Characterizing financial crises using high-frequency data

Dungey M., Holloway J., YALAMAN A., Yao W.

QUANTITATIVE FINANCE, vol.22, no.4, pp.743-760, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1080/14697688.2022.2027504
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit, MathSciNet, zbMATH
  • Page Numbers: pp.743-760
  • Keywords: High-frequency data, Tail behavior, Financial crisis, US Treasury markets, HEDGE FUND CONTAGION, JUMPS, VOLATILITY, COMPONENTS, RETURNS, MODELS
  • Eskisehir Osmangazi University Affiliated: Yes


Recent advances in high-frequency financial econometrics enable us to characterize which components of the data generating processes change in crisis, and which do not. This paper introduces a new statistic which captures large discontinuities in the composition of a given price series. Monte Carlo simulations suggest that this statistic is useful in characterizing the tail behavior across different sample periods. An application to US Treasury market provides evidence consistent with identifying periods of stress via flight-to-cash behavior which results in increased abrupt price falls at the short end of the term structure and decreased negative price jumps at the long end.