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Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws

Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws

Wing Lon Ng
Copyright: © 2013 |Pages: 24
ISBN13: 9781466620117|ISBN10: 1466620110|EISBN13: 9781466620124
DOI: 10.4018/978-1-4666-2011-7.ch013
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MLA

Ng, Wing Lon. "Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws." Simulation in Computational Finance and Economics: Tools and Emerging Applications, edited by Biliana Alexandrova-Kabadjova, et al., IGI Global, 2013, pp. 255-278. https://doi.org/10.4018/978-1-4666-2011-7.ch013

APA

Ng, W. L. (2013). Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws. In B. Alexandrova-Kabadjova, S. Martinez-Jaramillo, A. Garcia-Almanza, & E. Tsang (Eds.), Simulation in Computational Finance and Economics: Tools and Emerging Applications (pp. 255-278). IGI Global. https://doi.org/10.4018/978-1-4666-2011-7.ch013

Chicago

Ng, Wing Lon. "Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws." In Simulation in Computational Finance and Economics: Tools and Emerging Applications, edited by Biliana Alexandrova-Kabadjova, et al., 255-278. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2011-7.ch013

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Abstract

This chapter uses the abundance of high frequency data to estimate scaling law models and then apply appropriately scaled measures to provide long-term market risk forecasts. The objective is to analyse extreme price movements from tick-by-tick real-time data to trace the footprints of traders that eventually form the overall movement of market prices (price coastline) and potential bubbles. The framework is applied to empirical limit order book data from the London Stock Exchange. The sample period ranges from June 2007 to June 2008 and covers the start of the subprime crisis that later escalated into the economic crisis. After extracting the scaling exponent and checking its robustness with bootstrap simulations, the authors investigate longer term price movements in more detail, making use of the scale invariance property of the scaling law. In particular, they provide financial risk forecasts for a testing period and compare these with the popular Value-at-Risk and expected tail loss measures, showing the outperformance of the scaling law approach. Finally, a set of simulations are run to explore which scaling exponent is more likely to trigger market turbulence.

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