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References and Appendices

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Abstract, Acknowledgements, and Statements and Declarations

  1. Introduction

  2. Background and Related Work

    2.1 Agent-based Financial Market simulation

    2.2 Flash Crash Episodes

  3. Model Structure and 3.1 Model Set-up

    3.2 Common Trader Behaviours

    3.3 Fundamental Trader (FT)

    3.4 Momentum Trader (MT)

    3.5 Noise Trader (NT)

    3.6 Market Maker (MM)

    3.7 Simulation Dynamics

  4. Model Calibration and Validation and 4.1 Calibration Target: Data and Stylised Facts for Realistic Simulation

    4.2 Calibration Workflow and Results

    4.3 Model Validation

  5. 2010 Flash Crash Scenarios and 5.1 Simulating Historical Flash Crash

    5.2 Flash Crash Under Different Conditions

  6. Mini Flash Crash Scenarios and 6.1 Introduction of Spiking Trader (ST)

    6.2 Mini Flash Crash Analysis

    6.3 Conditions for Mini Flash Crash Scenarios

  7. Conclusion and Future Work

    7.1 Summary of Achievements

    7.2 Future Works

References and Appendices

References

Borkovec, M., Domowitz, I., Serbin, V., and Yegerman, H. (2010). Liquidity and price discovery in exchange-traded funds: One of several possible lessons from the flash crash. The Journal of Index Investing, 1(2):24–42.


Byron, M. Y., Shenoy, K. V., and Sahani, M. (2004). Derivation of kalman filtering and smoothing equations. In Technical report. Stanford University.


Chiarella, C. (1992). The dynamics of speculative behaviour. Annals of operations research, 37(1):101–123.


Cont, R. (2007). Volatility clustering in financial markets: Empirical facts and agent-based models. In Long memory in economics, pages 289–309. Springer.


Darley, V. and Outkin, A. (2007). A NASDAQ market simulation: insights on a major market from the science of complex adaptive systems, volume 1. World Scientific.


Franke, R. and Westerhoff, F. (2012). Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control, 36(8):1193–1211.


Gode, D. K. and Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of political economy, 101(1):119–137.


Golub, A., Keane, J., and Poon, S. (2012). High frequency trading and mini flash crashes. Available at SSRN 2182097.


Hayes, R., Todd, A., Chaidarun, N., Tepsuporn, S., Beling, P., and Scherer, W. (2014). An agent-based financial simulation for use by researchers. In Proceedings of the Winter Simulation Conference 2014, pages 300–309.


Hill, J. B. (2010). On tail index estimation for dependent, heterogeneous data. Econometric Theory, 26(5):1398–1436.


Johnson, N., Zhao, G., Hunsader, E., Meng, J., Ravindar, A., Carran, S., and Tivnan, B. (2012). Financial black swans driven by ultrafast machine ecology. arXiv preprint arXiv:1202.1448.


Karvik, G., Noss, J., Worlidge, J., and Beale, D. (2018). The deeds of speed: an agent-based model of market liquidity and flash episodes. Bank of England Working Paper.


Kirilenko, A., Kyle, A. S., Samadi, M., and Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3):967–998.


Kyle, A. S. and Obižaeva, A. (2020). Large bets and stock market crashes. CEFIR.


Lamperti, F., Roventini, A., and Sani, A. (2018). Agent-based model calibration using machine learning surrogates. Journal of Economic Dynamics and Control, 90:366–389.


LeBaron, B. (2001). A builder’s guide to agent-based financial markets. Quantitative finance, 1:254–261.


Madhavan, A. (2012). Exchange-traded funds, market structure, and the flash crash. Financial Analysts Journal, 68(4):20–35.


Majewski, A., Ciliberti, S., and Bouchaud, J. P. (2020). Co-existence of trend and value in financial markets: Estimating an extended chiarella model. Journal of Economic Dynamics and Control, 112:103791.


McGroarty, F., Booth, A., Gerding, E., and Chinthalapati, V. R. (2019). High frequency trading strategies, market fragility and price spikes: An agent based model perspective. Annals of Operations Research, 282(1):217–244.


Menkveld, A. (2013). High frequency trading and the new market makers. Journal of financial Markets, 16(4):712–740.


Menkveld, A. and Yueshen, B. Z. (2019). The flash crash: A cautionary tale about highly fragmented markets. Management Science, 65(10):4470–4488.


O’Hara, M. (1995). Market Microstructure Theory. Wiley.


Paddrik, M., Hayes, R., Scherer, W., and Beling, P. (2017). Effects of limit order book information level on market stability metrics. Journal of Economic Interaction and Coordination, 12(2):221–247.


Paddrik, M., Hayes, R., Todd, A., Yang, S., Beling, P., and Scherer, W. (2012). An agent based model of the e-mini s&p 500 applied to flash crash analysis. In 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr), pages 1–8.


Paulin, J., Calinescu, A., and Wooldridge, M. (2019). Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach. Journal of Economic Dynamics and Control, 100:200–229.


Ralaivola, L. and d’Alche Buc, F. (2005). Time series filtering, smoothing and learning using the kernel kalman filter. Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 3:1449–1454.


SEC and CFTC (2010). Findings regarding the market events of may 6, 2010. Washington DC.


Sewell, M. (2011). Characterization of financial time series. Research Note, 11(01):01.


Todd, A., Beling, P., Scherer, W., and Yang, S. Y. (2016). Agent-based financial markets: A review of the methodology and domain. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–5.


Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, ˙I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261–272.

Appendices

A Descriptions for All Model Parameters


Table 5 Descriptions for All Parameters involved in the proposed agent-based model


B Values for Fixed Model Parameters in Calibration


Table 6 Values for fixed model parameters in calibration


C Values for Model Parameters in 2010 Flash Crash Simulation


Table 7 Values for model parameters in 2010 Flash Crash simulation


Authors:

(1) Kang Gao, Department of Computing, Imperial College London, London SW7 2AZ, UK and Simudyne Limited, London EC3V 9DS, UK ([email protected]);

(2) Perukrishnen Vytelingum, Simudyne Limited, London EC3V 9DS, UK;

(3) Stephen Weston, Department of Computing, Imperial College London, London SW7 2AZ, UK;

(4) Wayne Luk, Department of Computing, Imperial College London, London SW7 2AZ, UK;

(5) Ce Guo, Department of Computing, Imperial College London, London SW7 2AZ, UK.


This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.


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