Open-loop equilibrium strategy for mean-variance Portfolio selection with investment constraints in a non-Markovian regime-switching jump-diffusion model

Ishak Alia; Mohamed Sofiane Alia

doi:10.3934/jimo.2022048

Article Contents

2023, Volume 19, Issue 4: 2396-2435. Doi: 10.3934/jimo.2022048

This issue Previous Article A heuristically accelerated reinforcement learning method for maintenance policy of an assembly line Next Article Study on government subsidy in a two-level supply chain of direct-fired biomass power generation based on contract coordination

Open-loop equilibrium strategy for mean-variance Portfolio selection with investment constraints in a non-Markovian regime-switching jump-diffusion model

Ishak Alia^, and
Mohamed Sofiane Alia

Institute of Optics and Precision Mechanics, Ferhat Abbas University Setif 1, Setif 19000, Algeria

^*Corresponding author: Ishak Alia
^*Corresponding author: Ishak Alia

Received: July 2021

Revised: December 2021

Early access: April 2022

Published: April 2023

Abstract Full Text(HTML) Figure(3) Related Papers Cited by

Abstract

This paper is devoted to study the open-loop equilibrium strategy for a mean-variance portfolio problem with investment constraints in a non-Markovian regime-switching jump-diffusion model. Specially, the investment strategies are constrained in a closed convex cone and all coefficients in the model are stochastic processes adapted to the filtration generated by a Markov chain. First, we provide a necessary and sufficient condition for an equilibrium strategy, which involves a system of forward and backward stochastic differential equations (FBSDEs, for short). Second, by solving these FBSDEs, we obtain a feedback representation of the equilibrium strategy. Third, we prove a theorem ensuring the almost everywhere uniqueness of the equilibrium solution. Finally, the results are applied to solve an example of the Markovian regime-switching model.

Keywords:

Mathematics Subject Classification: Primary: 93E20, 60H30, 93E99, 60H10.

Citation:

Full Text(HTML)

Figure 1. The investment ratio in the two regimes with $ \mu = 1.2 $

Download: Full-size image PowerPoint slide

Figure 2. The impact of the parameter $ \mu $ on the investment ratio in regime 1

Download: Full-size image PowerPoint slide

Figure 3. The impact of the parameter $ \mu $ on the investment ratio in regime 2

