Block-Crossing Networks and The Value ofNatural Liquidity

Марков, В. А.; Ingargiola, Tito

doi:10.3905/jot.2013.8.3.016

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Large institutional orders are often sent to opportunistic liquidity-seeking algorithms that harness liquidity from exchanges and dark pools [Markov and Ingargiola, 2013].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Марков¹

2019

Preprint

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We present a formulation of transaction cost analysis (TCA) in the Bayesian framework for the primary purpose of comparing broker algorithms using standardized benchmarks. Our formulation allows effective calculation of the expected value of trading benchmarks with only a finite sample of data relevant to practical applications. We discuss the nature of distribution of implementation shortfall, volume-weighted average price, participation-weighted price and short-term reversion benchmarks. Our model takes into account fat tails, skewness of the distributions and heteroscedasticity of benchmarks. The proposed framework allows the use of hierarchical models to transfer approximate knowledge from a large aggregated sample of observations to a smaller sample of a particular algorithm.

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“…Large institutional orders are often sent to opportunistic liquidity-seeking algorithms that harness liquidity from exchanges and dark pools [Markov and Ingargiola, 2013].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Марков¹

2019

Preprint

Self Cite

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“…This means that once a trade has occurred in a dark pool, the probability of observing another one increases. See, e.g., Buti et al (2011), Chapter 3 in Lehalle and Laruelle (2013) and Markov and Ingargiola (2013) for details. Various market events can lead to trade clustering in dark pools.…”

Section: Introductionmentioning

confidence: 99%

“…In the dark pool literature, our work is closely related to studies including Markov and Ingargiola (2013) and Afeche et al (2014). The paper Markov and Ingargiola (2013) from the industry explicitly modeled the clustering of trade arrivals in a dark pool using the classical Hawkes process with n ≡ 1. They discussed estimation of this classical Hawkes model using exponential exciting functions.…”

Section: Introductionmentioning

confidence: 99%

Transform Analysis for Hawkes Processes with Applications in Dark Pool Trading

Gao

Zhou

Zhu

2017

Preprint

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Hawkes processes are a class of simple point processes that are self-exciting and have clustering effect, with wide applications in finance, social networks and many other fields. This paper considers a self-exciting Hawkes process where the baseline intensity is time-dependent, the exciting function is a general function and the jump sizes of the intensity process are independent and identically distributed non-negative random variables. This Hawkes model is non-Markovian in general. We obtain closedform formulas for the Laplace transform, moments and the distribution of the Hawkes process. To illustrate the applications of our results, we use the Hawkes process to model the clustered arrival of trades in a dark pool and analyze various performance metrics including time-to-first-fill, time-to-complete-fill and the expected fill rate of a resting dark order.

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