ArticleiScience2025
Early warning of regime switching in a complex financial system from a spillover network dynamic perspective.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Spatial early warning signals to assess economic resilience.iScience · 2025Article
- The Evolution of the Linkage Among Geopolitical Risk, the US Dollar Index, Crude Oil Prices, and Gold Prices at Multiple Scales: A Wavelet Transform-Based Dynamic Transfer Entropy Network Method.Entropy (Basel, Switzerland) · 2025Article
- Early warning of regime switching in a financial time series: A heteroskedastic network model.PloS one · 2025Article
- Analysis of the risk spillover network of G20 stock markets based on transfer entropy and complex network approaches.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early warning of regime switching in a complex financial system is a critical and challenging issue in risk management. Previous research has examined regime switching through analyzing the fluctuation features in a single point in time series; however, it has rarely examined the dynamic spillovers across multivariable time series. This paper develops an early warning model of regime switching that incorporates a spillover network model and a machine learning model. Typical energy prices and stock market indices are selected as the sample data. The key spillover networks can be detected according to the distribution of the network indicators. The early warning signals can be captured by six typical machine learning models, and the random forest model has better performance. The robustness of the model is also discussed. Our study enriches regime switching research and provides important early warning signals for policymakers and market investors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.