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	I. Alia and F. Chighoub, Continuous-time mean-variance portfolio selection with regime-switching financial market: Time-consistent solution, Random Oper. Stoch. Equ., 29 (2021), 11-25. doi: 10.1515/rose-2020-2050.
[2]	I. Alia, F. Chighoub and A. Sohail, A characterization of equilibrium strategies in continuous-time mean-variance problems for insurers, Insurance Math. Econom., 68 (2016), 212-223. doi: 10.1016/j.insmatheco.2016.03.009.
[3]	D. Andersson and B. Djehiche, A maximum principle for SDEs of mean-field type, Appl. Math. Optim., 63 (2011), 341-356. doi: 10.1007/s00245-010-9123-8.
[4]	S. Basak and G. Chabakauri, Dynamic mean-variance asset allocation, Review of Financial Studies, 23 (2010), 2970-3016. doi: 10.1093/rfs/hhq028.
[5]	I. Bajeux-Besnainou and R. Portait, Dynamic asset allocation in a mean-variance framework, Management Science, 44 (1998), 79-95. doi: 10.1287/mnsc.44.11.S79.
[6]	A. Bensoussan, K. C. Wong, S. C. P. Yam and S. P. Yung, Time-consistent portfolio selection under short-selling prohibition: From discrete to continuous setting, SIAM J. Financial Math., 5 (2014), 153-190. doi: 10.1137/130914139.
[7]	T. R. Bielecki, H. Jin, S. Pliska and X. Zhou, Continuous-time mean-variance portfolio selection with bankruptcy prohibition, Math. Finance, 15 (2005), 213–244, Available from: http://www.columbia.edu/xz2574/download/bjpz.pdf. doi: 10.1111/j.0960-1627.2005.00218.x.
[8]	T. Björk and A. Murgoci, A general theory of Markovian time-inconsistent stochastic control problems, SSRN, (2010), Available from: https://ssrn.com/abstract=1694759.
[9]	T. Björk, A. Murgoci and X. Y. Zhou, Mean-variance portfolio optimization with state-dependent risk aversion, Math. Finance, 24 (2014), 1-24. doi: 10.1111/j.1467-9965.2011.00515.x.
[10]	P. Chen and H. Yang, Markowitz's mean-variance asset-liability management with regime switching: A multi period model, Appl. Math. Finance, 18 (2011), 29-50. doi: 10.1080/13504861003703633.
[11]	P. Chen, H. Yang and G. Yin, Markowitz's mean-variance asset-liability management with regime switching: A continuous-time model, Insurance Math. Econom., 43 (2008), 456-465. doi: 10.1016/j.insmatheco.2008.09.001.
[12]	S. Cohen and R. Elliott, Comparisons for backward stochastic differential equations on Markov chains and related no-arbitrage conditions, Ann. Appl. Probab., 20 (2010), 267-311. doi: 10.1214/09-AAP619.
[13]	R. Cont and P. Tankov, Financial Modelling with Jump Processes, Chapman & Hall/CRC Financial Mathematics Series. Chapman & Hall/CRC, Boca Raton, FL, 2004. doi: 10.1201/9780203485217.
[14]	S. Crepey, Financial Modeling: A Backward Stochastic Differential Equations Perspective Processes, Springer Finance. Springer Finance Textbooks. Springer, Heidelberg, 2013. doi: 10.1007/978-3-642-37113-4.
[15]	S. Crépey and A. Matoussi, Reflected and doubly reflected BSDEs with jumps: A priori estimates and comparison, Ann. Appl. Probab., 18 (2008), 2041-2069. doi: 10.1214/08-AAP517.
[16]	C. Czichowsky, Time-consistent mean-variance porftolio selection in discrete and continuous time, Finance Stoch., 17 (2013), 227-271. doi: 10.1007/s00780-012-0189-9.
[17]	M. Dai, Z. Xu and X. Zhou, Continuous-time Markowitz's model with transaction costs, SIAM J. Financial Math., 1 (2010), 96-125. doi: 10.1137/080742889.
[18]	C. Donnelly, Sufficient stochastic maximum principle in the regime-switching diffusion model, Appl. Math. Optim., 64 (2011), 155-169. doi: 10.1007/s00245-010-9130-9.
[19]	R. J. Elliott, T. K. Siu and A. Badescu, On mean-variance portfolio selection under a hidden Markovian regime-switching model, Economic Modelling, 27 (2010), 678-686.
[20]	J. M. Ingram and M. M. Marsh, Projections onto convex cones in Hilbert space, J. Approx. Theory, 64 (1991), 343-350. doi: 10.1016/0021-9045(91)90067-K.
[21]	A. Jobert and L. C. G. Rogers, Option pricing with Markov-modulated dynamics, SIAM J. Control Optim., 44 (2006), 2063-2078. doi: 10.1137/050623279.
[22]	Y. Hu, J. Huang and X. Li, Equilibrium for time-inconsistent stochastic linear–quadratic control under constraint, preprint, arXiv: 1703.09415v1.
[23]	Y. Hu, H. Jin and X. Y. Zhou, Time-inconsistent stochastic linear quadratic control, SIAM J. Control Optim., 50 (2012), 1548-1572. doi: 10.1137/110853960.
[24]	Y. Hu, H. Jin and X. Y. Zhou, Time-inconsistent stochastic linear quadratic control: Characterization and uniqueness of equilibrium, SIAM J. Control Optim., 55 (2017), 1261-1279. doi: 10.1137/15M1019040.
[25]	D. Li and W. Ng, Optimal dynamic portfolio selection: Multi-period mean-variance formulation, Math. Finance, 10 (2000), 387-406. doi: 10.1111/1467-9965.00100.
