G-curve inversion as a precursor of a financial crisis

Currently applicable techniques for identifying financial crises are very diverse and, as a rule, relate to two main approaches: econometric and signaling. The indicators of these techniques show high efficiency directly at the start of a crisis, which necessitates the use of complementary indicators that can identify a crisis long before it occurs. The assessment of the effectiveness of using the G-curve as a precursor of a financial crisis is indicated in the paper as the key problem, which will allow taking anti-crisis government regulation measures in advance. The authors of the paper analyzed the macroeconomic parameters of the development of the Russian economy in order to identify the prerequisites for the crisis emergence. It was found that the current indicators of economic development do not allow concluding that a crisis is approaching; official forecasting institutions that use signaling and econometric assessment techniques in their activities do not confirm this fact. Nevertheless, the conducted analysis of the yield curve configuration indicates the formation of negative conditions for the emergence of an economic recession. The authors have proved the role of the G-curve in assessing the behavioral patterns of market participants, which often become the basis for the imbalance of financial relations. The paper substantiates the necessity to use a systemic approach in the process of identifying financial crises, taking into account both the short-term forecasting capabilities of econometric and signaling techniques and long-term assessing investment sentiment, which will ensure the timeliness and comprehensiveness of the anti-crisis state regulation measures.

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Publication Details

Journal
Digital Economy & Innovations
Published
2025-01-01
DOI
https://doi.org/10.18323/3034-2074-2025-1-60-2
Primary Topic
Complex Systems and Time Series Analysis
Type
article
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article

G-curve inversion as a precursor of a financial crisis

Maria E. Konovalova, Olga Kuzmina, Sergey Zhironkin
Digital Economy & Innovations
Complex Systems and Time Series Analysis
article

G-curve inversion as a precursor of a financial crisis

Maria E. Konovalova, Olga Kuzmina, Sergey Zhironkin
article en

Abstract

Currently applicable techniques for identifying financial crises are very diverse and, as a rule, relate to two main approaches: econometric and signaling. The indicators of these techniques show high efficiency directly at the start of a crisis, which necessitates the use of complementary indicators that can identify a crisis long before it occurs. The assessment of the effectiveness of using the G-curve as a precursor of a financial crisis is indicated in the paper as the key problem, which will allow taking anti-crisis government regulation measures in advance. The authors of the paper analyzed the macroeconomic parameters of the development of the Russian economy in order to identify the prerequisites for the crisis emergence. It was found that the current indicators of economic development do not allow concluding that a crisis is approaching; official forecasting institutions that use signaling and econometric assessment techniques in their activities do not confirm this fact. Nevertheless, the conducted analysis of the yield curve configuration indicates the formation of negative conditions for the emergence of an economic recession. The authors have proved the role of the G-curve in assessing the behavioral patterns of market participants, which often become the basis for the imbalance of financial relations. The paper substantiates the necessity to use a systemic approach in the process of identifying financial crises, taking into account both the short-term forecasting capabilities of econometric and signaling techniques and long-term assessing investment sentiment, which will ensure the timeliness and comprehensiveness of the anti-crisis state regulation measures.

Digital Economy & Innovations(1)
Samara State University of Economics (RU), Siberian Federal University (RU)
Openalex Percentile: Top 90%
Complex Systems and Time Series Analysis
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