American Institute of Mathematical Sciences

Advanced
  • Previous Article
    A DC programming approach for sensor network localization with uncertainties in anchor positions
  • JIMO Home
  • This Issue
  • Next Article
    Solving structural engineering design optimization problems using an artificial bee colony algorithm
July  2014, 10(3): 795-815. doi: 10.3934/jimo.2014.10.795

Optimal stochastic investment games under Markov regime switching market

Lin Xu 1, , Rongming Wang 2, and  Dingjun Yao 3,

1. 

School of Mathematics and Computer Science, Anhui Normal University, Wuhu, Anhui, 241003, China
2. 

School of Finance and Statistics, East China Normal University, Shanghai, 200241
3. 

School of Finance, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210046, China

Received  November 2012 Revised  June 2013 Published  November 2013

This paper focuses on stochastic investment games between two investors with incorporating the influence of the macro economical environment that modeled by a Markov chain with $d$ states. There are two correlated assets are available to two investors, each investor can only invest into one of assets and his opponent choose to invest the other one. The dynamic of the two assets are driven by two drifted Brownian motion with coefficients specified by the functions of the Markov chain. Thus the system considered in this paper is controlled SDEs with random coefficients. Only one payoff function is available to both investors, one investor wants to maximize the expected payoff function, while his opponent wants to minimize the quantity at the same time. As results, the existence of the saddle point of the game, a couple of equations satisfied by the value functions and optimal policies for both investors are derived. Based on finite-difference method and weak convergence theory, a vector-valued Markov chain is constructed for approximating the underlying risky process weakly, which enables us to obtain the value function and optimal policies numerically. To some extend, we can view this paper as a further research of the problems proposed in Wan [23].
Keywords: Optimal investment, finite difference method, Markov regime switching market, weak convergence., zero-sum stochastic differential games.
Mathematics Subject Classification: Primary: 91G10, 91G80; Secondary: 90B5.
Citation: Lin Xu, Rongming Wang, Dingjun Yao. Optimal stochastic investment games under Markov regime switching market. Journal of Industrial & Management Optimization, 2014, 10 (3) : 795-815. doi: 10.3934/jimo.2014.10.795
References:
[1]

S. Browne, Optimal investment policy for a firm with random risk process: Exponential utility and minimizing the probabilty of ruin,, Mathematical Operation Research, 20 (1995), 937. Google Scholar

[2]

S. Browne, Stochastic differential portfolio games/em>,, Journal of Applied Probability, 37 (2000), 126. doi: 10.1239/jap/1014842273. Google Scholar

[3]

M. Clements and H. Krolzig, Can regime-swichting models reproducing the business cycle features of U.S. aggregate consumption, investment and output?, International Journal of Financing and Economics, 9 (2004), 1. Google Scholar

[4]

R. Elliott, The existence of value in stochastic differential games,, SIAM: Journal of Control and Optimizaiton, 14 (1976), 85. doi: 10.1137/0314006. Google Scholar

[5]

R. Elliott and J. Hoek, An application of hidden Markov models to asset allocation problems,, Finance and Stochastics, 1 (1997), 229. doi: 10.1007/s007800050022. Google Scholar

[6]

R. Elliott and P. Kopp, Mathematics of Financial Markets,, $2^{nd}$ edition, (2005). Google Scholar

[7]

R. Elliott, T. K. Siu and L. Chan, Pricing volatility swaps under Heston's stochastic volatility model with regime switching,, Applied Mathematical Finance, 14 (2007), 41. doi: 10.1080/13504860600659222. Google Scholar

[8]

W. H. Fleming and P. E. Souganidis, On the existence of value functions of two-player, zero-sum stochastic differential games,, Indiana Universtiy Mathematical Journal, 38 (1989), 293. doi: 10.1512/iumj.1989.38.38015. Google Scholar

[9]

W. H. Fleming and H. M. Soner, Controlled Markov Processes and Viscosity Solutions,, Second edition. Stochastic Modelling and Applied Probability, (2006). Google Scholar

[10]

X. Guo, Information and option pricings,, Quantitative Finance, 1 (2001), 38. doi: 10.1080/713665550. Google Scholar

[11]

J. D. Hamilton, A new approach to the economic analysis of nonstationary time series and the business cycle,, Econometrica, 57 (1989), 357. doi: 10.2307/1912559. Google Scholar

[12]

C. Hipp and M. Plum, Optimal investment for insurers,, Insurance: Mathematics and Economics, 27 (2000), 215. doi: 10.1016/S0167-6687(00)00049-4. Google Scholar

[13]

