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Characterizing financial crises using high-frequency data

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Listed:
  • Mardi Dungey
  • Jet Holloway
  • Abdullah Yalaman
  • Wenying Yao

Abstract

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.

Suggested Citation

  • Mardi Dungey & Jet Holloway & Abdullah Yalaman & Wenying Yao, 2022. "Characterizing financial crises using high-frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 22(4), pages 743-760, April.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:4:p:743-760
    DOI: 10.1080/14697688.2022.2027504
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    1. Akay, Ozgur (Ozzy) & Senyuz, Zeynep & Yoldas, Emre, 2013. "Hedge fund contagion and risk-adjusted returns: A Markov-switching dynamic factor approach," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 16-29.
    2. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    4. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2015. "Modelling systemic price cojumps with Hawkes factor models," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1137-1156, July.
    5. Dungey, Mardi & Matei, Marius & Treepongkaruna, Sirimon, 2020. "Examining stress in Asian currencies: A perspective offered by high frequency financial market data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    6. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    7. Lucio Maria Calcagnile & Giacomo Bormetti & Michele Treccani & Stefano Marmi & Fabrizio Lillo, 2018. "Collective synchronization and high frequency systemic instabilities in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 18(2), pages 237-247, February.
    8. Graciela L. Kaminsky & Carmen M. Reinhart & Carlos A. Végh, 2003. "The Unholy Trinity of Financial Contagion," Journal of Economic Perspectives, American Economic Association, vol. 17(4), pages 51-74, Fall.
    9. Wenying Yao & Mardi Dungey & Vitali Alexeev, 2020. "Modelling Financial Contagion Using High Frequency Data," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 314-330, September.
    10. Alexeev, Vitali & Dungey, Mardi & Yao, Wenying, 2017. "Time-varying continuous and jump betas: The role of firm characteristics and periods of stress," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 1-19.
    11. Kee-Hong Bae & G. Andrew Karolyi & René M. Stulz, 2003. "A New Approach to Measuring Financial Contagion," The Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 717-763, July.
    12. Fabio Busetti & Andrew Harvey, 2011. "When is a Copula Constant? A Test for Changing Relationships," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 106-131, Winter.
    13. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    14. Mardi Dungey & Renee Fry & Brenda Gonzalez-Hermosillo & Vance Martin, 2005. "Empirical modelling of contagion: a review of methodologies," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 9-24.
    15. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    16. Alexeev, Vitali & Urga, Giovanni & Yao, Wenying, 2019. "Asymmetric jump beta estimation with implications for portfolio risk management," International Review of Economics & Finance, Elsevier, vol. 62(C), pages 20-40.
    17. Akay, Ozgur (Ozzy) & Senyuz, Zeynep & Yoldas, Emre, 2013. "Hedge fund contagion and risk-adjusted returns: A Markov-switching dynamic factor approach," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 16-29.
    18. Jérôme Lahaye & Sébastien Laurent & Christopher J. Neely, 2011. "Jumps, cojumps and macro announcements," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 893-921, September.
    19. Kuntara Pukthuanthong & Richard Roll, 2015. "Internationally Correlated Jumps," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 5(1), pages 92-111.
    20. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
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