doi: 10.3934/jimo.2021025
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Robust optimal strategies of DC pension plans with stochastic volatility and stochastic income under mean-variance criteria

1. 

School of Mathematical Sciences, Tiangong University, Tianjin 300387, China

2. 

School of Mathematical Sciences, Tianjin University, Tianjin 300072, China

* Corresponding authors: Hao Chang and Hui Zhao

Received  March 2020 Revised  December 2020 Early access February 2021

Fund Project: This research is supported by the National Natural Science Foundation of China (Nos.71671122 and 11771329)

This paper studies a robust optimal investment problem under the mean-variance criterion for a defined contribution (DC) pension plan with an ambiguity-averse member (AAM), who worries about model misspecification and aims to find robust optimal strategy. The member has access to a risk-free asset (i.e., cash or bank account) and a risky asset (i.e., the stock) in a financial market. In order to get closer to the actual environment, we assume that both the income level and stock price are driven by Heston's stochastic volatility model. A continuous-time mean-variance model with ambiguity aversion for a DC pension plan is established. By using the Lagrangian multiplier method and stochastic optimal control theory, the closed-form expressions for robust efficient strategy and efficient frontier are derived. In addition, some special cases are derived in detail. Finally, a numerical example is presented to illustrate the effects of model parameters on the robust efficient strategy and the efficient frontier, and some economic implications have been revealed.

Citation: Hao Chang, Jiaao Li, Hui Zhao. Robust optimal strategies of DC pension plans with stochastic volatility and stochastic income under mean-variance criteria. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021025
References:
[1]

E. W. AndersonL. P. Hansen and T. J. Sargent, A quartet of semigroups for model specification, robustness, prices of risk, and model detection, Journal of the European Economic Association, 1 (2003), 68-123.  doi: 10.4324/9780203358061_chapter_16.  Google Scholar

[2]

E. W. Anderson, L. P. Hansen and T. J. Sargent, Robustness, detection and the price of risk, Working paper, University of Chicago, 1999. Available from: https://files.nyu.edu/ts43/public/research/.svn/text-base/ahs3.pdf.svn-base. Google Scholar

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N. Bäuerle, Benchmark and mean-variance problems for insurers, Mathematical Methods of Operations Research, 62 (2005), 159-165.  doi: 10.1007/s00186-005-0446-1.  Google Scholar

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J. BiZ. Liang and K. C. Yuen, Optimal mean-variance investment/reinsurance with common shock in a regime-switching market, Mathematical Methods of Operations Research, 90 (2019), 109-135.  doi: 10.1007/s00186-018-00657-3.  Google Scholar

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H. Chang, Dynamic mean-variance portfolio selection with liability and stochastic interest rate, Economic Modelling, 51 (2015), 172-182.  doi: 10.1016/j.econmod.2015.07.017.  Google Scholar

[6]

H. Chang and X. M. Rong, An investment and consumption problem with CIR interest rate and stochastic volatility, Abstract and Applied Analysis, 2013, 219397. doi: 10.1155/2013/219397.  Google Scholar

[7]

P. ChristoffersenK. Jacobs and K. Mimouni, Volatility dynamics for the S & P500: Evidence from realized volatility, daily returns, and option prices, The Review of Financial Studies, 23 (2010), 3141-3189.   Google Scholar

[8]

G. DeelstraM. Grasselli and P. F. Koehl, Optimal investment strategies in the presence of a minimum guarantee, Insurance: Mathematics and Economics, 33 (2003), 189-207.  doi: 10.1016/S0167-6687(03)00153-7.  Google Scholar

[9]

M. EscobarS. Ferrando and A. Rubtsov, Robust portfolio choice with derivative trading under stochastic volatility, Journal of Banking & Finance, 61 (2015), 142-157.   Google Scholar

[10]

R. Ferland and F. Watier, Mean-variance efficiency with extended CIR interest rates, Applied Stochastic Models in Business and Industry, 26 (2010), 71-84.  doi: 10.1002/asmb.767.  Google Scholar

[11]

C. FuA. Lari-Lavassani and X. Li, Dynamic mean-variance portfolio selection with borrowing constraint, European Journal of Operational Research, 200 (2010), 312-319.  doi: 10.1016/j.ejor.2009.01.005.  Google Scholar

[12]

M. D. GiacintoS. Federico and F. Gozzi, Pension funds with a minimum guarantee: A stochastic control approach, Finance and Stochastics, 15 (2011), 297-342.  doi: 10.1007/s00780-010-0127-7.  Google Scholar

