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Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets

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  • Chaboud, Alain P.
  • Chiquoine, Benjamin
  • Hjalmarsson, Erik
  • Loretan, Mico

Abstract

Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. Using the standard realized volatility estimator, we find that one can sample dollar/euro returns as frequently as once every 15 to 20Â s without contaminating estimates of integrated volatility; 10-year Treasury note returns may be sampled as frequently as once every 2 to 3Â min on days without U.S. macroeconomic announcements, and as frequently as once every 40Â s on announcement days. Using a simple realized kernel estimator, this sampling frequency can be increased to once every 2 to 5Â s for dollar/euro returns and to about once every 30 to 40Â s for T-note returns. These sampling frequencies, especially in the case of dollar/euro returns, are much higher than those that are generally recommended in the empirical literature on realized volatility in equity markets. The higher sampling frequencies for dollar/euro and T-note returns likely reflect the superior depth and liquidity of these markets.

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  • Chaboud, Alain P. & Chiquoine, Benjamin & Hjalmarsson, Erik & Loretan, Mico, 2010. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 212-240, March.
  • Handle: RePEc:eee:empfin:v:17:y:2010:i:2:p:212-240
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    Cited by:

    1. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
    2. Marina Theodosiou, 2010. "Calendar Time Sampling of High Frequency Financial Asset Price and the Verdict on Jumps," Working Papers 2010-7, Central Bank of Cyprus.
    3. repec:eee:empfin:v:43:y:2017:i:c:p:59-73 is not listed on IDEAS
    4. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    5. Opschoor, Anne & Taylor, Nick & van der Wel, Michel & van Dijk, Dick, 2014. "Order flow and volatility: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 185-201.
    6. Taesuk Lee & Mico Loretan & Werner Ploberger, 2013. "Rate-optimal tests for jumps in diffusion processes," Statistical Papers, Springer, vol. 54(4), pages 1009-1041, November.
    7. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2009. "Rise of the machines: algorithmic trading in the foreign exchange market," International Finance Discussion Papers 980, Board of Governors of the Federal Reserve System (U.S.).
    8. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    9. Schmidt, Anatoly B., 2009. "Detrending the realized volatility in the global FX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(9), pages 1887-1892.

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