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Semiparametric Density Forecasts of Daily Financial Returns from Intraday Data


  • Mark Hallam
  • Jose Olmo


In this article we propose a new method for producing semiparametric density forecasts for daily financial returns from high-frequency intraday data. The daily return density is estimated directly from intraday observations that have been appropriately rescaled using results from the theory of unifractal processes. The method preserves information concerning both the magnitude and sign of the intraday returns and allows them to influence all properties of the daily return density via the use of nonparametric specifications for the daily return distribution. The out-of-sample density forecasting performance of the method is shown to be competitive with existing methods based on intraday data for exchange rate and equity index data. (JEL: C58, C22, G17)

Suggested Citation

  • Mark Hallam & Jose Olmo, 2014. "Semiparametric Density Forecasts of Daily Financial Returns from Intraday Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(2), pages 408-432.
  • Handle: RePEc:oup:jfinec:v:12:y:2014:i:2:p:408-432.

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    References listed on IDEAS

    1. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," Review of Economic Studies, Oxford University Press, vol. 63(3), pages 435-463.
    2. Anne Vila Wetherilt & Simon Wells, 2004. "Long-horizon equity return predictability: some new evidence for the United Kingdom," Bank of England working papers 244, Bank of England.
    3. Khil, Jaeuk & Lee, Bong-Soo, 2002. "A Time-Series Model of Stock Returns with a Positive Short-Term Correlation and a Negative Long-Term Correlation," Review of Quantitative Finance and Accounting, Springer, vol. 18(4), pages 381-404, June.
    4. Carl Chiarella & Shenhuai Gao, 2002. "Type I Spurious Regression in Econometrics," Working Paper Series 114, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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    Cited by:

    1. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein f├╝r Socialpolitik / German Economic Association.
    2. repec:eee:reveco:v:49:y:2017:i:c:p:69-83 is not listed on IDEAS
    3. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.

    More about this item

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


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