[13]

G. Guan and Z. Liang, Optimal management of DC pension plan in a stochastic interest rate and stochastic volatility framework, Insurance: Mathematics and Economics, 57 (2014), 58-66.  doi: 10.1016/j.insmatheco.2014.05.004.  Google Scholar

[14]

S. Haberman and E. Vigna, Optimal investment strategies and risk measures in defined contribution pension schemes, Insurance: Mathematics and Economics, 31 (2002), 35-69.  doi: 10.1016/S0167-6687(02)00128-2.  Google Scholar

[15]

N. W. Han and M. W. Hung, Optimal asset allocation for DC pension plans under inflation, Insurance: Mathematics and Economics, 51 (2012), 172-181.  doi: 10.1016/j.insmatheco.2012.03.003.  Google Scholar

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L. Hansen and T. J. Sargent, Robust control and model uncertainty, American Economic Review, 91 (2001), 60-66.  doi: 10.1142/9789814578127_0005.  Google Scholar

[17]

S. L. Heston, A closed-form solution for options with stochastic volatility with applications to bond and currency options, The Review of Financial Studies, 6 (1993), 327-343.  doi: 10.1093/rfs/6.2.327.  Google Scholar

[18]

H. Y. Kim and F. G. Viens, Portfolio optimization in discrete time with proportional transaction costs under stochastic volatility, Annals of Finance, 8 (2012), 405-425.  doi: 10.1007/s10436-010-0149-3.  Google Scholar

[19]

D. Li and W. L. Ng, Optimal dynamic portfolio selection: Multiperiod mean-variance formulation, Mathematical finance, 10 (2000), 387-406.  doi: 10.1111/1467-9965.00100.  Google Scholar

[20]

D. LiX. Rong and H. Zhao, Optimal reinsurance and investment problem for an insurer and a reinsurer with jump-diffusion risk process under the Heston model, Computational and Applied Mathematics, 35 (2016), 533-557.  doi: 10.1007/s40314-014-0204-1.  Google Scholar

[21]

Z. Liang and M. Ma, Optimal dynamic asset allocation of pension fund in mortality and salary risks framework, Insurance: Mathematics and Economics, 64 (2015), 151-161.  doi: 10.1016/j.insmatheco.2015.05.008.  Google Scholar

[22]

A. E. Lim and X. Y. Zhao, Mean-variance portfolio selection with random parameters in a complete market, Mathematics of Operations Research, 27 (2002), 101-120.  doi: 10.1287/moor.27.1.101.337.  Google Scholar

[23]

P. J. Maenhout, Robust portfolio rules and detection-error probabilities for a mean-reverting risk premium, Journal of Economic Theory, 128 (2006), 136-163.  doi: 10.1016/j.jet.2005.12.012.  Google Scholar

[24]

P. J. Maenhout, Robust portfolio rules and asset pricing, Review of Financial Studies, 17 (2004), 951-983.  doi: 10.1093/rfs/hhh003.  Google Scholar

[25]

H. Markowitz, Portfolio selection, The Journal of Finance, 7 (1952), 77-91.   Google Scholar

[26]

C. U. OkonkwoB. O. OsuS. A. Ihedioha and C. Chibuisi, Optimal Investment Strategy for Defined Contribution Pension Scheme under the Heston Volatility Model, Journal of Mathematical Finance, 8 (2018), 613-622.  doi: 10.4236/jmf.2018.84039.  Google Scholar

[27]

R. Uppal and T. Wang, Model misspecification and underdiversification, The Journal of Finance, 58 (2003), 2465-2486.  doi: 10.1046/j.1540-6261.2003.00612.x.  Google Scholar

[28]

E. Vigna and S. Haberman, Optimal investment strategy for defined contribution pension schemes, Insurance: Mathematics and Economics, 28 (2001), 233-262.  doi: 10.1016/S0167-6687(00)00077-9.  Google Scholar

[29]

P. Wang and Z. Li, Robust optimal investment strategy for an AAM of DC pension plans with stochastic interest rate and stochastic volatility, Insurance: Mathematics and Economics, 80 (2018), 67-83.  doi: 10.1016/j.insmatheco.2018.03.003.  Google Scholar

[30]

