A multi agent model for the limit order book dynamics

Bartolozzi, Maria Letizia

doi:10.1140/epjb/e2010-10406-4

Cited by 21 publications

(13 citation statements)

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“…Price determination . Similar to [ 34 ], the price set by the i -th agent is distributed around the bid (in case of a buy order) or ask price (in case of a sell order). Namely, where and are the bid and ask price before agent i participates to the k -th trading session, and η i ( k ) is the realization of a log-normal random variable with meta-parameters defined as in [ 34 ], that is, with mean 7 and standard deviation 10; the shift parameter m is the median of the distribution, and regulates the tradeoff between market and limit orders.…”

Section: Methodsmentioning

confidence: 99%

“…Similar to [ 34 ], the price set by the i -th agent is distributed around the bid (in case of a buy order) or ask price (in case of a sell order). Namely, where and are the bid and ask price before agent i participates to the k -th trading session, and η i ( k ) is the realization of a log-normal random variable with meta-parameters defined as in [ 34 ], that is, with mean 7 and standard deviation 10; the shift parameter m is the median of the distribution, and regulates the tradeoff between market and limit orders. The log-normal distribution is selected in accordance with the empirical observations on the order distribution in the book: counter-intuitively, submitted orders are not always market orders or limit orders slightly above the best quote, but may also strongly deviate from (or ) [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…Price determination. Similar to [34], the price set by the i-th agent is distributed around the bid (in case of a buy order) or ask price (in case of a sell order…”

Section: Plos Onementioning

confidence: 99%

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Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market

2020

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Herding has often been blamed as one of the possible causes of market instabilities, ultimately yielding to bubbles and crushes. On the other hand, researchers hypothesized that financial systems may benefit from the so-called wisdom of the crowd. To solve this apparent dichotomy, we leverage a novel financial market model, where the agents form their expectations by combining their individual return estimation with the expectations of their neighbors. By establishing a link between herding, sociality, and market instabilities, we point out that the emergence of collective decisions in the market is not necessarily detrimental. Indeed, when all the agents tend to conform their expectations to those of one or few leaders, herding might dramatically reduce market efficiency. However, when each agent accounts for a plurality of opinions, thus following the wisdom of the crowd, market dynamics become efficient. Following these observations, we propose two alternative control strategies to reduce market instability and enhance its efficiency.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market

2020

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“…Agents trade an asset for T periods and transactions are executed through a limit-order book (LOB) where the information about the type, the size and the price of all agents' orders is stored (see, for instance, Maslov, 2000;Zovko and Farmer, 2002;Avellaneda and Stoikov, 2008;Bartolozzi, 2010). The market is populated by two groups of agents depending on their trading frequency (i.e., the average amount of time elapsed between two order 4 placements), namely N L low-frequency (LF) and N H high-frequency (HF) traders (N = N L + N H ).…”

Section: The Modelmentioning

confidence: 99%

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

Leal¹,

Napoletano

2019

Journal of Economic Behavior & Organization

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low-and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of flash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes flash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-off between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off.

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“…So far, the few existing agent-based models dealing with HFT have mainly treated HF as zero-intelligence agents with an exogenously-given trading frequency [6,11]. However, only few attempts have been made to account for the interplay between HF and LF traders [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

Rock around the clock: An agent-based model of low- and high-frequency trading

et al. 2015

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Abstract. We build an agent-based model to study how the interplay between low-and highfrequency trading affects asset price dynamics. Our main goal is to investigate whether highfrequency trading exacerbates market volatility and generates flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of highfrequency traders, i.e., their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.

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A multi agent model for the limit order book dynamics

Cited by 21 publications

References 43 publications

Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market

Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market

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

Rock around the clock: An agent-based model of low- and high-frequency trading

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