Market Microstructure Design and Flash Crashes: A Simulation Approach

Abstract

We study consequences of regulatory interventions in limit order markets that aim at stabilizing the market after an occurrence of a "flash crash". We use a simulation platform that creates random arrivals of trade orders, that allows us to analyze subtle theoretical features of liquidity and price variability under various market structures. The simulations are performed under continuous double-auction microstructure, and under alternatives, including imposing minimum resting times, shutting off trading for a period of time, and switching to call auction mechanisms. We find that the latter is the most effective in restoring the liquidity of the book and recovery of the price level. However, one has to be cautious about possible consequences of the intervention on the traders' strategies, including an undesirable slowdown of a convergence to a new equilibrium after a change in fundamentals.

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

“…In addition, longer expiration times create liquidity that reduces price variance in the market (Brewer, Cvitanic, and Plott, 2013). Lastly, by "slowing down" markets, minimum resting times may favor participation, especially if some traders (e.g., small retails investors) feel that high speed makes market unfair and hursts market integrity (see, for instance, Haldane, 2011).…”

“…Earlier empirical and theoretical works have already attempted to study the effect of different sets of regulatory measures (e.g., Westerhoff, 2008;Brewer, Cvitanic, and Plott, 2013;Vuorenmaa and Wang, 2014) and of some specific regulation policies such as financial transaction tax (Colliard and Hoffmann, 2013;Fricke and Lux, 2015;Lavicka, Lichard, and Novotny, 2014), minimum resting times (Hayes, Paddrik, Todd, Yang, Beling, and Scherer, 2012), market design (Budish, Cramton, and Shim, 2015), cancellation fee (Friederich and Payne, 2015), position limits (Lee, Cheng, and Koh, 2011). However, these works have either not considered the role of HFT (e.g., Westerhoff, 2008), or they have treated flash crashes as resulting from an exogenous shock (e.g., Brewer, Cvitanic, and Plott, 2013) or, finally, they have only focused on a very narrow set of policies (e.g., Hayes, Paddrik, Todd, Yang, Beling, and Scherer, 2012;Vuorenmaa and Wang, 2014).…”

“…However, these works have either not considered the role of HFT (e.g., Westerhoff, 2008), or they have treated flash crashes as resulting from an exogenous shock (e.g., Brewer, Cvitanic, and Plott, 2013) or, finally, they have only focused on a very narrow set of policies (e.g., Hayes, Paddrik, Todd, Yang, Beling, and Scherer, 2012;Vuorenmaa and Wang, 2014).…”

“…This model is particularly well-suited and relevant in this case because, differently from existing works (e.g., Brewer, Cvitanic, and Plott, 2013), it is able to endogenously generate flash crashes as the result of the interactions between low-and high-frequency (HF) traders. Moreover, compared to the existing literature we consider a broader set of policies, also of various nature.…”

Deep Reinforcement Learning in Agent Based Financial Market Simulation

“…In order to overcome such issues, we argue that artificial market simulation provides a promising avenue for improving the predictive ability of deep reinforcement learning models. Agent-based artificial market simulation is an established and widely studied methodology (Raberto et al 2001;Streltchenko et al 2005) that has been shown to be of practical significance as alternatives to real markets (Brewer et al 2013;Muranaga et al 1999;Raberto et al 2001). One of the main advantages of simulated markets is that they can be adapted to create realistic scenarios and regimes that have never been realized in the past (Lux and Marchesi 1999;Silva et al 2016).…”