H. YaoZ. Yang and P. Chen, Markowitz's mean-variance defined contribution pension fund management under inflation: A continuous-time model, Insurance: Mathematics and Economics, 53 (2013), 851-863.  doi: 10.1016/j.insmatheco.2013.10.002.  Google Scholar

[31]

B. YiZ. LiF. G. Viens and Y. Zeng, Robust optimal control for an insurer with reinsurance and investment under Heston's stochastic volatility model, Insurance: Mathematics and Economics, 53 (2013), 601-614.  doi: 10.1016/j.insmatheco.2013.08.011.  Google Scholar

[32]

B. YiF. ViensZ. Li and Y. Zeng, Robust optimal strategies for an insurer with reinsurance and investment under benchmark and mean-variance criteria, Scandinavian Actuarial Journal, 2015 (2015), 725-751.  doi: 10.1080/03461238.2014.883085.  Google Scholar

[33]

Y. ZengD. LiZ. Chen and Z. Yang, Ambiguity aversion and optimal derivative-based pension investment with stochastic income and volatility, Journal of Economic Dynamics and Control, 88 (2018), 70-103.  doi: 10.1016/j.jedc.2018.01.023.  Google Scholar

[34]

A. Zhang and C. O. Ewald, Optimal investment for a pension fund under inflation risk, Mathematical Methods of Operations Research, 71 (2010), 353-369.  doi: 10.1007/s00186-009-0294-5.  Google Scholar

[35]

H. ZhaoX. Rong and Y. Zhao, Optimal excess-of-loss reinsurance and investment problem for an insurer with jump-diffusion risk process under the Heston model, Insurance: Mathematics and Economics, 53 (2013), 504-514.  doi: 10.1016/j.insmatheco.2013.08.004.  Google Scholar

[36]

X. ZhengJ. Zhou and Z. Sun, Robust optimal portfolio and proportional reinsurance for an insurer under a CEV model, Insurance: Mathematics and Economics, 67 (2016), 77-87.  doi: 10.1016/j.insmatheco.2015.12.008.  Google Scholar

[37]

X. Y. Zhou and D. Li, Continuous-time mean-variance portfolio selection: A stochastic LQ framework, Applied Mathematics and Optimization, 42 (2000), 19-33.  doi: 10.1007/s002450010003.  Google Scholar

show all references

References:
[1]

E. W. AndersonL. P. Hansen and T. J. Sargent, A quartet of semigroups for model specification, robustness, prices of risk, and model detection, Journal of the European Economic Association, 1 (2003), 68-123.  doi: 10.4324/9780203358061_chapter_16.  Google Scholar

[2]

E. W. Anderson, L. P. Hansen and T. J. Sargent, Robustness, detection and the price of risk, Working paper, University of Chicago, 1999. Available from: https://files.nyu.edu/ts43/public/research/.svn/text-base/ahs3.pdf.svn-base. Google Scholar

[3]

N. Bäuerle, Benchmark and mean-variance problems for insurers, Mathematical Methods of Operations Research, 62 (2005), 159-165.  doi: 10.1007/s00186-005-0446-1.  Google Scholar

[4]

J. BiZ. Liang and K. C. Yuen, Optimal mean-variance investment/reinsurance with common shock in a regime-switching market, Mathematical Methods of Operations Research, 90 (2019), 109-135.  doi: 10.1007/s00186-018-00657-3.  Google Scholar

[5]

H. Chang, Dynamic mean-variance portfolio selection with liability and stochastic interest rate, Economic Modelling, 51 (2015), 172-182.  doi: 10.1016/j.econmod.2015.07.017.  Google Scholar

[6]

H. Chang and X. M. Rong, An investment and consumption problem with CIR interest rate and stochastic volatility, Abstract and Applied Analysis, 2013, 219397. doi: 10.1155/2013/219397.  Google Scholar

[7]

P. ChristoffersenK. Jacobs and K. Mimouni, Volatility dynamics for the S & P500: Evidence from realized volatility, daily returns, and option prices, The Review of Financial Studies, 23 (2010), 3141-3189.   Google Scholar

[8]

G. DeelstraM. Grasselli and P. F. Koehl, Optimal investment strategies in the presence of a minimum guarantee, Insurance: Mathematics and Economics, 33 (2003), 189-207.  doi: 10.1016/S0167-6687(03)00153-7.  Google Scholar

[9]

M. EscobarS. Ferrando and A. Rubtsov, Robust portfolio choice with derivative trading under stochastic volatility, Journal of Banking & Finance, 61 (2015), 142-157.   Google Scholar

