Optimal deleveraging and liquidation of financial portfolios with market impact

Chen, Jingnan

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https://hdl.handle.net/2142/50347 Copy

Description

Title

Optimal deleveraging and liquidation of financial portfolios with market impact

Author(s)

Chen, Jingnan

Issue Date

2014-09-16

Director of Research (if dissertation) or Advisor (if thesis)

• Feng, Liming
• Peng, Jiming

Doctoral Committee Chair(s)

Feng, Liming

Committee Member(s)

• Peng, Jiming
• Birge, John
• Shen, Jianhong
• Sowers, Richard B.

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Portfolio Deleveraging
• Portfolio Liquidation
• Market Impact
• Quadratic Program
• Markov Chain Approximation

Abstract

The 2008 Financial Crisis highlighted the importance of effective portfolio deleveraging and liquidation strategies, which is critical to surviving financial distress and maintaining system stability. This thesis studies two related problems: which portion of the portfolio should be executed to relieve the financial distress and how the execution should be conducted to balance the trading cost and the trading risk. An optimal deleveraging strategy determines what portion of the portfolio needs to be liquidated to reduce leverage at the minimal trading cost. While an optimal execution strategy tells how liquidation should proceed to minimize the cost and risk. In this thesis, we formulate a one-period optimal deleveraging problem as a non-convex quadratic (polynomial) program with quadratic (polynomial) and box constraints under linear (nonlinear) market price impact functions. A Lagrangian algorithm is developed to numerically solve the NP-hard problem and estimate the quality of the solution. We further propose a two-period robust deleveraging program to account for market uncertainties. Depending on whether the portfolio contains derivative securities, the robust optimization program can be converted to either a convex semidefinite program or a convex second-order cone program, both of which are computationally tractable. We model the optimal execution problem as a stochastic control program and propose a Markov chain approximation scheme to numerically obtain the optimal trading trajectory. We also analyze theoretically how asset characteristics and market conditions affect the optimal deleveraging and execution strategies, which provides guidance on how to design trading policies from qualitative aspects.

Graduation Semester

2014-08

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http://hdl.handle.net/2142/50347

Copyright and License Information

Copyright 2014 Jingnan Chen

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