H. Kushner and S. Chamberlain, On stochastic differential games: Sufficient conditions that a given strategy be a saddle point, and numerical procedures for the solution of the game,, Journal of Mathmatical Analysis and Applications, 26 (1969), 560. doi: 10.1016/0022-247X(69)90199-1. Google Scholar

[14]

H. J. Kusher, Probability Methods for Approximations in Stochastic Control and for Elliptic Equations,, Academic Press, (1977). Google Scholar

[15]

H. J. Kusher, Approximations and Weak Convergence Methods for Random Processes,, MIT Press, (1984). Google Scholar

[16]

H. J. Kusher, Numerical methods for stochastic control problems in continuous time,, SIAM: Journal of Control and Optimization, 28 (1990), 990. doi: 10.1137/0328056. Google Scholar

[17]

H. J. Kushner and P. G. Dupuis, Numerical Methods for Stochastic Control Problems in Continuous Time,, Springer-Verlag: New York, (2001). Google Scholar

[18]

H. J. Kushner and S. G. Chamberlain, Finite state stochastic games: existence theorems and computational procedures,, IEEE Trans. Automat. Control, 14 (1969), 248. Google Scholar

[19]

H. Meng, F. Yuen, T. Siu and H. Yang, Optimal portfolio in a continuous-time self-exciting threshold model,, Journal of Industrial and Management Optimization, 9 (2013), 487. doi: 10.3934/jimo.2013.9.487. Google Scholar

[20]

S. Pliska, Introduction to Mathematical Finance,, United States: Blackwell Publishing, (1997). Google Scholar

[21]

P. E. Protter, Stochastic Integration and Differential Equations,, 2nd edition, (2005). Google Scholar

[22]

Q. Song, G. Yin and Z. Zhang, Numercial solutions for stochastic differential games with regime switching,, IEEE Transactions on Automatica Control, 53 (2008), 509. doi: 10.1109/TAC.2007.915169. Google Scholar

[23]

S. Wan, Stochastic differential portfolio games based on utility with regime switching model,, in, (2007), 2302. Google Scholar

[24]

H. Yang and L.Zhang, Optimal investment for insurer with jump-diffusion risk process,, Insurance: Mathematics and Economics, 37 (2005), 615. doi: 10.1016/j.insmatheco.2005.06.009. Google Scholar

[25]

D. Yao, H. Yang and R. Wang, Optimal financing and dividend strategies in a dual model with proportional costs,, Journal of Industrial and Management Optimization, 6 (2010), 761. doi: 10.3934/jimo.2010.6.761. Google Scholar

[26]

K. C. Yiu, J. Liu, T. Siu and K., Ching, Optimal portfolios with regime switching and value-at-risk constraint,, Automatica, 46 (2010), 979. doi: 10.1016/j.automatica.2010.02.027. Google Scholar

[27]

Y. Zeng and Z. Li, Optimal reinsurance-investment strategies for insurers under mean-CaR criteria,, Journal of Industrial and Management Optimization, 8 (2012), 673. doi: 10.3934/jimo.2012.8.673. Google Scholar

show all references

References:
[1]

S. Browne, Optimal investment policy for a firm with random risk process: Exponential utility and minimizing the probabilty of ruin,, Mathematical Operation Research, 20 (1995), 937. Google Scholar

[2]

S. Browne, Stochastic differential portfolio games/em>,, Journal of Applied Probability, 37 (2000), 126. doi: 10.1239/jap/1014842273. Google Scholar

[3]

M. Clements and H. Krolzig, Can regime-swichting models reproducing the business cycle features of U.S. aggregate consumption, investment and output?, International Journal of Financing and Economics, 9 (2004), 1. Google Scholar

[4]

R. Elliott, The existence of value in stochastic differential games,, SIAM: Journal of Control and Optimizaiton, 14 (1976), 85. doi: 10.1137/0314006. Google Scholar

[5]

R. Elliott and J. Hoek, An application of hidden Markov models to asset allocation problems,, Finance and Stochastics, 1 (1997), 229. doi: 10.1007/s007800050022. Google Scholar

[6]

R. Elliott and P. Kopp, Mathematics of Financial Markets,, $2^{nd}$ edition, (2005). Google Scholar

[7]

R. Elliott, T. K. Siu and L. Chan, Pricing volatility swaps under Heston's stochastic volatility model with regime switching,, Applied Mathematical Finance, 14 (2007), 41. doi: 10.1080/13504860600659222. Google Scholar

[8]

W. H. Fleming and P. E. Souganidis, On the existence of value functions of two-player, zero-sum stochastic differential games,, Indiana Universtiy Mathematical Journal, 38 (1989), 293. doi: 10.1512/iumj.1989.38.38015. Google Scholar

