Optimal Liquidation Strategy of Multi-assets Based on Minimum Loss Probability

Qixuan Luo , Can Jia , Shaobo Zhao , Handong Li

Journal of Systems Science and Systems Engineering ›› 2020, Vol. 29 ›› Issue (5) : 555 -571.

PDF
Journal of Systems Science and Systems Engineering ›› 2020, Vol. 29 ›› Issue (5) : 555 -571. DOI: 10.1007/s11518-020-5459-z
Article

Optimal Liquidation Strategy of Multi-assets Based on Minimum Loss Probability

Author information +
History +
PDF

Abstract

Based on the minimum loss probability criterion, this paper discusses the optimal strategy in multi-asset liquidation. First, we give the framework of the multi-asset liquidation problem and obtain the boundary conditions of the optimal liquidation strategy under the assumption of linear price impact functions and transform the multi-asset liquidation problem into the portfolio liquidation problem. On this basis, the asymptotic solution and numerical solution of the optimal liquidation strategy are obtained. Then, we simulate the trajectories of the optimal liquidation strategy and analyze the effects of parameters changes.

Keywords

Minimum loss probability / multi-asset liquidation / permanent impact / temporary impact / optimal liquidation strategy

Cite this article

Download citation ▾
Qixuan Luo, Can Jia, Shaobo Zhao, Handong Li. Optimal Liquidation Strategy of Multi-assets Based on Minimum Loss Probability. Journal of Systems Science and Systems Engineering, 2020, 29(5): 555-571 DOI:10.1007/s11518-020-5459-z

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Alfonsi A, Fruth A, Schied A. Optimal execution strategies in limit order books with general shape functions. Quantitative Finance, 2010, 10: 143-157.

[2]

Alfonsi A, Schied A, Slynko A. Order book resilience, price manipulation, and the positive portfolio problem. SIAM Journal of Financial Mathematics, 2012, 3: 511533.

[3]

Almgren R, Chriss N. Value under liquidation. Risk, 1999, 12: 61-63.

[4]

Almgren R, Chriss N. Optimal execution of portfolio transactions. Journal of Risk, 2000, 3: 5-39.

[5]

Bertsimas D, Lo A. Optimal control of execution costs. Journal of Financial Markets, 1998, 1: 1-50.

[6]

Brunnermeier M K, Pedersen L H. Market liquidity and funding liquidity. The Review of Financial Studies, 2008, 22(6): 2201-2238.

[7]

Engle R, Ferstenberg R. Execution risk. Journal of Portfolio Management, 2007, 33(2): 34-44.

[8]

Forsyth P, Shannon K, Tse S, Windcliff H. Optimal trade execution: A mean quadratic variation approach. Journal of Economic Dynamics and Control, 2012, 36(12): 1971-1991.

[9]

Gatheral J. No-dynamic-arbitrage and market impact. Quantitative Finance, 2010, 10: 749-759.

[10]

Gatheral J, Schied A, Slynko A. Transient linear price impact and Fredholm integral equations. Mathematical Finance, 2012, 22: 445-474.

[11]

Gatheral J, Schied A. Dynamical models of market impact and algorithms for order execution. Handbook on Systemic Risk, 2013, London: Cambridge University Press

[12]

He H, Mamaysky H. Dynamic trading policies with price impact. Journal of Economic Dynamics and Control, 2005, 29: 891-930.

[13]

Jin Y. Optimal execution strategy and liquidity adjusted value-at-risk. Quantitative Finance, 2017, 17(8): 1147-1157.

[14]

Kelley W G, Peterson A C. Difference Equations: An Introduction with Applications, 2001, Harcourt: Academic Press.

[15]

Obizhaeva A, Wang J. Optimal trading strategy and supply/demand dynamics. Journal of Financial Markets, 2013, 16: 1-32.

[16]

Schied A, Schoneborn T. Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets. Finance and Stochastics, 2009, 13(2): 181-204.

[17]

Scholtus M L, Dijk D V, Frijns B. Speed, Algorithmic Trading, and Market Quality around Macroeconomic News Announcements. Journal of Banking and Finance, 2014, 38(1): 89-105.

AI Summary AI Mindmap
PDF

175

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/