Portfolio Liquidation and Ambiguity Aversion

40 Pages Posted: 4 Apr 2017

See all articles by Álvaro Cartea

Álvaro Cartea

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Ryan Francis Donnelly

King's College London

Sebastian Jaimungal

University of Toronto - Department of Statistics

Date Written: April 4, 2017


We consider an optimal execution problem where an agent holds a position in an asset which must be liquidated (using limit orders) before a terminal horizon. Beginning with a standard model for the trading dynamics, we analyse how the acknowledgement of model misspecification affects the agent's optimal trading strategy. The three possible sources of misspecification in this context are: (i) the arrival rate of market orders, (ii) the fill probability of limit orders, and (iii) the dynamics of the asset price. We show that ambiguity aversion with respect to each factor of the model has a similar effect on the optimal strategy, but the magnitude of the effect depends on time and inventory position in different ways depending on the source of uncertainty. In addition we allow the agent to employ market orders to further increase the strategy's profitability and show the effect of ambiguity aversion on the shape of the optimal impulse region. In some cases we have a closed-form expression for the optimal trading strategy which significantly enhances the efficiency in which the strategy can be executed in real time.

Keywords: Optimal Execution, Ambiguity Aversion, Model Uncertainty, Algorithmic Trading, High Frequency Trading, Short Term Alpha, Adverse Selection, Robust Optimization

JEL Classification: C6, C61, C73, G12

Suggested Citation

Cartea, Álvaro and Donnelly, Ryan Francis and Jaimungal, Sebastian, Portfolio Liquidation and Ambiguity Aversion (April 4, 2017). Available at SSRN: https://ssrn.com/abstract=2946136 or http://dx.doi.org/10.2139/ssrn.2946136

Álvaro Cartea

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Ryan Francis Donnelly (Contact Author)

King's College London ( email )

London, England WC2R 2LS
United Kingdom

Sebastian Jaimungal

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3

HOME PAGE: http://http:/sebastian.statistics.utoronto.ca

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics