Can We Predict Financial Crises?
- DOI
- 10.2991/978-94-6463-246-0_7How to use a DOI?
- Keywords
- Financial Crises; Forecasting; Predictive Regressions; Credit
- Abstract
Historically, financial crises have crippled individuals, businesses and global economies; identifying prospective threats accurately can mitigate their repercussions. This paper examines the annual economic statistics in fourteen developed countries from 1870 to 2008. We demonstrate the variations of credit and money aggregates over time, analysing the reasons behind those changes in light of macroeconomic history. We build OLS and logit models to determine the underlying link between financial instability and major macroeconomic indicators, proving that growth in credit aggregates is a salient indicator for a higher likelihood of a financial crisis. We compare the predictive power of GDP, money and credit growth in different eras, taking the Second World War and the 1980s as turning points. The results confirm the significance of credit in forecasting. These findings contribute to the discussion of the predictability of financial crises and provide valuable insights for economic agents.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wen Dang AU - Jiayi Peng AU - Ruidan Fan AU - Zifei Wang PY - 2023 DA - 2023/09/26 TI - Can We Predict Financial Crises? BT - Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023) PB - Atlantis Press SP - 53 EP - 70 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-246-0_7 DO - 10.2991/978-94-6463-246-0_7 ID - Dang2023 ER -