Artikel

A singular stochastic control approach for optimal pairs trading with proportional transaction costs

Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies that determine optimally both trade times and number of shares in a pairs trading process, we use a singular stochastic control approach to study an optimal pairs trading problem with proportional transaction costs. Assuming a cointegrated relationship for a pair of stock log-prices, we consider a portfolio optimization problem that involves dynamic trading strategies with proportional transaction costs. We show that the value function of the control problem is the unique viscosity solution of a nonlinear quasi-variational inequality, which is equivalent to a free boundary problem for the singular stochastic control value function. We then develop a discrete time dynamic programming algorithm to compute the transaction regions, and show the convergence of the discretization scheme. We illustrate our approach with numerical examples and discuss the impact of different parameters on transaction regions. We study the out-of-sample performance in an empirical study that consists of six pairs of U.S. stocks selected from different industry sectors, and demonstrate the efficiency of the optimal strategy.

Language
Englisch

Bibliographic citation
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 4 ; Pages: 1-23 ; Basel: MDPI

Classification
Wirtschaft
Subject
free-boundary problem
pairs trading
stochastic control
trading strategies
transaction costs
transaction regions

Event
Geistige Schöpfung
(who)
Xing, Haipeng
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2022

DOI
doi:10.3390/jrfm15040147
Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Xing, Haipeng
  • MDPI

Time of origin

  • 2022

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