Arbeitspapier

Using machine learning to measure financial risk in China

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We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad.

ISBN
978-92-899-5509-6
Sprache
Englisch

Erschienen in
Series: ECB Working Paper ; No. 2767

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Miscellaneous Mathematical Tools
Business Fluctuations; Cycles
International Business Cycles
International Financial Markets
Thema
China
financial risk
textual analysis
machine learning
topic modelling
LDA

Ereignis
Geistige Schöpfung
(wer)
Al-Haschimi, Alexander
Apostolou, Apostolos
Azqueta-Gavaldon, Andres
Ricci, Martino
Ereignis
Veröffentlichung
(wer)
European Central Bank (ECB)
(wo)
Frankfurt a. M.
(wann)
2023

DOI
doi:10.2866/565243
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Original beim Datenpartner anzeigen

Objekttyp

  • Arbeitspapier

Beteiligte

  • Al-Haschimi, Alexander
  • Apostolou, Apostolos
  • Azqueta-Gavaldon, Andres
  • Ricci, Martino
  • European Central Bank (ECB)

Entstanden

  • 2023

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