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Calendar Time Sampling of High Frequency Financial Asset Price and the Verdict on Jumps

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  • Marina Theodosiou

    ()
    (Central Bank of Cyprus)

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    Abstract

    In the current paper, we investigate the bias introduced through the calendar time sampling of the price process of financial assets. We analyze results from a Monte Carlo simulation which point to the conclusion that the multitude of jumps reported in the literature might be, to a large extent, an artifact of the bias introduced through the previous tick sampling scheme, used for the time homogenization the price series. We advocate the use of Akima cubic splines as an alternative to the popular previous tick method. Monte Carlo simulation results confirm the suitability of Akima cubic splines in high frequency applications and the advantages of these over other calendar time sampling schemes, such as the linear interpolation and the previous tick method. Empirical results from the FX market complement the analysis.

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    File URL: http://www.centralbank.gov.cy/media/pdf/NPWPE_No7_092010.pdf
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    Bibliographic Info

    Paper provided by Central Bank of Cyprus in its series Working Papers with number 2010-7.

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    Length: 54 pages
    Date of creation: Sep 2010
    Date of revision:
    Handle: RePEc:cyb:wpaper:2010-7

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    Web page: http://www.centralbank.gov.cy/nqcontent.cfm?a_id=1
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    Related research

    Keywords: Sampling schemes; previous tick method; quadratic variation; jumps; stochastic volatility; realized measures; high-frequency data;

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    1. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    2. George Chacko & Luis M. Viceira, 1999. "Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets," NBER Working Papers 7377, National Bureau of Economic Research, Inc.
    3. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    4. Mark Podolskij & Daniel Ziggel, 2008. "New tests for jumps: a threshold-based approach," CREATES Research Papers 2008-34, School of Economics and Management, University of Aarhus.
    5. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, Elsevier.
    6. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
    7. Sang Hyop Lee & Andrew Mason, 2006. "Who Gains from the Demographic Dividend? Forecasting Income by Age," Working Papers 200613, University of Hawaii at Manoa, Department of Economics.
    8. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2002. "Analytic Evaluation of Volatility Forecasts," CIRANO Working Papers 2002s-90, CIRANO.
    9. Fulvio Corsi & Gilles Zumbach & Ulrich M├╝ller & Michel Dacorogna, 2004. "Consistent high-precision volatility from high-frequency data," Finance 0407005, EconWPA.
    10. Bollerslev, Tim & Zhou, Hao, 2002. "Estimating stochastic volatility diffusion using conditional moments of integrated volatility," Journal of Econometrics, Elsevier, vol. 109(1), pages 33-65, July.
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