[9]

W. H. Fleming and H. M. Soner, Controlled Markov Processes and Viscosity Solutions,, Second edition. Stochastic Modelling and Applied Probability, (2006). Google Scholar

[10]

X. Guo, Information and option pricings,, Quantitative Finance, 1 (2001), 38. doi: 10.1080/713665550. Google Scholar

[11]

J. D. Hamilton, A new approach to the economic analysis of nonstationary time series and the business cycle,, Econometrica, 57 (1989), 357. doi: 10.2307/1912559. Google Scholar

[12]

C. Hipp and M. Plum, Optimal investment for insurers,, Insurance: Mathematics and Economics, 27 (2000), 215. doi: 10.1016/S0167-6687(00)00049-4. Google Scholar

[13]

H. Kushner and S. Chamberlain, On stochastic differential games: Sufficient conditions that a given strategy be a saddle point, and numerical procedures for the solution of the game,, Journal of Mathmatical Analysis and Applications, 26 (1969), 560. doi: 10.1016/0022-247X(69)90199-1. Google Scholar

[14]

H. J. Kusher, Probability Methods for Approximations in Stochastic Control and for Elliptic Equations,, Academic Press, (1977). Google Scholar

[15]

H. J. Kusher, Approximations and Weak Convergence Methods for Random Processes,, MIT Press, (1984). Google Scholar

[16]

H. J. Kusher, Numerical methods for stochastic control problems in continuous time,, SIAM: Journal of Control and Optimization, 28 (1990), 990. doi: 10.1137/0328056. Google Scholar

[17]

H. J. Kushner and P. G. Dupuis, Numerical Methods for Stochastic Control Problems in Continuous Time,, Springer-Verlag: New York, (2001). Google Scholar

[18]

H. J. Kushner and S. G. Chamberlain, Finite state stochastic games: existence theorems and computational procedures,, IEEE Trans. Automat. Control, 14 (1969), 248. Google Scholar

[19]

H. Meng, F. Yuen, T. Siu and H. Yang, Optimal portfolio in a continuous-time self-exciting threshold model,, Journal of Industrial and Management Optimization, 9 (2013), 487. doi: 10.3934/jimo.2013.9.487. Google Scholar

[20]

S. Pliska, Introduction to Mathematical Finance,, United States: Blackwell Publishing, (1997). Google Scholar

[21]

P. E. Protter, Stochastic Integration and Differential Equations,, 2nd edition, (2005). Google Scholar

[22]

Q. Song, G. Yin and Z. Zhang, Numercial solutions for stochastic differential games with regime switching,, IEEE Transactions on Automatica Control, 53 (2008), 509. doi: 10.1109/TAC.2007.915169. Google Scholar

[23]

S. Wan, Stochastic differential portfolio games based on utility with regime switching model,, in, (2007), 2302. Google Scholar

[24]

H. Yang and L.Zhang, Optimal investment for insurer with jump-diffusion risk process,, Insurance: Mathematics and Economics, 37 (2005), 615. doi: 10.1016/j.insmatheco.2005.06.009. Google Scholar

[25]

D. Yao, H. Yang and R. Wang, Optimal financing and dividend strategies in a dual model with proportional costs,, Journal of Industrial and Management Optimization, 6 (2010), 761. doi: 10.3934/jimo.2010.6.761. Google Scholar

[26]

K. C. Yiu, J. Liu, T. Siu and K., Ching, Optimal portfolios with regime switching and value-at-risk constraint,, Automatica, 46 (2010), 979. doi: 10.1016/j.automatica.2010.02.027. Google Scholar

[27]

Y. Zeng and Z. Li, Optimal reinsurance-investment strategies for insurers under mean-CaR criteria,, Journal of Industrial and Management Optimization, 8 (2012), 673. doi: 10.3934/jimo.2012.8.673. Google Scholar

[1]

Libin Mou, Jiongmin Yong. Two-person zero-sum linear quadratic stochastic differential games by a Hilbert space method. Journal of Industrial & Management Optimization, 2006, 2 (1) : 95-117. doi: 10.3934/jimo.2006.2.95
[2]

Beatris A. Escobedo-Trujillo. Discount-sensitive equilibria in zero-sum stochastic differential games. Journal of Dynamics & Games, 2016, 3 (1) : 25-50. doi: 10.3934/jdg.2016002
[3]

