Optimal trade execution: A mean quadratic variation approach

Forsyth, Peter; Kennedy, J. S.; Tse, Shu Tong; Windcliff, H.

doi:10.1016/j.jedc.2012.05.007

Cited by 141 publications

(92 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here, a model's parameters or probabilistic structure are often modified ( [61], [60], [65]). If a strategy and its performance are found to be sufficiently stable, one might be somewhat assured that model risks are contained.…”

Section: Background and Contributionsmentioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

View full text Add to dashboard Cite

Abstract. Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision, fundamental value shocks, and market fragmentation. We develop a mathematical model which captures aspects of the first three explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our mini-flash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission.Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents' opinions. In particular, every agent's beliefs will be expressly incorrect.As far as we know, this setup suggests both a new paradigm for modeling heterogeneous agent systems and a novel blueprint for understanding model misspecification risks in the context of optimal execution.

show abstract

Section: Background and Contributionsmentioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…For example, the so called "level-1 order book" contains the best price level of the order book, while "level-2 order book" provides the prices and quantities of the best five levels on both the ask and the bid sides. The following table is a typical example showing the dynamics of the limit order book of the top 5 levels: a market sell order of size 1200, followed by a limit ask order of size 400 at price 9.08, and then a cancellation of size 23 Table 1: A market sell order with size of 1200, a limit ask order with size of 400 at 9.08, and a cancelation of 23 shares of limit ask order at 9.10, in sequence.…”

Section: Limit Order Book (Lob)mentioning

confidence: 99%

“…Recall that the optimal execution problem is to slice big orders into smaller ones on a daily/weekly basis in order to minimize the price impact or to maximize some expected utility function; see for instance, Bertsima and Lo (1998) [10], Almgren andChriss (1999, 2000) [5,6], Almgren (2003) [4], Almgren and Lorenz(2007) [7], Schied and Schöneborn (2009) [47], Weiss (2009) [50], Alfonsi, Fruth, and Schied (2010) [1], Gatheral (2010) [24], Predoiu, Shaiket, and Shreve (2010) [43], Schied, Schöneborn, and Tehranchi (2010) [48], and Alfonsi, Schied, and Slynko (2012) [2], Gatheral and Schied (2011) [25], Forsyth, Kennedy, Tse, and Windcliff (2011) [23], and Guo and Zervos (2012) [30], and Obizhaeva and Wang (2013) [42]. Optimal placement problem, on the other hand, is on a smaller (10-100 seconds) time scale and mostly for different type of (i.e., HFT) traders (see Kirilenko et.…”

Section: Limit Order Book (Lob)mentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Larrard

Ruan

2013

SSRN Journal

View full text Add to dashboard Cite

This paper proposes and studies an optimal placement problem in a limit order book. Two simple models are proposed: one with price impact and one without price impact. For the first model, optimal placement strategies for both single-period and multi-period cases are derived. For the second model, it is shown that the optimal strategy never mixes market orders and (best) limit orders; in particular, with special choices of parameters, the optimal strategy reduces to the VWAP type of Bertsimas and Lo (1998) for the optimal execution problem.

show abstract

“…Later on, the notion of price manipulation is developed as analog of no arbitrage for derivative pricing. Standard references for these models with additive and possible nonlinear price impact include Huberman and Stanzl [40], Obizhaeva and Wang [50], Schied and Schöneborn [57], Schied et al [58], Almgren and Lorenz [6], Alfonsi et al [1,2], Gatheral [27], Predoiu et al [55], Schied et al [58], Weiss [62], and works involving the geometric Brownian motion and/or multiplicative price impact include Gatheral and Schied [28], Forsyth et al [23], and Guo and Zervos [33].…”

Section: Optimal Placement Vs Optimal Executionmentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Guo¹

2013

Theory Driven by Influential Applications

View full text Add to dashboard Cite

Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract Algorithmic trading refers to the automatic and rapid trading of large quantities with orders specified and implemented by an algorithm. Roughly speaking, algorithmic trading is based on two different time scales: the daily or weekly scale and a smaller (10-to 100-second) time scale. These two time scales essentially reflect the two steps by which the traders slice and place orders. The first step is to optimally slice big orders into smaller ones on a daily basis with the goal to minimize the price impact and/or to maximize the expected utility; the second step is to optimally place the orders within seconds. The former is the well-known optimal execution problem, and the latter is the much less studied optimal placement problem. This paper reviews several simple models and approaches for the optimal placement problem. Several most relevant statistical issues are presented, together with a brief discussion on the key differences between the system of limit order book and the multiclass queues with reneging.

show abstract

Optimal trade execution: A mean quadratic variation approach

Cited by 141 publications

References 22 publications

Mini-Flash Crashes, Model Risk, and Optimal Execution

Mini-Flash Crashes, Model Risk, and Optimal Execution

Optimal Placement in a Limit Order Book

Optimal Placement in a Limit Order Book

Contact Info

Product

Resources

About