[10]

R. Ferland and F. Watier, Mean-variance efficiency with extended CIR interest rates, Applied Stochastic Models in Business and Industry, 26 (2010), 71-84.  doi: 10.1002/asmb.767.  Google Scholar

[11]

C. FuA. Lari-Lavassani and X. Li, Dynamic mean-variance portfolio selection with borrowing constraint, European Journal of Operational Research, 200 (2010), 312-319.  doi: 10.1016/j.ejor.2009.01.005.  Google Scholar

[12]

M. D. GiacintoS. Federico and F. Gozzi, Pension funds with a minimum guarantee: A stochastic control approach, Finance and Stochastics, 15 (2011), 297-342.  doi: 10.1007/s00780-010-0127-7.  Google Scholar

[13]

G. Guan and Z. Liang, Optimal management of DC pension plan in a stochastic interest rate and stochastic volatility framework, Insurance: Mathematics and Economics, 57 (2014), 58-66.  doi: 10.1016/j.insmatheco.2014.05.004.  Google Scholar

[14]

S. Haberman and E. Vigna, Optimal investment strategies and risk measures in defined contribution pension schemes, Insurance: Mathematics and Economics, 31 (2002), 35-69.  doi: 10.1016/S0167-6687(02)00128-2.  Google Scholar

[15]

N. W. Han and M. W. Hung, Optimal asset allocation for DC pension plans under inflation, Insurance: Mathematics and Economics, 51 (2012), 172-181.  doi: 10.1016/j.insmatheco.2012.03.003.  Google Scholar

[16]

L. Hansen and T. J. Sargent, Robust control and model uncertainty, American Economic Review, 91 (2001), 60-66.  doi: 10.1142/9789814578127_0005.  Google Scholar

[17]

S. L. Heston, A closed-form solution for options with stochastic volatility with applications to bond and currency options, The Review of Financial Studies, 6 (1993), 327-343.  doi: 10.1093/rfs/6.2.327.  Google Scholar

[18]

H. Y. Kim and F. G. Viens, Portfolio optimization in discrete time with proportional transaction costs under stochastic volatility, Annals of Finance, 8 (2012), 405-425.  doi: 10.1007/s10436-010-0149-3.  Google Scholar

[19]

D. Li and W. L. Ng, Optimal dynamic portfolio selection: Multiperiod mean-variance formulation, Mathematical finance, 10 (2000), 387-406.  doi: 10.1111/1467-9965.00100.  Google Scholar

[20]

D. LiX. Rong and H. Zhao, Optimal reinsurance and investment problem for an insurer and a reinsurer with jump-diffusion risk process under the Heston model, Computational and Applied Mathematics, 35 (2016), 533-557.  doi: 10.1007/s40314-014-0204-1.  Google Scholar

[21]

Z. Liang and M. Ma, Optimal dynamic asset allocation of pension fund in mortality and salary risks framework, Insurance: Mathematics and Economics, 64 (2015), 151-161.  doi: 10.1016/j.insmatheco.2015.05.008.  Google Scholar

[22]

A. E. Lim and X. Y. Zhao, Mean-variance portfolio selection with random parameters in a complete market, Mathematics of Operations Research, 27 (2002), 101-120.  doi: 10.1287/moor.27.1.101.337.  Google Scholar

[23]

P. J. Maenhout, Robust portfolio rules and detection-error probabilities for a mean-reverting risk premium, Journal of Economic Theory, 128 (2006), 136-163.  doi: 10.1016/j.jet.2005.12.012.  Google Scholar

[24]

P. J. Maenhout, Robust portfolio rules and asset pricing, Review of Financial Studies, 17 (2004), 951-983.  doi: 10.1093/rfs/hhh003.  Google Scholar

[25]

H. Markowitz, Portfolio selection, The Journal of Finance, 7 (1952), 77-91.   Google Scholar

[26]

C. U. OkonkwoB. O. OsuS. A. Ihedioha and C. Chibuisi, Optimal Investment Strategy for Defined Contribution Pension Scheme under the Heston Volatility Model, Journal of Mathematical Finance, 8 (2018), 613-622.  doi: 10.4236/jmf.2018.84039.  Google Scholar

[27]

R. Uppal and T. Wang, Model misspecification and underdiversification, The Journal of Finance, 58 (2003), 2465-2486.  doi: 10.1046/j.1540-6261.2003.00612.x.  Google Scholar

