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Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes*

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A micro-scale model is proposed for the evolution of a limit order book in modern high-frequency trading applications. Within this model, order flows are described by doubly stochastic Poisson processes (also called Cox processes) taking account of the stochastic character of the intensities of order flows. The models for the number of order imbalance (NOI) process and order flow imbalance (OFI) process are introduced as two-sided risk processes that are special compound Cox processes. These processes are sensitive indicators of the current state of the limit order book since time intervals between events in a limit order book are usually so short that price changes are relatively infrequent events. Therefore price changes provide a very coarse and limited description of market dynamics at time micro-scales. NOI and OFI processes track best bid and ask queues and change much faster than prices. They incorporate information about build-ups and depletions of order queues and they can be used to interpolate market dynamics between price changes and to track the toxicity of order flows. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers without modeling the external information background. The proposed model gives the opportunity to link the micro-scale high-frequency dynamics of the limit order book with the macroscale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems for special random sums and hence, to give a deeper insight in the nature of popular models of statistical regularities of the evolution of characteristics of financial markets such as generalized hyperbolic distributions and other normal variance-mean mixtures.

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References

  1. F. Abergel and A. Jedidi, “A mathematical approach to order book modelling,” in: Chakrabarti, B. K., Chakraborti, A., and Mitra, M. (Eds.), Econophysics of Order-Driven Markets, Springer, New York (2011), pp. 93–108.

    Chapter  Google Scholar 

  2. M. Avellaneda and S. Stoikov, “High-frequency trading in a limit order book,” Quant. Financ., 8, 217–224 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  3. O. E. Barndorff-Nielsen, “Hyperbolic distributions and distributions of hyperbolae,” Scand. J. Stat., 5, 151–157 (1978).

    MathSciNet  MATH  Google Scholar 

  4. O. E. Barndorff-Nielsen, “Models for non-Gaussian variation, with applications to turbulence,” Proc. Roy. Soc. London. Ser. A, A(368), 501–520 (1979).

  5. O. E. Barndorff-Nielsen, J. Kent, and M. Sorensen, “Normal variance-mean mixtures and zdistributions,” Int. Stat. Rev., 50, No. 2, 145–159 (1982).

    Article  MathSciNet  MATH  Google Scholar 

  6. O. E. Barndorff-Nielsen, “Processes of normal inverse Gaussian type,” Financ. Stoch., 2, 41–18 (1998).

    Article  MathSciNet  MATH  Google Scholar 

  7. O. E. Barndorff-Nielsen, P. Blasild, and J. Schmiegel, “A parsimonious and universal description of turbulent velocity increments,” Eur. Phys. J., B. 41, 345–363 (2004).

    Article  Google Scholar 

  8. V. Bening and V. Korolev, Generalized Poisson Models and Their Applications in Insurance and Finance, VSP, Utrecht (2002).

    Book  MATH  Google Scholar 

  9. J.-P. Bouchaud, M. Mezard, and M. Potters, “Statistical properties of stock order books: Empirical results and models,” Quant. Financ., 2, 251–256 (2002).

    Article  Google Scholar 

  10. P. P. Carr, D. B. Madan, and E. C. Chang, “The Variance Gamma process and option pricing,” Eur. Financ. Rev., 2, 79–105 (1998).

    Article  MATH  Google Scholar 

  11. A. Chakraborti, I. Toke, M. Patriarca, and F. Abrergel, “Empirical facts and agent-based models,” arXiv:0909.1974 (2009).

  12. A. Chertok, V. Korolev, A. Korchagin, and S. Shorgin, “Application of compound Cox processes in modeling order flows with non-homogeneous intensities,” January 14, 2014. Available at SSRN: http://ssrn.com/abstract=2378975

  13. R. Cont, S. Stoikov, and R. Talreja, “A stochastic model for order book dynamics,” Oper. Res., 58, No. 3, 549–563 (2010).

    Article  MathSciNet  MATH  Google Scholar 

  14. R. Cont, A. Kukanov, and S. Stoikov, “The price impact of order book events,” March 01, 2011. available at SSRN: http://ssrn.com/abstract=1712822

  15. R. Cont, A. Kukanov, and S. Stoikov, “The price impact of order book events,” J. Financ. Econometrics, 12, No.1, 47–88 (2014).

  16. R. Cont and de Larrard A., Price dynamics in a Markovian limit order market Working paper, 2011. Available ar SSRN: http://ssrn.com/abstract=1735338

  17. E. Eberlein and U. Keller, “Hyperbolic Distributions in Finance,” Bernoulli, 1, No. 3, 281–299 (1995).

  18. E. Eberlein, U. Keller, and K. Prause, “New insights into smile, mispricing and value at risk: the hyperbolic model,” J. Business, 71, 371–405 (1998).

    Article  Google Scholar 

  19. E. Eberlein and K. Prause, The Generalized Hyperbolic Model: Financial Derivatives and Risk Measures, Preprint No. 56, Universität Freiburg, Institut f¨ur Mathematische Stochastic, Freiburg (1998).

  20. E. Eberlein, Application of Generalized Hyperbolic L’evy Motions to Finance Freiburg: Universität Freiburg, Institut für Mathematische Stochastic, Preprint No. 64 (1999).

  21. T. Foucault, “Order flow composition and trading costs in a dynamic limit order market,” J. Financ. Markets, 2, 99–134 (1999).

    Article  Google Scholar 

  22. L. J. Gleser, The Gamma Distribution as a Mixture of Exponential Distributions, Technical Report No. 87–28, West Lafayette: Purdue University (1987).