[26]	A. E. B. Lim, Quadratic hedging and mean-variance portfolio selection with random parameters in an incomplete market, Math. Oper. Res., 29 (2004), 132-161. doi: 10.1287/moor.1030.0065.
[27]	A. E. B. Lim and X. Zhou, Quadratic hedging and mean-variance portfolio selection with random parameters in a complete market, Math. Oper. Res., 27 (2002), 101-120. doi: 10.1287/moor.27.1.101.337.
[28]	H. M. Markowitz, Portfolio selection, Jornal of Finance, 7 (1952), 77–91, Available from: https://www.math.hkust.edu.hk/maykwok/courses/ma362/07F/markowitz_JF.pdf. doi: doi.org/10.2307/2975974.
[29]	L. C. G. Rogers and D. Williams, Diffusions, Markov Processes and Martingales, 2$^{nd}$ edition, Cambridge University Press, Cambridge, 2000. doi: 10.1017/CBO9781107590120.
[30]	J. Sass and U. G. Haussmann, Optimizing the terminal wealth under partial information: The drift process as a continuous time Markov chain, Finance Stoch., 8 (2004), 553-577. doi: 10.1007/s00780-004-0132-9.
[31]	Y. Shen, J. Wei and Q. Zhao, Mean-variance asset-liability management problem under non-Markovian regime-switching model, Appl. Math. Optim., 81 (2020), 859-897. doi: 10.1007/s00245-018-9523-8.
[32]	K. Si, Z. Xu, K. F. C. Yiu and X. Li, Open-loop solvability for mean-field stochastic linear quadratic optimal control problems of Markov regime-switching system, Journal of Industrial and Management Optimization. doi: 10.3934/jimo.2021074.
[33]	R. H. Stockbridge, Portfolio optimization in markets having stochastic rates, Stochastic Theory and Control, 280 (2002), 447-458. doi: 10.1007/3-540-48022-6_30.
[34]	Z. Sun and X. Guo, Equilibrium for a time-inconsistent stochastic linear–quadratic control system with jumps and its application to the mean-variance problem, J. Optim. Theory Appl., 181 (2019), 383-410. doi: 10.1007/s10957-018-01471-x.
[35]	H. Sun, Z. Sun and Y. Huang, Equilibrium investment and risk control for an insurer with non-Markovian regime-switching and no-shorting constraints, AIMS Math., 5 (2020), 6996-7013. doi: 10.3934/math.2020449.
[36]	T. Wang, Z. Jin and J. Wei, Mean-variance portfolio selection under a non-Markovian regime-switching model: Time-consistent solutions, SIAM J. Control Optim., 57 (2019), 3249-3271. doi: 10.1137/18M1186423.
[37]	T. Wang and J. Wei, Mean-variance portfolio selection under a non-Markovian regime switching model, J. Comput. Appl. Math., 350 (2019), 442-455. doi: 10.1016/j.cam.2018.10.040.
[38]	J. Wei and T. Wang, Time-consistent mean-variance asset-liability management with random coefficients, Insurance Math. Econom., 77 (2017), 84-96. doi: 10.1016/j.insmatheco.2017.08.011.
[39]	J. Wei, K. C. Wong, S. C. P. Yam and S. P. Yung, Markowitz's mean–variance asset-liability management with regime switching: A time-consistent approach, Insurance Math. Econom., 53 (2013), 281-291. doi: 10.1016/j.insmatheco.2013.05.008.
[40]	J. Q. Wen, X. Li and J. Xiong, Weak closed-loop solvability of stochastic linear quadratic optimal control problems of Markovian regime switching system, Appl. Math. Optim., 84 (2021), 535-565. doi: 10.1007/s00245-020-09653-8.
[41]	J. M. Xia, Mean-variance portfolio choice: Quadratic partial hedging, Math. Finance, 15 (2005), 533-538. doi: 10.1111/j.1467-9965.2005.00231.x.
[42]	J. Zhang, P. Chen, Z. Jin and S. Li, Open-loop equilibrium strategy for mean-variance portfolio selection: A log-return model, Journal of Industrial and Management Optimization, 17 (2021), 765-777. doi: 10.3934/jimo.2019133.
[43]	X. Zhang, X. Li and J. Xiong, Open-loop and closed-loop solvabilities for stochastic linear quadratic optimal control problems of Markovian regime switching system, ESAIM Control Optim. Calc. Var., 27 (2021), Paper No. 69, 35 pp. doi: 10.1051/cocv/2021066.
[44]	Y. Zhang, X. Li and S. Guo, Portfolio selection problems with Markowitz's mean–variance framework: A review of literature, Fuzzy Optim. Decis. Mak., 17 (2018), 125-158. doi: 10.1007/s10700-017-9266-z.
[45]	X. Y. Zhou and D. Li, Continuous-time mean-variance portfolio selection: A stochastic LQ framework, Appl. Math. Optim., 42 (2000), 19-33. doi: 10.1007/s002450010003.
[46]	X. Zhang, Z. Sun and J. Xiong, A general stochastic maximum principle for a Markov regime switching jump-diffusion model of mean-field type, SIAM J. Control Optim., 56 (2018), 2563-2592. doi: 10.1137/17M112395X.
[47]	X. Y. Zhou and G. Yin, Markowitzs mean–variance portfolio selection with regime switching: A continuous-time model, SIAM J. Control Optim., 42 (2003), 1466-1482. doi: 10.1137/S0363012902405583.