Qingmeng Wei, Zhiyong Yu. Time-inconsistent recursive zero-sum stochastic differential games. Mathematical Control & Related Fields, 2018, 8 (3&4) : 1051-1079. doi: 10.3934/mcrf.2018045
[4]

Xiangxiang Huang, Xianping Guo, Jianping Peng. A probability criterion for zero-sum stochastic games. Journal of Dynamics & Games, 2017, 4 (4) : 369-383. doi: 10.3934/jdg.2017020
[5]

Fernando Luque-Vásquez, J. Adolfo Minjárez-Sosa. Average optimal strategies for zero-sum Markov games with poorly known payoff function on one side. Journal of Dynamics & Games, 2014, 1 (1) : 105-119. doi: 10.3934/jdg.2014.1.105
[6]

Salah Eddine Choutri, Boualem Djehiche, Hamidou Tembine. Optimal control and zero-sum games for Markov chains of mean-field type. Mathematical Control & Related Fields, 2019, 9 (3) : 571-605. doi: 10.3934/mcrf.2019026
[7]

Lin Xu, Dingjun Yao, Gongpin Cheng. Optimal investment and dividend for an insurer under a Markov regime switching market with high gain tax. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-32. doi: 10.3934/jimo.2018154
[8]

Sylvain Sorin, Guillaume Vigeral. Reversibility and oscillations in zero-sum discounted stochastic games. Journal of Dynamics & Games, 2015, 2 (1) : 103-115. doi: 10.3934/jdg.2015.2.103
[9]

Antoine Hochart. An accretive operator approach to ergodic zero-sum stochastic games. Journal of Dynamics & Games, 2019, 6 (1) : 27-51. doi: 10.3934/jdg.2019003
[10]

Marianne Akian, Stéphane Gaubert, Antoine Hochart. Ergodicity conditions for zero-sum games. Discrete & Continuous Dynamical Systems - A, 2015, 35 (9) : 3901-3931. doi: 10.3934/dcds.2015.35.3901
[11]

Alexander J. Zaslavski. Structure of approximate solutions of dynamic continuous time zero-sum games. Journal of Dynamics & Games, 2014, 1 (1) : 153-179. doi: 10.3934/jdg.2014.1.153
[12]

Dejian Chang, Zhen Wu. Stochastic maximum principle for non-zero sum differential games of FBSDEs with impulse controls and its application to finance. Journal of Industrial & Management Optimization, 2015, 11 (1) : 27-40. doi: 10.3934/jimo.2015.11.27
[13]

Alexander J. Zaslavski. Turnpike properties of approximate solutions of dynamic discrete time zero-sum games. Journal of Dynamics & Games, 2014, 1 (2) : 299-330. doi: 10.3934/jdg.2014.1.299
[14]

Fabien Gensbittel, Miquel Oliu-Barton, Xavier Venel. Existence of the uniform value in zero-sum repeated games with a more informed controller. Journal of Dynamics & Games, 2014, 1 (3) : 411-445. doi: 10.3934/jdg.2014.1.411
[15]

Kun Fan, Yang Shen, Tak Kuen Siu, Rongming Wang. On a Markov chain approximation method for option pricing with regime switching. Journal of Industrial & Management Optimization, 2016, 12 (2) : 529-541. doi: 10.3934/jimo.2016.12.529
[16]

Valery Y. Glizer, Oleg Kelis. Singular infinite horizon zero-sum linear-quadratic differential game: Saddle-point equilibrium sequence. Numerical Algebra, Control & Optimization, 2017, 7 (1) : 1-20. doi: 10.3934/naco.2017001
[17]

Beatris Adriana Escobedo-Trujillo, José Daniel López-Barrientos. Nonzero-sum stochastic differential games with additive structure and average payoffs. Journal of Dynamics & Games, 2014, 1 (4) : 555-578. doi: 10.3934/jdg.2014.1.555
[18]

Ka Chun Cheung, Hailiang Yang. Optimal investment-consumption strategy in a discrete-time model with regime switching. Discrete & Continuous Dynamical Systems - B, 2007, 8 (2) : 315-332. doi: 10.3934/dcdsb.2007.8.315
[19]

Ming Yan, Hongtao Yang, Lei Zhang, Shuhua Zhang. Optimal investment-reinsurance policy with regime switching and value-at-risk constraint. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-17. doi: 10.3934/jimo.2019050
[20]

Jiaqin Wei, Zhuo Jin, Hailiang Yang. Optimal dividend policy with liability constraint under a hidden Markov regime-switching model. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1965-1993. doi: 10.3934/jimo.2018132

2018 Impact Factor: 1.025

Tools

Metrics

  • PDF downloads (7)
  • HTML views (0)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences