Disentangling and quantifying market participant volatility contributions

References

• Abergel, F. and Jedidi, A., A mathematical approach to order book modelling. Int. J. Theoretical Appl. Financ., 2013, 16, 1–40. doi: 10.1142/S0219024913500258
   Google Scholar
• Achab, M., Bacry, E., Gaïffas, S., Mastromatteo, I. and Muzy, J., Uncovering causality from multivariate Hawkes integrated cumulants. In Proceedings of the International Conference on Machine Learning, Sydney, Australia, 2017.
   Google Scholar
• Achab, M., Bacry, E., Muzy, J. and Rambaldi, M., Analysis of order book flows using a non-parametric estimation of the branching ratio matrix. Quant. Finance, 2018, 18(2), 199–212. doi: 10.1080/14697688.2017.1403132
   Web of Science ®Google Scholar
• Bacry, E. and Muzy, J.-F., Hawkes model for price and trades high-frequency dynamics. Quant. Finance, 2014, 14(7), 1147–1166. doi: 10.1080/14697688.2014.897000
   Web of Science ®Google Scholar
• Bacry, E. and Muzy, J.-F., First-and second-order statistics characterization of Hawkes processes and non-parametric estimation. IEEE Trans. Inf. Theory, 2016, 62(4), 2184–2202. doi: 10.1109/TIT.2016.2533397
   Web of Science ®Google Scholar
• Bacry, E., Delattre, S., Hoffmann, M. and Muzy, J.-F., Modelling microstructure noise with mutually exciting point processes. Quant. Finance, 2013, 13(1), 65–77. doi: 10.1080/14697688.2011.647054
   Web of Science ®Google Scholar
• Bacry, E., Mastromatteo, I. and Muzy, J.F., Hawkes processes in finance. Market Microstructure and Liquidity, 2015, 1(1), 1550005. doi: 10.1142/S2382626615500057
   Web of Science ®Google Scholar
• Bacry, E., Jaisson, T. and Muzy, J.F., Estimation of slowly decreasing Hawkes kernels: Application to high-frequency order book dynamics. Quant. Finance, 2016, 16(8), 1179–1201. doi: 10.1080/14697688.2015.1123287
   Web of Science ®Google Scholar
• Bacry, E., Bompaire, M., Gaïas, S. and Poulsen, S., tick: A Python library for statistical learning, with a particular emphasis on time-dependent modeling. ArXiv e-prints, 2017.
   Google Scholar
• Bauwens, L. and Hautsch, N., Modelling financial high frequency data using point processes. In Handbook of Financial Time Series, edited by T. Mikosch, J.-P. Kreiß, R.A. Davis, and T.G. Andersen, pp. 953–979, 2009 (Springer: Berlin, Heidelberg).
   Google Scholar
• Bellia, M., High frequency market making: Liquidity provision, adverse selection, and competition. Available at SSRN, 2017.
   Google Scholar
• Biais, B. and Foucault, T., HFT and market quality. Bankers, Markets & Investors, 2014, 128, 5–19.
   Google Scholar
• Bouchaud, J.-P., Agent-based models for market impact and volatility. In Handbook of Computational Economics, Volume 4, Chapter 7, edited by Cars Hommes and Blake LeBaron, pp. 393–436, 2018 (Elsevier: Amsterdam).
   Google Scholar
• Bouchaud, J., Donier, J. and Gould, M., Trades, Quotes and Prices, Financial Markets Under the Microscope, 2018 (Cambridge University Press: Cambridge).
   Google Scholar
• Brogaard, J., Hendershott, T. and Riordan, R., High-frequency trading and price discovery. Rev. Financ. Stud., 2014, 27(8), 2267–2306. doi: 10.1093/rfs/hhu032
   Web of Science ®Google Scholar
• Cartea, A., Jaimungal, S. and Ricci, J., Buy low, sell high: A high frequency trading perspective. SIAM J. Financ. Math., 2014, 5(1), 415–444. doi: 10.1137/130911196
   Web of Science ®Google Scholar
• Cartea, A., Jaimungal, S. and Ricci, J., Algorithmic trading, stochastic control, and mutually exciting processes. SIAM Rev., 2018, 60(3), 673–703. doi: 10.1137/18M1176968
   Web of Science ®Google Scholar
• Cont, R., Stoikov, S. and Talreja, R., A stochastic model for order book dynamics. Oper. Res., 2010, 58(3), 549–563. doi: 10.1287/opre.1090.0780
   Web of Science ®Google Scholar
• ESMA Economic Report No. 1, High-frequency trading activity in EU equity markets, 2014.
