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Public news flow in intraday component models for trading activity and volatility

Author

Listed:
  • Adam Clements

    (QUT)

  • Joanne Fuller

    (QUT)

  • Vasilios Papalexiou

Abstract

Understanding the determinants of, and forecasting asset return volatility are crucial issues in many financial applications. Many earlier studies have considered the impact of trading activity and news arrivals on volatility. This paper develops a range of intraday component models for volatility and order flow that include the impact of news arrivals. Estimates of the conditional mean of order flow, taking into account news flow are included in models ofvolatility providing a superior in-sample fit. At a 1-minute frequency, it is found that first generating forecasts of order flow which are then included in forecasts of volatility leads to superior day-ahead forecasts of volatility. While including overnight news arrivals directly into models for volatility improves in-sample fit, this approach produces inferior forecasts.

Suggested Citation

  • Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.
    • Handle: RePEc:qut:auncer:2015_04
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    File URL: http://www.ncer.edu.au/papers/documents/WP106.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Volatility; Order flow; News; Dynamic conditional score; forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G00 - Financial Economics - - General - - - General

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