• Previous Article
    Numerical solution to an inverse problem on a determination of places and capacities of sources in the hyperbolic systems
  • JIMO Home
  • This Issue
  • Next Article
    Optimal investment and risk control problems with delay for an insurer in defaultable market
doi: 10.3934/jimo.2019084

Multi-period optimal investment choice post-retirement with inter-temporal restrictions in a defined contribution pension plan

1. 

China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081, China

2. 

School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China

3. 

Beijing Branch, China Minsheng Banking Corporation Limited, Beijing 100031, China

4. 

School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510006, China

* Corresponding author: zengli@gdufs.edu.cn

Received  October 2018 Revised  March 2019 Published  July 2019

Fund Project: The first author is supported by grants from National Natural Science Foundation of China (Nos. 11671411, 11771465)

This paper studies a multi-period portfolio selection problem during the post-retirement phase of a defined contribution pension plan. The retiree is allowed to defer the purchase of the annuity until the time of compulsory annuitization. A series of investment targets over time are set, and restrictions on the inter-temporal expected values of the portfolio are considered. We aim to minimize the accumulated variances from the time of retirement to the time of compulsory annuitization. Using the Lagrange multiplier technique and dynamic programming, we study in detail the existence of the optimal strategy and derive its closed-form expression. For comparison purposes, the explicit solution of the classical target-based model is also provided. The properties of the optimal investment strategy, the probabilities of achieving a worse or better pension at the time of compulsory annuitization and the bankruptcy probability are compared in detail under two models. The comparison shows that our model can greatly decrease the probability of achieving a worse pension at the compulsory time and can significantly increase the probability of achieving a better pension.

Citation: Huiling Wu, Xiuguo Wang, Yuanyuan Liu, Li Zeng. Multi-period optimal investment choice post-retirement with inter-temporal restrictions in a defined contribution pension plan. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2019084
References:
[1]

M. AkianJ. Menaldi and A. Sulem, On an investment-consumption model with transaction costs, SIAM Journal on Control and Optimization, 34 (1996), 329-364.  doi: 10.1137/S0363012993247159.  Google Scholar

[2]

P. Albrecht and R. Maurer, Self-annuitization, consumption shortfall in retirement and asset allocation: The annuity benchmark, Journal of Pension Economics and Finance, 1 (2002), 269-288.  doi: 10.1017/S1474747202001117.  Google Scholar

[3]

P. BattocchioF. Menoncin and O. Scaillet, Optimal asset allocation for pension funds under mortality risk during the accumulation and decumulation phases, Annals of Operations Research, 152 (2007), 141-165.  doi: 10.1007/s10479-006-0144-2.  Google Scholar

[4]

E. Bayraktar and V. R. Young, Minimizing the probability of lifetime ruin with deferred life annuities, North American Actuarial Journal, 13 (2009), 141-154.  doi: 10.1080/10920277.2009.10597543.  Google Scholar

[5]

M. S. Bazaraa, H. D. Sherali and C. M. Shetty, Nonlinear Programming: Theory and Algorithms, Wiley Interscience, New Jersey, 2006. doi: 10.1002/0471787779.  Google Scholar

[6]

D. BlakeA. J. G. Cairns and K. Dowd, Pensionmetrics 2: Stochastic pension plan design during the distribution phase, Insurance: Mathematics and Economics, 33 (2003), 29-47.  doi: 10.1016/S0167-6687(03)00141-0.  Google Scholar

[7]

J. R. Brown, Private pensions, mortality risk, and the decision to annuitize, Journal of Public Economics, 82 (2001), 29-62.  doi: 10.3386/w7191.  Google Scholar

[8]

O. L. V. Costa and R. B. Nabholz, Multiperiod mean-variance optimization with intertemporal restrictions, Journal of Optimization Theory and Applications, 134 (2007), 257-274.  doi: 10.1007/s10957-007-9233-x.  Google Scholar

[9]

H. Dadashi, Optimal investment-consumption problem post-retirement with a minimum guarantee, Working paper, arXiv: 1803.00611, 2018. doi: 10.1016/j.cam.2019.02.027.  Google Scholar

[10]

M. Di GiacintoS. FedericoF. Gozzi and E. Vigna, Income drawdown option with minimum guarantee, European Journal of Operational Research, 234 (2014), 610-624.  doi: 10.1016/j.ejor.2013.10.026.  Google Scholar

[11]

P. Emms, Relative choice models for income drawdown in a defined contribution pension scheme, North American Actuarial Journal, 14 (2010), 176-197.  doi: 10.1080/10920277.2010.10597584.  Google Scholar

[12]

