University of Illinois Urbana-Champaign

Optimal deleveraging and liquidation of financial portfolios with market impact

Chen, Jingnan

Content Files
Jingnan_Chen.pdf

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

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
Date of Ingest
2014-09-16T17:11:54Z
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
Permalink
http://hdl.handle.net/2142/50347
Copyright and License Information
Copyright 2014 Jingnan Chen

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