The Anatomy of a Flash Crash, Modeled to the Millisecond

Table of Links

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

7 Conclusion and Future Work

7.1 Summary of Achievements

A novel high-frequency agent-based financial market simulator is implemented to generate a realistic high-frequency simulated financial market. Each simulation step corresponds to 100 milliseconds in the real-world trading environment. Full exchange protocols (limit order books) are implemented to simulate the order matching process. In this way, we provide a microstructure model of a single security traded on a central limit order book in which market participants follow fixed behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the simulation is capable of reproducing realistic stylised facts in financial markets.

The simulator is then employed to explore the dynamics during flash crash episodes and the conditions that affect flash crash episodes. Under the framework of the proposed high-frequency agent-based financial market simulator, the 2010 Flash Crash is realistically simulated by introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010. We investigate the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. We then explore the conditions that could influence the characteristics of the 2010 Flash Crash. It is found that three conditions significantly affect the amplitude of the 2010 Flash Crash: the percentage of volume of the Sell Algorithm, market maker inventory limit, and the trading frequency of fundamental traders. In particular, we found that the relationship between the amplitude of the simulated 2010 Flash Crash and the POV of the Sell Algorithm is not monotonous, and so is the relationship between the amplitude and the market maker inventory limit. For the trading frequency of fundamental traders, the higher the frequency, the smaller the amplitude of the simulated 2010 Flash Crash.

Similar analysis is carried out for mini flash crash events. An innovative type of trader called “Spiking Trader” is introduced to the agent-based financial market simulator, creating more price shock and precipitating more mini flash crash events. Market dynamics for a typical simulated mini flash crash event are analysed. We also explore the conditions that could influence the characteristics of mini flash crash events. Experimental results show that market maker inventory limit significantly affects both the frequency and amplitude of mini flash crash events. However, the trading frequency of fundamental traders shows no obvious influence on mini flash crash events in our experiments.

7.2 Future Work

During the 2010 Flash Crash, there are lots of cross-market arbitrageurs who transferred the selling pressure to the equities markets by opportunistically buying E-Mini contracts and simultaneously selling products like SPY. This cross-market mechanism has not yet been implemented in our agent-based modelling framework. One future direction is to implement simulated markets for two correlated securities and explore the contagion during stressed scenarios. Another extension is to use the proposed agent-based financial market simulation framework for examining how regulatory policy interventions could influence the current market dynamics. For example, whether a circuit breaker in the market would help stabilize financial markets and curb the severity of flash crash scenarios. Last but not the least, an examination of possible indicators of an imminent flash crash event is another interesting future extension of this work.

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Appendices

A Descriptions for All Model Parameters

B Values for Fixed Model Parameters in Calibration

C Values for Model Parameters in 2010 Flash Crash Simulation