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

Jun Qi, Lan Yi and Yiyun Chen

Quantitative Finance, 2018, vol. 18, issue 12, 2085-2099

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.

Date: 2018
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DOI: 10.1080/14697688.2018.1453166

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