Abstract
We present some simulation results on various mono- and multi-asset market making strategies. Starting with a zero-intelligence market, we gradually enhance the model by taking into account such properties as the autocorrelation of trade signs, or the existence of informed traders. We then use Monte Carlo simulations to study the effects of those properties on some elementary market making strategies. Finally, we present some possible improvements of the strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chakraborti, A., Toke, I.M., Patriarca, M., Abergel, F.: Econophysics: Empirical facts and agent-based models (2009.http://arxiv.org/abs/0909.1974.). URL http://arxiv.org/abs/0909. 1974
Chan, E.P.: Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley Trading) (2008)
Epps, T.W.: Comovements in stock prices in the very short-run. Journal of the American Statistical Association 74 (1979). URL http://www.jstor.org/pss/2286325
Farmer, J.D., Patelli, P., Zovko, I.I.: The predictive power of zero intelligence in financial markets. Quantitative Finance Papers. cond-mat/0309233, arXiv.org (2003). URL http://ideas.repec.org/p/arx/papers/cond-mat-0309233.html
Huth, N., Abergel, F.: The Times Change: Multivariate Subordination, Empirical Facts. SSRN eLibrary (2009. [to be published in Quantitative Finance Papers])
Lillo, F., Farmer, J.D.: The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics 8(3) (2004). URL http://ideas.repec.org/a/bpj/sndecm/v8y2004i3n1. html
Mike, S., Farmer, J.D.: An empirical behavioral model of liquidity and volatility. Quantitative finance papers, arXiv.org (2007). URL http://econpapers.repec.org/RePEc:arx:papers:0709.0159
Preis, T., Golke, S., Paul, W., Schneider, J.J.: Statistical analysis of financial returns for a multiagent order book model of asset trading. Phys Rev E Stat Nonlin Soft Matter Phys 76(1 Pt 2), 016,108 (2007)
Qi, Z.: Simulation of multi-agents in financial market (2009). Done under the supervision of Abergel, Frederic
Toke, I.M.: “market making” behaviour in an order book model and its impact on the bid-ask spread. Quantitative finance papers, arXiv.org (2010). URL http://econpapers.repec.org/RePEc:arx: papers:1003.3796
Voss, R.F.: Fractals in nature: from characterization to simulation pp. 21–70 (1988)
Wyart, M., Bouchaud, J.P., Kockelkoren, J., Potters, M., Vettorazzo, M.: Relation between bid-ask spread, impact and volatility in order-driven markets. Quantitative Finance 8(1), 41–57 (2008). URL http://ideas.repec.org/a/taf/quantf/v8y2008i1p41-57.html
Bacry, E.: Modeling microstructure noise using point processes, Econophys-Kolkata V Conference, submitted to Quantitative finance papers
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Italia
About this chapter
Cite this chapter
Foata, L., Vidhamali, M., Abergel, F. (2011). Multi-Agent Order Book Simulation: Mono- and Multi-Asset High-Frequency Market Making Strategies. In: Abergel, F., Chakrabarti, B.K., Chakraborti, A., Mitra, M. (eds) Econophysics of Order-driven Markets. New Economic Windows. Springer, Milano. https://doi.org/10.1007/978-88-470-1766-5_10
Download citation
DOI: https://doi.org/10.1007/978-88-470-1766-5_10
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-1765-8
Online ISBN: 978-88-470-1766-5
eBook Packages: Business and EconomicsEconomics and Finance (R0)