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Forecasting market risk using ultra-high-frequency data and scaling laws

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  • Jun Qi
  • Lan Yi
  • Yiyun Chen

Abstract

This paper develops a new multiple time scale-based empirical framework for market risk estimates and forecasts. Ultra-high frequency data are used in the empirical analysis to estimate the parameters of empirical scaling laws which gives a better understanding of the dynamic nature of the market. A comparison of the new approach with the popular Value-at-Risk and expected tail loss measures with respect to their risk forecasts during the crisis period in 2008 is presented. The empirical results show the outperformance of the new scaling law method which turns out to be more accurate and flexible due to the scale invariance. The scaling law method promotes the use of massive real data in developing risk measurement and forecasting models.

Suggested Citation

  • Jun Qi & Lan Yi & Yiyun Chen, 2018. "Forecasting market risk using ultra-high-frequency data and scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 18(12), pages 2085-2099, December.
    • Handle: RePEc:taf:quantf:v:18:y:2018:i:12:p:2085-2099
    DOI: 10.1080/14697688.2018.1453166
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    Cited by:

    1. Hu, Shicheng & Zhang, Weijie & Li, Danping & Wu, Bing, 2023. "Incorporating improved directional change and regime change detection to formulate trading strategies in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

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