P. Emms and S. Haberman, Income drawdown schemes for a defined-contribution pension plan, The Journal of Risk and Insurance, 75 (2008), 739-761.  doi: 10.1080/10920277.2010.10597584.  Google Scholar

[13]

A. Finkelstein and J. Poterba, Selection effects in the United Kingdom individual annuities market, Economic Journal, 112 (2002), 28-50.  doi: 10.1111/1468-0297.0j672.  Google Scholar

[14]

A. M. Geoffrion, Duality in nonlinear programming: A simplified application-oriented development, SIAM Review, 13 (1971), 1-37.  doi: 10.1137/1013001.  Google Scholar

[15]

R. GerrardS. Haberman and E. Vigna, Optimal investment choices post-retirement in a defined contribution pension scheme, Insurance: Mathematics and Economics, 35 (2004), 321-342.  doi: 10.1016/j.insmatheco.2004.06.002.  Google Scholar

[16]

R. GerrardS. Haberman and E. Vigna, The management of decumulation risks in a defined contribution pension plan, North American Actuarial Journal, 10 (2006), 84-110.  doi: 10.1080/10920277.2006.10596241.  Google Scholar

[17]

R. GerrardB. Høgaard and E. Vigna, Choosing the optimal annuitization time post retirement, Quantitative Finance, 12 (2012), 1143-1159.  doi: 10.1080/14697680903358248.  Google Scholar

[18]

J. InkmannP. Lopes and A. Michaelides, How deep is the annuity market participation puzzle?, The Review of Financial Studies, 24 (2011), 279-319.   Google Scholar

[19]

C. W. LinL. Zeng and H. L. Wu, Multi-period portfolio optimization in a defined contribution pension plan during the decumulation phase, J. Ind. Manag. Optim., 15 (2019), 401-427.  doi: 10.3934/jimo.2018059.  Google Scholar

[20]

L. M. Lockwood, Bequest motives and the annuity puzzle, Review of Economic Dynamics, 15 (2012), 226-243.  doi: 10.1016/j.red.2011.03.001.  Google Scholar

[21]

F. Menoncin and E. Vigna, Mean-variance target-based optimisation for defined contribution pension schemes in a stochastic framework, Insurance: Mathmatics and Economics, 76 (2017), 172-184.  doi: 10.1016/j.insmatheco.2017.08.002.  Google Scholar

[22]

M. A. Milevsky, Optimal annuitization policies: Analysis of the options, North American Actuarial Journal, 5 (2001), 57-69.  doi: 10.1080/10920277.2001.10595953.  Google Scholar

[23]

M. A. Milevsky and C. Robinson, Self-annuitization and ruin in retirement, North American Actuarial Journal, 4 (2000), 113-129.  doi: 10.1080/10920277.2000.10595940.  Google Scholar

[24]

M. A. MilevskyK. S. Moore and V. R. Young, Optimal asset allocation and ruin minimization annuitization strategies, Mathematical Finance, 16 (2006), 647-671.  doi: 10.1016/j.jedc.2006.11.003.  Google Scholar

[25]

M. A. Milevsky and V. R. Young, Annuitization and asset allocation, Journal of Economic Dynamics and Control, 31 (2007), 3138-3177.  doi: 10.1016/j.jedc.2006.11.003.  Google Scholar

[26]

G. Stabile, Optimal timing of the annuity purchase: combined stochastic control and optimal stopping problem, International Journal of Theoretical and Applied Finance, 9 (2006), 151-170.  doi: 10.1142/S0219024906003524.  Google Scholar

[27]

B. Z. TemocinR. Korn and A. S. Selcuk-Kestel, Constant proportion portfolio insurance in defined contribution pension plan management under discrete-time trading, Annals of Operations Research, 266 (2018), 329-348.  doi: 10.1007/s10479-017-2449-8.  Google Scholar

[28]

E. Vigna, On efficiency of mean-variance based portfolio selection in defined contribution pension schemes, Quantitative Finance, 14 (2014), 237-258.  doi: 10.1080/14697688.2012.708778.  Google Scholar

[29]

H. L. Wu and Y. Zeng, Equilibrium investment strategy for defined-contribution pension schemes with generalized mean-variance criterion and mortality risk, Insurance: Mathematics and Economics, 64 (2015), 396-408.  doi: 10.1016/j.insmatheco.2015.07.007.  Google Scholar

[30]

X. Y. Zhang and J. Y. Guo, The role of inflation-indexed bond in optimal management of defined contribution pension plan during the decumulation phase, Risks, 24 (2018).  doi: 10.3390/risks6020024.  Google Scholar

[31]