[28]

E. Vigna and S. Haberman, Optimal investment strategy for defined contribution pension schemes, Insurance: Mathematics and Economics, 28 (2001), 233-262.  doi: 10.1016/S0167-6687(00)00077-9.  Google Scholar

[29]

P. Wang and Z. Li, Robust optimal investment strategy for an AAM of DC pension plans with stochastic interest rate and stochastic volatility, Insurance: Mathematics and Economics, 80 (2018), 67-83.  doi: 10.1016/j.insmatheco.2018.03.003.  Google Scholar

[30]

H. YaoZ. Yang and P. Chen, Markowitz's mean-variance defined contribution pension fund management under inflation: A continuous-time model, Insurance: Mathematics and Economics, 53 (2013), 851-863.  doi: 10.1016/j.insmatheco.2013.10.002.  Google Scholar

[31]

B. YiZ. LiF. G. Viens and Y. Zeng, Robust optimal control for an insurer with reinsurance and investment under Heston's stochastic volatility model, Insurance: Mathematics and Economics, 53 (2013), 601-614.  doi: 10.1016/j.insmatheco.2013.08.011.  Google Scholar

[32]

B. YiF. ViensZ. Li and Y. Zeng, Robust optimal strategies for an insurer with reinsurance and investment under benchmark and mean-variance criteria, Scandinavian Actuarial Journal, 2015 (2015), 725-751.  doi: 10.1080/03461238.2014.883085.  Google Scholar

[33]

Y. ZengD. LiZ. Chen and Z. Yang, Ambiguity aversion and optimal derivative-based pension investment with stochastic income and volatility, Journal of Economic Dynamics and Control, 88 (2018), 70-103.  doi: 10.1016/j.jedc.2018.01.023.  Google Scholar

[34]

A. Zhang and C. O. Ewald, Optimal investment for a pension fund under inflation risk, Mathematical Methods of Operations Research, 71 (2010), 353-369.  doi: 10.1007/s00186-009-0294-5.  Google Scholar

[35]

H. ZhaoX. Rong and Y. Zhao, Optimal excess-of-loss reinsurance and investment problem for an insurer with jump-diffusion risk process under the Heston model, Insurance: Mathematics and Economics, 53 (2013), 504-514.  doi: 10.1016/j.insmatheco.2013.08.004.  Google Scholar

[36]

X. ZhengJ. Zhou and Z. Sun, Robust optimal portfolio and proportional reinsurance for an insurer under a CEV model, Insurance: Mathematics and Economics, 67 (2016), 77-87.  doi: 10.1016/j.insmatheco.2015.12.008.  Google Scholar

[37]

X. Y. Zhou and D. Li, Continuous-time mean-variance portfolio selection: A stochastic LQ framework, Applied Mathematics and Optimization, 42 (2000), 19-33.  doi: 10.1007/s002450010003.  Google Scholar

Figure 1.  The effects of volatility parameters $ \alpha $, $ \sigma _0 $, $ \lambda _S $ and $ \rho $ on $ \pi ^{\ast }\left( t \right) $
Figure 2.  The effects of income parameters $ \mu $, $ \sigma _1 $ and $ k $ on $ \pi ^ {\ast }\left( t \right) $
Figure 3.  The effect of ambiguity-aversion coefficient $ \beta $ on $ \pi ^ {\ast }\left( t \right) $
Figure 4.  The effects of volatility parameters $ \alpha $ and $ \sigma _0 $ on $ \sigma \left( {X\left( T \right)} \right) $
Figure 5.  The effects of income parameters $ \mu $ and $ k $ on $ \sigma \left( {X\left( T \right)} \right) $
Figure 6.  The effect of ambiguity-aversion coefficient $ \beta $ on $ \sigma \left( {X\left( T \right)} \right) $
Figure 7.  Comparisons of the efficient strategies and the efficient frontiers
Figure 8.  When $ \rho = -0.65 $ and $ \lambda _S = 1.5 $, we have $ \Delta>0 $; when $ \rho = -0.87 $ and $ \lambda _S = 12.56 $, we get $ \Delta = 0 $; when $ \rho = -0.9 $ and $ \lambda _S = 10 $, we have $ \Delta<0 $. The effects of different symbols of $ \Delta $ on $ \pi ^ {\ast }(t) $ and $ \sigma (X(T)) $
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