Optimal Execution Strategy in Liquidity Framework Under Exponential Temporary Market Impact

2017 ◽  
pp. 251-265
Author(s):  
Chiara Benazzoli ◽  
Luca Di Persio
Keyword(s):  
Market Impact ◽  
Optimal Execution ◽  
Execution Strategy

scholarly journals A dynamic optimal execution strategy under stochastic price recovery

2015 ◽  
Vol 02 (04) ◽  
pp. 1550025 ◽  
Author(s):  
Masashi Ieda
Keyword(s):  
Order Book ◽  
Market Impact ◽  
Time Step ◽  
Limit Order ◽  
Optimal Execution ◽  
Execution Strategy ◽  
Quasi Variational Inequality ◽  
Finite Time Horizon ◽  
Hamilton Jacobi Bellman ◽  
Optimal Execution Problem

In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the market impact. We include not only the market order, but also the limit order in our strategy in a restricted fashion. We formulate the problem as a combined stochastic control problem over a finite time horizon. The corresponding Hamilton–Jacobi–Bellman quasi-variational inequality is solved numerically. The optimal strategy obtained consists of three components: (i) the initial large trade; (ii) the unscheduled small trades during the period; (iii) the terminal large trade. The size and timing of the trade is governed by the tolerance for market impact depending on the state at each time step, and hence the strategy behaves dynamically. We also provide competitive results due to inclusion of the limit order, even though a limit order is allowed under conservative evaluation of the execution price.

On Optimal Options Book Execution Strategies with Market Impact

2016 ◽  
Vol 02 (03n04) ◽  
pp. 1750002
Author(s):  
Aymeric Kalife ◽  
Saad Mouti
Keyword(s):  
Implied Volatility ◽  
Option Price ◽  
Spot Price ◽  
Market Impact ◽  
Impact Function ◽  
Optimal Execution ◽  
Execution Strategy ◽  
Variance Risk ◽  
Hamilton Jacobi Bellman ◽  
Mean Variance

We consider the optimal execution of a book of options when market impact is a driver of the option price. We aim at minimizing the mean-variance risk criterion for a given market impact function. First, we develop a framework to justify the choice of our market impact function. Our model is inspired from Leland’s option replication with transaction costs where the market impact is directly part of the implied volatility function. The option price is then expressed through a Black– Scholes-like PDE with a modified implied volatility directly dependent on the market impact. We set up a stochastic control framework and solve an Hamilton–Jacobi–Bellman equation using finite differences methods. The expected cost problem suggests that the optimal execution strategy is characterized by a convex increasing trading speed, in contrast to the equity case where the optimal execution strategy results in a rather constant trading speed. However, in such mean valuation framework, the underlying spot price does not seem to affect the agent’s decision. By taking the agent risk aversion into account through a mean-variance approach, the strategy becomes more sensitive to the underlying price evolution, urging the agent to trade faster at the beginning of the strategy.

Optimal Execution Strategy for Large Orders in Big Data: Order Type using Q-learning Considerations

2020 ◽  
Vol 112 (1) ◽  
pp. 123-148 ◽  
Author(s):  
Amir Javadpour ◽  
Kh Saedifar ◽  
Guojun Wang ◽  
Kuan-Ching Li
Keyword(s):  
Big Data ◽  
Order Type ◽  
Q Learning ◽  
Optimal Execution ◽  
Execution Strategy

scholarly journals Extension and Calibration of a Hawkes-Based Optimal Execution Model

2016 ◽  
Vol 02 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Aurélien Alfonsi ◽  
Pierre Blanc
Keyword(s):  
Time Scales ◽  
Financial Data ◽  
Optimal Execution ◽  
Calibration Protocol ◽  
Execution Strategy ◽  
Execution Model ◽  
Market Needs

We provide some theoretical extensions and a calibration protocol for our former dynamic optimal execution model. The Hawkes parameters and the propagator are estimated independently on financial data from stocks of the CAC40. Interestingly, the propagator exhibits a smoothly decaying form with one or two dominant time scales, but only so after a few seconds that the market needs to adjust after a large trade. Motivated by our estimation results, we derive the optimal execution strategy for a multi-exponential Hawkes kernel and backtest it on the data for round trips. We find that the strategy is profitable on average when trading at the midprice, which is in accordance with violated martingale conditions. However, in most cases, these profits vanish when we take bid–ask costs into account.

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