   Google Scholar
• Filimonov, V. and Sornette, D., Quantifying reflexivity in financial markets: Toward a prediction of flash crashes. Phys. Rev. E, 2012, 85(5), 056108. doi: 10.1103/PhysRevE.85.056108
   Web of Science ®Google Scholar
• Foucault, T., Order flow composition and trading costs in a dynamic limit order market. J. Financ. Market, 1999, 2, 99–134. doi: 10.1016/S1386-4181(98)00012-3
   Google Scholar
• Hagströmer, B. and Nordén, L., The diversity of high-frequency traders. J. Financ. Markets, 2013, 16(4), 741–770. doi: 10.1016/j.finmar.2013.05.009
   Web of Science ®Google Scholar
• Hansen, N.R., Reynaud-Bouret, P. and Rivoirard, V., Lasso and probabilistic inequalities for multivariate point processes. Bernoulli, 2015, 21(1), 83–143. doi: 10.3150/13-BEJ562
   Web of Science ®Google Scholar
• Hardiman, S.J., Bercot, N. and Bouchaud, J.-P., Critical reflexivity in financial markets: A Hawkes process analysis. Eur. Phys. J. B, 2013, 86(10), 442. doi: 10.1140/epjb/e2013-40107-3
   Web of Science ®Google Scholar
• Hongteng, X., Mehrdad, F. and Hongyuan, Z., Learning Granger causality for Hawkes processes. In Proceedings of The 33rd International Conference on Machine Learning, edited by M.F. Balcan and K.Q. Weinberger, Volume 48 of Proceedings of Machine Learning Research, New York, USA, pp. 1717–1726, 2016 (PMLR).
   Google Scholar
• Huang, W., Lehalle, C.A. and Rosenbaum, M., Simulating and analyzing order book data: The queue-reactive model. J. Amer. Statist. Assoc., 2015, 110, 107–122. doi: 10.1080/01621459.2014.982278
   Web of Science ®Google Scholar
• Jovanović, S., Hertz, J. and Rotter, S., Cumulants of Hawkes point processes. Phys. Rev. E, 2015, 91(4), 042802. doi: 10.1103/PhysRevE.91.042802
   Web of Science ®Google Scholar
• Kirilenko, A., Kyle, A.S., Samadi, M. and Tuzun, T., The flash crash: High-frequency trading in an electronic market. J. Finance, 2017, 72(3), 967–998. doi: 10.1111/jofi.12498
   Web of Science ®Google Scholar
• Large, J., Measuring the resiliency of an electronic limit order book. J. Financ. Markets, 2007, 10(1), 1–25. doi: 10.1016/j.finmar.2006.09.001
   Web of Science ®Google Scholar
• Liu, L.Y., Patton, A.J. and Sheppard, K., Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. J. Econom., 2015, 187(1), 293–311. doi: 10.1016/j.jeconom.2015.02.008
   Web of Science ®Google Scholar
• Lu, X. and Abergel, F., Limit order book modelling with high dimensional Hawkes processes. Available at HAL, 2017.
   Google Scholar
• Megarbane, N., Lehalle, C.-A., Rosenbaum, M. and Saliba, P., The behaviour of high-frequency traders under different market stress scenarios. Available at SSRN, 2017.
   Google Scholar
• Menkveld, A.J., High frequency trading and the new market makers. J. Financ. Markets, 2013, 16(4), 712–740. doi: 10.1016/j.finmar.2013.06.006
   Web of Science ®Google Scholar
• Parlour, C.A., Price dynamics in limit order markets. Rev. Financ. Stud., 1998, 11, 789–816. doi: 10.1093/rfs/11.4.789
   Web of Science ®Google Scholar
• Rambaldi, M., Bacry, E. and Lillo, F., The role of volume in order book dynamics: A multivariate Hawkes process analysis. Quant. Finance, 2017, 17(7), 999–1020. doi: 10.1080/14697688.2016.1260759
   Web of Science ®Google Scholar
• Rosu, I., A dynamic model of the limit order book. Rev. Financ. Stud., 2009, 22, 4601–4641. doi: 10.1093/rfs/hhp011
   Web of Science ®Google Scholar
• Smith, E., Farmer, J.D., Gillemot, L. and Krishnamurthy, S., Statistical theory of the continuous double auction. Quant. Finance, 2003, 3, 481–514. doi: 10.1088/1469-7688/3/6/307
   Web of Science ®Google Scholar
• Soros, G., The Alchemy of Finance, 2003 (John Wiley & Sons: Hoboken, NJ).
   Google Scholar
• Yang, S.Y., Qiao, Q., Beling, P.A., Scherer, W.T. and Kirilenko, A.A., Gaussian process-based algorithmic trading strategy identification. Quant. Finance, 2015, 15(10), 1683–1703. doi: 10.1080/14697688.2015.1011684
   Web of Science ®Google Scholar