Optimal portfolio trading subject to stochastic dominance constraints under second‐order autoregressive price dynamics

This paper studies the optimal portfolio trading problem under the generalized second‐order autoregressive execution price model. The problem of minimizing expected execution cost under the proposed price model is formulated as a quadratic programming (QP) problem. For a risk‐averse trader, problem...

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Vydáno v:International transactions in operational research Ročník 27; číslo 3; s. 1771 - 1803
Hlavní autoři: Singh, Arti, Dharmaraja, Selvamuthu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.05.2020
Témata:
autoregressive behavior
Autoregressive models
Computational geometry
Constraints
convex programming
Convexity
Economic models
execution cost
Mathematical programming
Mutual funds
Operations research
optimal trading
Optimization
Quadratic programming
Risk
risk aversion
stochastic dominance
ISSN:0969-6016, 1475-3995
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Shrnutí:This paper studies the optimal portfolio trading problem under the generalized second‐order autoregressive execution price model. The problem of minimizing expected execution cost under the proposed price model is formulated as a quadratic programming (QP) problem. For a risk‐averse trader, problem formulation under the second‐order stochastic dominance constraints results in a quadratically constrained QP problem. Under some conditions on the execution price model, it is proved that the portfolio trading problems for risk‐neutral and risk‐averse traders become convex programming problems, which have many theoretical and computational advantages over the general class of optimization problems. Extensive numerical illustrations are provided, which render the practical significance of the proposed execution price model and the portfolio trading problems.
Bibliografie:ObjectType-Article-1
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ISSN:0969-6016
1475-3995
DOI:10.1111/itor.12435