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A big data approach to analyzing market volatility

Author

Listed:
  • Wu, Kesheng
  • Bethel, E. Wes
  • Gu, Ming
  • Leinweber, David
  • Rübe, Oliver

Abstract

Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. This requires computing resources that are not easily available to financial academics and regulators. Fortunately, data-intensive scientific research has developed a series of tools and techniques for working with a large amount of data. In this work, we demonstrate that these techniques are effective for market data analysis by computing an early warning indicator called Volume-synchronized Probability of Informed trading (VPIN) on a massive set of futures trading records. The test data contains five and a half year’s worth of trading data for about 100 most liquid futures contracts, includes about 3 billion trades, and takes 140GB as text files. By using (1) a more efficient file format for storing the trading records, (2) more effective data structures and algorithms, and (3) parallelizing the computations, we are able to explore 16,000 different parameter combinations for computing VPIN in less than 20 hours on a 32-core IBM DataPlex machine. On average, computing VPIN of one futures contract over 5.5 years takes around 1.5 seconds on one core, which demonstrates that a modest computer is sufficient to monitor a vast number of trading activities in real-time – an ability that could be valuable to regulators. By examining a large number of parameter combinations, we are also able to identify the parameter settings that improves the prediction accuracy from 80% to 93%.

Suggested Citation

  • Wu, Kesheng & Bethel, E. Wes & Gu, Ming & Leinweber, David & Rübe, Oliver, 2013. "A big data approach to analyzing market volatility," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 241-267.
    • Handle: RePEc:ris:iosalg:0016
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    Citations

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    Cited by:

    1. Torben G. Andersen & Oleg Bondarenko, 2013. "Assessing Measures of Order Flow Toxicity via Perfect Trade Classification," CREATES Research Papers 2013-43, Department of Economics and Business Economics, Aarhus University.
    2. Bonnie F. Van Ness & Robert A. Van Ness & Serhat Yildiz, 2017. "The role of HFTs in order flow toxicity and stock price variance, and predicting changes in HFTs’ liquidity provisions," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 739-762, October.
    3. Torben G. Andersen & Oleg Bondarenko, 2015. "Assessing Measures of Order Flow Toxicity and Early Warning Signals for Market Turbulence," Review of Finance, European Finance Association, vol. 19(1), pages 1-54.
    4. Khaladdin Rzayev & Gbenga Ibikunle, 2021. "Order aggressiveness and flash crashes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2647-2673, April.
    5. Prodromou, Tina & Westerholm, P. Joakim, 2022. "Are high frequency traders responsible for extreme price movements?," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 94-111.
    6. Andersen, Torben G. & Bondarenko, Oleg, 2014. "Reflecting on the VPIN dispute," Journal of Financial Markets, Elsevier, vol. 17(C), pages 53-64.
    7. Easley, David & López de Prado, Marcos M. & O'Hara, Maureen, 2014. "VPIN and the Flash Crash: A rejoinder," Journal of Financial Markets, Elsevier, vol. 17(C), pages 47-52.
    8. Zeynep Cobandag Guloglu & Cumhur Ekinci, 2022. "Liquidity measurement: A comparative review of the literature with a focus on high frequency," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 41-74, February.
    9. Abad, David & Massot, Magdalena & Pascual, Roberto, 2018. "Evaluating VPIN as a trigger for single-stock circuit breakers," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 21-36.
    10. Paparizos, Panagiotis & Dimitriou, Dimitrios & Kenourgios, Dimitris & Simos, Theodore, 2016. "On high frequency dynamics between information asymmetry and volatility for securities," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 21-34.
    11. Yildiz, Serhat & Van Ness, Bonnie & Van Ness, Robert, 2020. "VPIN, liquidity, and return volatility in the U.S. equity markets," Global Finance Journal, Elsevier, vol. 45(C).

    More about this item

    Keywords

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    JEL classification:

    • J00 - Labor and Demographic Economics - - General - - - General

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