scholarly journals Optimal execution strategy in the presence of permanent price impact and fixed transaction cost

2011 ◽  
Vol 33 (6) ◽  
pp. 713-738 ◽  
Author(s):  
Mauricio Junca
Keyword(s):  
Transaction Cost ◽  
Price Impact ◽  
Optimal Execution ◽  
Fixed Transaction Cost ◽  
Execution Strategy

DISCRETE-TIME OPTIMAL EXECUTION UNDER A GENERALIZED PRICE IMPACT MODEL WITH MARKOVIAN EXOGENOUS ORDERS

2021 ◽  
pp. 2150025
Author(s):  
MASAAKI FUKASAWA ◽  
MASAMITSU OHNISHI ◽  
MAKOTO SHIMOSHIMIZU
Keyword(s):  
Discrete Time ◽  
Absolute Risk ◽  
Price Impact ◽  
Affine Function ◽  
Impact Model ◽  
State Variables ◽  
Optimal Execution ◽  
Execution Strategy ◽  
Time Optimal ◽  
Small Traders

This paper examines a discrete-time optimal execution problem with generalized price impact. Our main objective is to investigate the effect of price impact caused by aggregate random trade orders posed by small traders on the optimal execution strategy when orders of the small traders have a Markovian dependence. Our problem is formulated as a Markov decision process with state variables which include the last small traders’ aggregate orders. Over a finite horizon, a large trader with Constant Absolute Risk Aversion (CARA) von Neumann–Morgenstern (vN-M) utility function maximizes the expected utility from the final wealth. By applying the backward induction method of dynamic programming, we characterize the optimal execution strategy and optimal value function and conclude that the optimal execution strategy is a time-dependent affine function of three state variables. Moreover, numerical analysis prevails that the optimal execution strategy admits a “statistical arbitrage” via a round-trip trading, although our model considers a linear permanent price impact. The result differs from the previous prevailing one that a linear permanent price impact model precludes any price manipulation or arbitrage. Thus, considering a price impact caused by small traders’ orders with a Markovian dependence is significant.

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.

scholarly journals Optimal Execution considering Trading Signal and Execution Risk Simultaneously

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuan Cheng ◽  
Lan Wu
Keyword(s):  
Kernel Function ◽  
Price Impact ◽  
Value Functions ◽  
Feedback Form ◽  
Bellman Equations ◽  
Impact Function ◽  
Optimal Execution ◽  
Dirac Function ◽  
Hamilton Jacobi Bellman ◽  
Optimal Execution Problem

In this paper, we study the optimal execution problem by considering the trading signal and the transaction risk simultaneously. We propose an optimal execution problem by taking into account the trading signal and the execution risk with the associated decay kernel function and the transient price impact function being of generalized forms. In particular, we solve the stochastic optimal control problems under the assumptions that the decay kernel function is the Dirac function and the transient price function is a linear function. We give the optimal executing strategies in state-feedback form and the Hamilton‐Jacobi‐Bellman equations that the corresponding value functions satisfy in the cases of a constant execution risk and a linear execution risk. We also demonstrate that our results can recover previous results when the process of the trading signal degenerates.

scholarly journals Periodic strategies in optimal execution with multiplicative price impact

2019 ◽  
Vol 29 (4) ◽  
pp. 1039-1065
Author(s):  
Daniel Hernández‐Hernández ◽  
Harold A. Moreno‐Franco ◽  
José‐Luis Pérez
Keyword(s):  
Price Impact ◽  
Optimal Execution
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