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Measuring the Impact Intradaily Events Have on the Persistent Nature of Volatility

In: Wavelet Applications in Economics and Finance

Author

Listed:
  • Mark J. Jensen

    (Federal Reserve Bank of Atlanta)

  • Brandon Whitcher

    (Pfizer Worldwide Research & Development)

Abstract

In this chapter we measure the effect a scheduled event, like the opening or closing of a regional foreign exchange market, or a unscheduled act, such as a market crash, a political upheaval, or a surprise news announcement, has on the foreign exchange rate’s level of volatility and its well documented long-memory behavior. Volatility in the foreign exchange rate is modeled as a non-stationary, long-memory, stochastic volatility process whose fractional differencing parameter is allowed to vary over time. This non-stationary model of volatility reveals that long-memory is not a spurious property associated with infrequent structural changes, but is a integral part of the volatility process. Over most of the sample period, volatility exhibits the strong persistence of a long-memory process. It is only after a market surprise or unanticipated economic news announcement that volatility briefly sheds its strong persistence.

Suggested Citation

  • Mark J. Jensen & Brandon Whitcher, 2014. "Measuring the Impact Intradaily Events Have on the Persistent Nature of Volatility," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 103-129, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-07061-2_5
    DOI: 10.1007/978-3-319-07061-2_5
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    Cited by:

    1. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia, 2023. "Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence," International Review of Financial Analysis, Elsevier, vol. 87(C).
    2. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia & Brzeszczyński, Janusz, 2024. "Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 205(C).

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