S. S. ZhuD. Li and S. Y. Wang, Risk control over bankruptcy in dynamic portfolio selection: A generalized mean-variance formulation, IEEE Transactions on Automatic Control, 49 (2004), 447-457.  doi: 10.1109/TAC.2004.824474.  Google Scholar

show all references

References:
[1]

M. AkianJ. Menaldi and A. Sulem, On an investment-consumption model with transaction costs, SIAM Journal on Control and Optimization, 34 (1996), 329-364.  doi: 10.1137/S0363012993247159.  Google Scholar

[2]

P. Albrecht and R. Maurer, Self-annuitization, consumption shortfall in retirement and asset allocation: The annuity benchmark, Journal of Pension Economics and Finance, 1 (2002), 269-288.  doi: 10.1017/S1474747202001117.  Google Scholar

[3]

P. BattocchioF. Menoncin and O. Scaillet, Optimal asset allocation for pension funds under mortality risk during the accumulation and decumulation phases, Annals of Operations Research, 152 (2007), 141-165.  doi: 10.1007/s10479-006-0144-2.  Google Scholar

[4]

E. Bayraktar and V. R. Young, Minimizing the probability of lifetime ruin with deferred life annuities, North American Actuarial Journal, 13 (2009), 141-154.  doi: 10.1080/10920277.2009.10597543.  Google Scholar

[5]

M. S. Bazaraa, H. D. Sherali and C. M. Shetty, Nonlinear Programming: Theory and Algorithms, Wiley Interscience, New Jersey, 2006. doi: 10.1002/0471787779.  Google Scholar

[6]

D. BlakeA. J. G. Cairns and K. Dowd, Pensionmetrics 2: Stochastic pension plan design during the distribution phase, Insurance: Mathematics and Economics, 33 (2003), 29-47.  doi: 10.1016/S0167-6687(03)00141-0.  Google Scholar

[7]

J. R. Brown, Private pensions, mortality risk, and the decision to annuitize, Journal of Public Economics, 82 (2001), 29-62.  doi: 10.3386/w7191.  Google Scholar

[8]

O. L. V. Costa and R. B. Nabholz, Multiperiod mean-variance optimization with intertemporal restrictions, Journal of Optimization Theory and Applications, 134 (2007), 257-274.  doi: 10.1007/s10957-007-9233-x.  Google Scholar

[9]

H. Dadashi, Optimal investment-consumption problem post-retirement with a minimum guarantee, Working paper, arXiv: 1803.00611, 2018. doi: 10.1016/j.cam.2019.02.027.  Google Scholar

[10]

M. Di GiacintoS. FedericoF. Gozzi and E. Vigna, Income drawdown option with minimum guarantee, European Journal of Operational Research, 234 (2014), 610-624.  doi: 10.1016/j.ejor.2013.10.026.  Google Scholar

[11]

P. Emms, Relative choice models for income drawdown in a defined contribution pension scheme, North American Actuarial Journal, 14 (2010), 176-197.  doi: 10.1080/10920277.2010.10597584.  Google Scholar

[12]

P. Emms and S. Haberman, Income drawdown schemes for a defined-contribution pension plan, The Journal of Risk and Insurance, 75 (2008), 739-761.  doi: 10.1080/10920277.2010.10597584.  Google Scholar

[13]

A. Finkelstein and J. Poterba, Selection effects in the United Kingdom individual annuities market, Economic Journal, 112 (2002), 28-50.  doi: 10.1111/1468-0297.0j672.  Google Scholar

[14]

A. M. Geoffrion, Duality in nonlinear programming: A simplified application-oriented development, SIAM Review, 13 (1971), 1-37.  doi: 10.1137/1013001.  Google Scholar

[15]

R. GerrardS. Haberman and E. Vigna, Optimal investment choices post-retirement in a defined contribution pension scheme, Insurance: Mathematics and Economics, 35 (2004), 321-342.  doi: 10.1016/j.insmatheco.2004.06.002.  Google Scholar

[16]

R. GerrardS. Haberman and E. Vigna, The management of decumulation risks in a defined contribution pension plan, North American Actuarial Journal, 10 (2006), 84-110.  doi: 10.1080/10920277.2006.10596241.  Google Scholar

[17]

R. GerrardB. Høgaard and E. Vigna, Choosing the optimal annuitization time post retirement, Quantitative Finance, 12 (2012), 1143-1159.  doi: 10.1080/14697680903358248.  Google Scholar

[18]

J. InkmannP. Lopes and A. Michaelides, How deep is the annuity market participation puzzle?, The Review of Financial Studies, 24 (2011), 279-319.   Google Scholar

