of crisis events. We apply RQA to daily data of Henry Hub natural gas spot prices, WTI spot prices, and Europe Brent spot prices. Our empirical results show that the recurrence measures capture the distinctive features of crashes and can be used for effective risk management strategies.

Item Type:	Article
Keywords:	energy market, recurrence quantification analysis, crash detection, risk management, price dynamics, instability, abnormal shocks
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.94 Simulation Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 33 Economics. Economic science
Divisions:	Institute for Digitalisation of Education > Department of Cloud-Oriented Systems and Artificial Intelligence in Education
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	04 Sep 2023 09:22
Last Modified:	04 Sep 2023 09:22
URI:	https://lib.iitta.gov.ua/id/eprint/736525

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