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Large scale simulation of synthetic markets

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  • Gerardo-Giorda, Luca
  • Germano, Guido
  • Scalas, Enrico

Abstract

High-frequency trading has been experiencing an increase of interest both for practical purposes within financial institutions and within academic research; recently, the UK Government Office for Science reviewed the state of the art and gave an outlook analysis. Therefore, models for tick-by-tick financial time series are becoming more and more important. Together with high-frequency trading comes the need for fast simulations of full synthetic markets for several purposes including scenario analyses for risk evaluation. These simulations are very suitable to be run on massively parallel architectures. Aside more traditional large-scale parallel computers, high-end personal computers equipped with several multi-core CPUs and general-purpose GPU programming are gaining importance as cheap and easily available alternatives. A further option are FPGAs. In all cases, development can be done in a unified framework with standard C or C++ code and calls to appropriate libraries like MPI (for CPUs) or CUDA for (GPGPUs). Here we present such a prototype simulation of a synthetic regulated equity market. The basic ingredients to build a synthetic share are two sequences of random variables, one for the inter-trade durations and one for the tick-by-tick logarithmic returns. Our extensive simulations are based on several distributional choices for the above random variables, including Mittag-Leffler distributed inter-trade durations and alpha-stable tick-by-tick logarithmic returns.

Suggested Citation

  • Gerardo-Giorda, Luca & Germano, Guido & Scalas, Enrico, 2015. "Large scale simulation of synthetic markets," LSE Research Online Documents on Economics 67563, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:67563
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    File URL: http://eprints.lse.ac.uk/67563/
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    References listed on IDEAS

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    2. Enrico Scalas, 2011. "A class of CTRWs: Compound fractional Poisson processes," Papers 1103.0647, arXiv.org.
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    5. Tijana Radivojević & Jonatha Anselmi & Enrico Scalas, 2014. "Ergodic Transition in a Simple Model of the Continuous Double Auction," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    6. G. Livan & S. Alfarano & E. Scalas, 2011. "The fine structure of spectral properties for random correlation matrices: an application to financial markets," Papers 1102.4076, arXiv.org.
    7. Beddington, John & Furse, Clara & Bond, Philip & Cliff, Dave & Goodhart, Charles & Houstoun, Kevin & Linton, Oliver & Zigrand, Jean-Pierre, 2012. "Foresight: the future of computer trading in financial markets: final project report," LSE Research Online Documents on Economics 62157, London School of Economics and Political Science, LSE Library.
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    Cited by:

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    2. Xu Wang & JinRong Wang & Michal Fečkan, 2020. "BP Neural Network Calculus in Economic Growth Modelling of the Group of Seven," Mathematics, MDPI, vol. 8(1), pages 1-11, January.

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    • J1 - Labor and Demographic Economics - - Demographic Economics

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