[19]

C. W. LinL. Zeng and H. L. Wu, Multi-period portfolio optimization in a defined contribution pension plan during the decumulation phase, J. Ind. Manag. Optim., 15 (2019), 401-427.  doi: 10.3934/jimo.2018059.  Google Scholar

[20]

L. M. Lockwood, Bequest motives and the annuity puzzle, Review of Economic Dynamics, 15 (2012), 226-243.  doi: 10.1016/j.red.2011.03.001.  Google Scholar

[21]

F. Menoncin and E. Vigna, Mean-variance target-based optimisation for defined contribution pension schemes in a stochastic framework, Insurance: Mathmatics and Economics, 76 (2017), 172-184.  doi: 10.1016/j.insmatheco.2017.08.002.  Google Scholar

[22]

M. A. Milevsky, Optimal annuitization policies: Analysis of the options, North American Actuarial Journal, 5 (2001), 57-69.  doi: 10.1080/10920277.2001.10595953.  Google Scholar

[23]

M. A. Milevsky and C. Robinson, Self-annuitization and ruin in retirement, North American Actuarial Journal, 4 (2000), 113-129.  doi: 10.1080/10920277.2000.10595940.  Google Scholar

[24]

M. A. MilevskyK. S. Moore and V. R. Young, Optimal asset allocation and ruin minimization annuitization strategies, Mathematical Finance, 16 (2006), 647-671.  doi: 10.1016/j.jedc.2006.11.003.  Google Scholar

[25]

M. A. Milevsky and V. R. Young, Annuitization and asset allocation, Journal of Economic Dynamics and Control, 31 (2007), 3138-3177.  doi: 10.1016/j.jedc.2006.11.003.  Google Scholar

[26]

G. Stabile, Optimal timing of the annuity purchase: combined stochastic control and optimal stopping problem, International Journal of Theoretical and Applied Finance, 9 (2006), 151-170.  doi: 10.1142/S0219024906003524.  Google Scholar

[27]

B. Z. TemocinR. Korn and A. S. Selcuk-Kestel, Constant proportion portfolio insurance in defined contribution pension plan management under discrete-time trading, Annals of Operations Research, 266 (2018), 329-348.  doi: 10.1007/s10479-017-2449-8.  Google Scholar

[28]

E. Vigna, On efficiency of mean-variance based portfolio selection in defined contribution pension schemes, Quantitative Finance, 14 (2014), 237-258.  doi: 10.1080/14697688.2012.708778.  Google Scholar

[29]

H. L. Wu and Y. Zeng, Equilibrium investment strategy for defined-contribution pension schemes with generalized mean-variance criterion and mortality risk, Insurance: Mathematics and Economics, 64 (2015), 396-408.  doi: 10.1016/j.insmatheco.2015.07.007.  Google Scholar

[30]

X. Y. Zhang and J. Y. Guo, The role of inflation-indexed bond in optimal management of defined contribution pension plan during the decumulation phase, Risks, 24 (2018).  doi: 10.3390/risks6020024.  Google Scholar

[31]

S. S. ZhuD. Li and S. Y. Wang, Risk control over bankruptcy in dynamic portfolio selection: A generalized mean-variance formulation, IEEE Transactions on Automatic Control, 49 (2004), 447-457.  doi: 10.1109/TAC.2004.824474.  Google Scholar

