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dc.contributor.authorSanjari, Ali
dc.date.accessioned2016-02-22T20:29:42Z
dc.date.available2016-02-22T20:29:42Z
dc.date.issued2016
dc.identifier.urihttps://hdl.handle.net/2144/14553
dc.description.abstractIn order to liquidate a large position in an asset, investors face a tradeoff between price volatility and market impact. The classical approach to this problem is to model volatility via a Brownian motion, and separate price impact into its permanent and temporary components. In this thesis, we consider two variations of the Chriss-Almgren model for temporary price impact. The first model investigates the infinite-horizon optimal liquidation problem in a market with float-dependent, nonlinear temporary price impact. The value function of the investor’s basket and the optimal strategy are characterized in terms of classical solutions of nonlinear parabolic partial differential equations. Depending on the price impact parameters, liquidation may require finite or infinite time. The second model considers time-varying market depth, in that intense trading increases temporary price-impact, which otherwise reverts to a long-term level. We find the optimal execution policy in a finite horizon for an investor with constant risk aversion, and derive the solution using calculus of variation techniques. Although the model potentially allows for price manipulation strategies, these policies are never optimal. We study the non time-constrained case as a limit to the finite-horizon case and explain the solution through a quasi-linear PDE.en_US
dc.language.isoen_USen_US
dc.subjectMathematicsen_US
dc.subjectDynamic impact with decay factoren_US
dc.subjectFloat-dependent impacten_US
dc.subjectOptimal liquidation policyen_US
dc.subjectTemporary price impacten_US
dc.titleLiquidation under dynamic price impacten_US
dc.typeThesis/Dissertation
dc.date.updated2016-02-16T02:16:13Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineMathematics & Statisticsen_US
etd.degree.grantorBoston Universityen_US


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