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10 - Long-Range Persistence of Order Flow

from PART IV - CLUSTERING AND CORRELATIONS

Published online by Cambridge University Press:  26 February 2018

Jean-Philippe Bouchaud ,
Julius Bonart ,
Jonathan Donier  and
Martin Gould
Jean-Philippe Bouchaud
Affiliation:
Capital Fund Management, Paris
Julius Bonart
Affiliation:
University College London
Jonathan Donier
Affiliation:
Capital Fund Management
Martin Gould
Affiliation:
CFM - Imperial Institute of Quantitative Finance
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Type
Chapter
Information
Trades, Quotes and Prices
Financial Markets Under the Microscope
 , pp. 187 - 204
Publisher: Cambridge University Press
Print publication year: 2018

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References

Jacobs, P. A., & Lewis, P. A. (1978). Discrete time series generated by mixtures. I: Correlational and runs properties. Journal of the Royal Statistical Society. Series B (Methodological), 40, 94–105.Google Scholar
Jacobs, P. A., & Lewis, P. A. (1978). Discrete time series generated by mixtures. III. Autoregressive processes (DAR (p)). Naval Postgraduate School Technical Report (Monterey, CA).Google Scholar
Jacobs, P. A., & Lewis, P. A. (1983). Stationary discrete autoregressive-moving average time series generated by mixtures. Journal of Time Series Analysis, 4(1), 19–36.CrossRefGoogle Scholar
Raftery, A. E. (1985). A model for high-order Markov chains. Journal of the Royal Statistical Society. Series B (Methodological), 47, 528–539.Google Scholar
Beran, J. (1994). Statistics for long-memory processes. Chapman & Hall.Google Scholar
Berchtold, A., & Raftery, A. E. (2002). The mixture transition distribution model for high-order Markov chains and non-Gaussian time series. Statistical Science, 17, 328–356.Google Scholar
Lillo, F., Mike, S., & Farmer, J. D. (2005). Theory for long memory in supply and demand. Physical Review E, 71(6), 066122.CrossRefGoogle ScholarPubMed
Taranto, D. E., Bormetti, G., Bouchaud, J. P., Lillo, F., & Tóth, B. (2016). Linear models for the impact of order flow on prices II. The Mixture Transition Distribution model. https://ssrn.com/abstract=2770363.
Bouchaud, J. P., Gefen, Y., Potters, M., & Wyart, M. (2004). Fluctuations and response in financial markets: The subtle nature of random price changes. Quantitative Finance, 4(2), 176–190.CrossRefGoogle Scholar
Lillo, F., & Farmer, J. D. (2004). The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics, 8(3), 1.Google Scholar
Bouchaud, J. P., Kockelkoren, J., & Potters, M. (2006). Random walks, liquidity molasses and critical response in financial markets. Quantitative Finance, 6(02), 115–123.CrossRefGoogle Scholar
Bouchaud, J. P., Farmer, J. D., & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Hens, T. & Schenk-Hoppe, K. R. (Eds.), Handbook of financial markets: Dynamics and evolution. North-Holland, Elsevier.Google Scholar
Yamamoto, R., & Lebaron, B. (2010). Order-splitting and long-memory in an order-driven market. The European Physical Journal B-Condensed Matter and Complex Systems, 73(1), 51–57.CrossRefGoogle Scholar
Tóth, B., Palit, I., Lillo, F., & Farmer, J. D. (2015). Why is equity order flow so persistent? Journal of Economic Dynamics and Control, 51, 218–239.CrossRefGoogle Scholar
Taranto, D. E., Bormetti, G., Bouchaud, J. P., Lillo, F., & Tóth, B. (2016). Linear models for the impact of order flow on prices I. Propagators: Transient vs. history dependent impact. https://ssrn.com/abstract=2770352.
Chan, L. K., & Lakonishok, J. (1993). Institutional trades and intra-day stock price behavior. Journal of Financial Economics, 33(2), 173–199.CrossRefGoogle Scholar
Chan, L. K., & Lakonishok, J. (1995). The behavior of stock prices aroundinstitutional trades. The Journal of Finance, 50(4), 1147–1174.CrossRefGoogle Scholar
Gabaix, X., Ramalho, R., & Reuter, J. (2003). Power laws and mutual fund dynamics. MIT mimeo.Google Scholar
Gabaix, X., Gopikrishnan, P., Plerou, V., & Stanley, H. E. (2006). Institutional investors and stock market volatility. The Quarterly Journal of Economics, 121(2), 461–504.CrossRefGoogle Scholar
Vaglica, G., Lillo, F., Moro, E., & Mantegna, R. (2008). Scaling laws of strategic behavior and size heterogeneity in agent dynamics. Physical Review E, 77, 0036110.CrossRefGoogle ScholarPubMed
Schwarzkopf, Y., & Farmer, J. D. (2010). Empirical study of the tails of mutual fund size. Physical Review E, 81(6), 066113.CrossRefGoogle ScholarPubMed
Bershova, N., & Rakhlin, D. (2013). The non-linear market impact of large trades: Evidence from buy-side order flow. Quantitative Finance, 13(11), 1759–1778.CrossRefGoogle Scholar
Donier, J., & Bonart, J. (2015). A million metaorder analysis of market impact on the Bitcoin. Market Microstructure and Liquidity, 1(02), 1550008.CrossRefGoogle Scholar
Kyle, A. S., & Obizhaeva, A. A. (2016). Large bets and stock market crashes. https:// ssrn.com/abstract=2023776.
Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High frequency trading in an electronic market. The Journal of Finance, 72, 967–998.CrossRefGoogle Scholar

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