    Google Scholar 

  23. B. V. Gnedenko and N. Kolmogorov A., Limit Distributions for Sums of Independent Random Variables, Addisson–Wesley, New York (1954).

  24. B. V. Gnedenko and V. Yu. Korolev, Random Summation: Limit Theorems and Applications, CRC Press, Boca Raton (1996).

    Google Scholar 

  25. R. Goettler, C. Parlour, and U. Rajan, “Equilibrium in a dynamic limit order market,” J. Financ., 60, 2149–2192 (2005).

    Article  Google Scholar 

  26. J. Grandell, Doubly Stochastic Poisson Processes, Springer, Berlin–Heidelberg–New York, (1976).

    MATH  Google Scholar 

  27. A. G. Hawkes, “Spectra of some self-exciting and mutually exciting point processes,” Biometrika, 58, No. 1, 83–90 (1971).

  28. P. Hewlett, Clustering of order arrivals, price impact and trade path optimization, Working paper, University of Oxford (2006).

    Google Scholar 

  29. H. Huang and A. N. Kercheval, “A generalized birth-death stochastic model for high-frequency order book dynamics,” Quant. Financ., 12, No. 4, 547–557 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  30. V. Yu. Korolev, “On convergence of the distributions of random sums of independent random variables to stable laws,” Theory Probab. Appl., 42, No. 4, 818–820 (1997).

    MathSciNet  Google Scholar 

  31. V. Yu. Korolev, “On convergence of the distributions of compound Cox processes to stable laws,” Theory Probab. Appl., 43, No. 4, 786–792 (1998).

    MathSciNet  Google Scholar 

  32. V. Yu. Korolev, “Asymptotic properties of extrema of compound Cox processes and their application to some problems of financial mathematics,” Theory Probab. Appl., 45, No. 1, 182–194 (2000).

    MathSciNet  Google Scholar 

  33. V. Yu. Korolev, Probabilistic and Statistical Methods for the Decomposition of Volatility of Chaotic Processes, Moscow State University Publishing House, Moscow (2011).

    Google Scholar 

  34. V. Yu. Korolev, V. E. Bening, and S. Ya. Shorgin, Mathematical Foundations of Risk Theory, 2nd ed., FIZMATLIT, Moscow (2011).

    Google Scholar 

  35. V. Korolev and I. Shevtsova, “An improvement of the Berry–Esseen inequality with applications to Poisson and mixed Poisson random sums,” Scand. Actuar. J., No. 2, 81–105 (2012).

    Article  MathSciNet  Google Scholar 

  36. A. Gorshenin, A. Doynikov, V. Korolev, and V. Kuzmin, “Statistical properties of the dynamics of order books: empirical results,” in: Applied Problems in Theory of Probabilities and Mathematical Statistics Related to Modeling of Information Systems: Abstracts of VI International Workshop, IPI RAS, Moscow (2012), pp. 31–51.

  37. L. M. Zaks and V. Yu. Korolev, “Generalized variance gamma distributions as limiting for random sums,” Inform. Appl., 7, No. 1, 105–115 (2013).

    Google Scholar 

  38. V. Yu. Korolev, “Generalized hyperbolic distributions as limiting for random sums,” Theory Probab. Appl., 58, No. 1 (2013).

  39. V. Yu. Korolev, A. V. Chertok, A. Yu. Korchagin, and A. K. Gorshenin, “Probability and statistical modeling of information flows in complex financial systems from high-frequency data,” Inform. Appl., 7, No. 1, 12–21 (2013).

    Google Scholar 

  40. D. B. Madan and E. Seneta, “The variance gamma (V.G.) model for share market return,” J. Business, 63, 511–524 (1990).

    Article  Google Scholar 

  41. Ch. A. Parlour, “Price dynamics in limit order markets,” Rev. Financ. Stud., 11, No. 4, 789–816 (1998).

    Article  Google Scholar 

  42. K. Prause, Modeling Financial Data Using Generalized Hyperbolic Distributions, Preprint No. 48, Universitäat Freiburg, Institut für Mathematische Stochastic, Freiburg (1997).

  43. I. Rosu, “A dynamic model of the limit order book,” Rev. Financ. Stud., 22, 4601–4641 (2009).

    Article  Google Scholar 

  44. I. G. Shevtsova, “On the accuracy of the normal approximation to Poisson random sums,” Theory Probab. Appl., 58, No. 1, 152–176 (2013).

    Google Scholar 

  45. A. N. Shiryaev, Essentials of Stochastic Finance: Facts, Models, Theory, World Scientific, Singapore (1999).

    MATH  Google Scholar 

  46. I. M. Toke, “Market making in an order book model and its impact on the spread,” Econophysics of Order-Driven Markets, Springer, New York (2011), pp. 49–64.

    Google Scholar 

  47. I. Zovko and J. D. Farmer, “The power of patience; A behavioral regularity in limit order placement,” Quant. Financ., 2, 387–392 (2002).

    Article  Google Scholar 

Download references

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Correspondence to A. V. Chertok.

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* Research supported by Russian Scientific Foundation, project 14–11–00397

Proceedings of the XXXII International Seminar on Stability Problems for Stochastic Models, Trondheim, Norway, June 16–21, 2014.

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Chertok, A.V., Korolev, V.Y. & Korchagin, A.Y. Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes*. J Math Sci 214, 44–68 (2016). https://doi.org/10.1007/s10958-016-2757-6

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