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Detecting intraday periodicities with application to high frequency exchange rates

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  • Chris Brooks
  • Melvin J. Hinich

Abstract

Summary. Many recent papers have documented periodicities in returns, return volatility, bid–ask spreads and trading volume, in both equity and foreign exchange markets. We propose and employ a new test for detecting subtle periodicities in time series data based on a signal coherence function. The technique is applied to a set of seven half‐hourly exchange rate series. Overall, we find the signal coherence to be maximal at the 8‐h and 12‐h frequencies. Retaining only the most coherent frequencies for each series, we implement a trading rule that is based on these observed periodicities. Our results demonstrate in all cases except one that, in gross terms, the rules can generate returns that are considerably greater than those of a buy‐and‐hold strategy, although they cannot retain their profitability net of transactions costs. We conjecture that this methodology could constitute an important tool for financial market researchers which will enable them to detect, quantify and rank the various periodic components in financial data better.

Suggested Citation

  • Chris Brooks & Melvin J. Hinich, 2006. "Detecting intraday periodicities with application to high frequency exchange rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 241-259, April.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:2:p:241-259
    DOI: 10.1111/j.1467-9876.2006.00534.x
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    Cited by:

    1. Romero-Meza, Rafael & Bonilla, Claudio & Benedetti, Hugo & Serletis, Apostolos, 2015. "Nonlinearities and financial contagion in Latin American stock markets," Economic Modelling, Elsevier, vol. 51(C), pages 653-656.
    2. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    3. Guglielmo D’Amico & Ada Lika & Filippo Petroni, 2019. "Change point dynamics for financial data: an indexed Markov chain approach," Annals of Finance, Springer, vol. 15(2), pages 247-266, June.
    4. Escañuela Romana, Ignacio, 2011. "Evidencia empírica sobre la predictibilidad de los ciclos bursátiles: el comportamiento del índice Dow Jones Industrial Average en las crisis bursátiles de 1929, 1987 y 2997 [Empirical evidence on ," MPRA Paper 33150, University Library of Munich, Germany.

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