Figure 1.  $ \eta_n- {{\rm{E}}}_{0,x_0}\left(X^{\tilde\pi}_{n}\right) $, $ n = 1,2,\ldots,T $
Table 1.  The targets over time
$ \eta_1=198747 $ $ \eta_2=197494 $ $ \eta_3=196241 $ $ \eta_4=194988 $ $ \eta_5=193735 $
$ \eta_6=192482 $ $ \eta_7=191229 $ $ \eta_8=189976 $ $ \eta_9=188723 $ $ \eta_{10}=187470 $
$ \eta_{11}=186217 $ $ \eta_{12}=184964 $ $ \eta_{13}=183711 $ $ \eta_{14}=182458 $ $ \eta_{15}=181205 $
$ \eta_1=198747 $ $ \eta_2=197494 $ $ \eta_3=196241 $ $ \eta_4=194988 $ $ \eta_5=193735 $
$ \eta_6=192482 $ $ \eta_7=191229 $ $ \eta_8=189976 $ $ \eta_9=188723 $ $ \eta_{10}=187470 $
$ \eta_{11}=186217 $ $ \eta_{12}=184964 $ $ \eta_{13}=183711 $ $ \eta_{14}=182458 $ $ \eta_{15}=181205 $
Table 2.  The targets over time
$ \eta_1=201356 $ $ \eta_2=202333 $ $ \eta_3=202942 $ $ \eta_4=203160 $ $ \eta_5=202960 $
$ \eta_6=202362 $ $ \eta_7=201386 $ $ \eta_8=200057 $ $ \eta_9=198330 $ $ \eta_{10}=196284 $
$ \eta_{11}=193886 $ $ \eta_{12}=191191 $ $ \eta_{13}=188231 $ $ \eta_{14}=184908 $ $ \eta_{15}=181193 $
$ \eta_1=201356 $ $ \eta_2=202333 $ $ \eta_3=202942 $ $ \eta_4=203160 $ $ \eta_5=202960 $
$ \eta_6=202362 $ $ \eta_7=201386 $ $ \eta_8=200057 $ $ \eta_9=198330 $ $ \eta_{10}=196284 $
$ \eta_{11}=193886 $ $ \eta_{12}=191191 $ $ \eta_{13}=188231 $ $ \eta_{14}=184908 $ $ \eta_{15}=181193 $
Table 3.  The frequencies that $ X_{T}/ $ä$ _{75} $$ <\zeta_0 $
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_1\; \; \text{Our model} $ 0.2774 0.2661 0.2535
$ p_2\; \; \text{Target-based model} $ 0.5394 0.4597 0.3975
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_1\; \; \text{Our model} $ 0.3300 0.3151 0.3024
$ p_2\; \; \text{Target-based model} $ 0.6624 0.5736 0.4957
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_1\; \; \text{Our model} $ 0.3661 0.3507 0.3360
$ p_2\; \; \text{Target-based model} $ 0.7428 0.6532 0.5672
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_1\; \; \text{Our model} $ 0.2759 0.2611 0.2523
$ p_2\; \; \text{Target-based model} $ 0.5125 0.4391 0.3760
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_1\; \; \text{Our model} $ 0.3302 0.3167 0.3090
$ p_2\; \; \text{Target-based model} $ 0.6395 0.5450 0.4763
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_1\; \; \text{Our model} $ 0.3641 0.3545 0.3431
$ p_2\; \; \text{Target-based model} $ 0.7178 0.6313 0.5508
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_1\; \; \text{Our model} $ 0.2774 0.2661 0.2535
$ p_2\; \; \text{Target-based model} $ 0.5394 0.4597 0.3975
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_1\; \; \text{Our model} $ 0.3300 0.3151 0.3024
$ p_2\; \; \text{Target-based model} $ 0.6624 0.5736 0.4957
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_1\; \; \text{Our model} $ 0.3661 0.3507 0.3360
$ p_2\; \; \text{Target-based model} $ 0.7428 0.6532 0.5672
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_1\; \; \text{Our model} $ 0.2759 0.2611 0.2523
$ p_2\; \; \text{Target-based model} $ 0.5125 0.4391 0.3760
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_1\; \; \text{Our model} $ 0.3302 0.3167 0.3090
$ p_2\; \; \text{Target-based model} $ 0.6395 0.5450 0.4763
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_1\; \; \text{Our model} $ 0.3641 0.3545 0.3431
$ p_2\; \; \text{Target-based model} $ 0.7178 0.6313 0.5508
Table 4.  The gap between $ p_1 $ and $ p_2 $
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_2-p_1 $ 0.2620 0.1936 0.1440
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_2-p_1 $ 0.3324 0.2585 0.1933
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_2-p_1 $ 0.3767 0.3025 0.2312
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_2-p_1 $ 0.2366 0.1780 0.1237
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_2-p_1 $ 0.3093 0.2283 0.1673
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_2-p_1 $ 0.3537 0.2768 0.2077
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_2-p_1 $ 0.2620 0.1936 0.1440
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_2-p_1 $ 0.3324 0.2585 0.1933
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_2-p_1 $ 0.3767 0.3025 0.2312
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n)=0.4 $ $ p_2-p_1 $ 0.2366 0.1780 0.1237
$ {{\rm{Var}}}(R_n)=0.6 $ $ p_2-p_1 $ 0.3093 0.2283 0.1673
$ {{\rm{Var}}}(R_n)=0.8 $ $ p_2-p_1 $ 0.3537 0.2768 0.2077
Table 5.  The frequencies that $ X_{T}/ $ä$ _{75} $$ \ge k\zeta_0 $
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n) = 0.4 $ $ g_1\; \; \text{Our model} $ 0.5717 0.5725 0.5762
$ g_2\; \; \text{Target-based model} $ 0.0038 0.0045 0.0040
$ {{\rm{Var}}}(R_n) = 0.6 $ $ g_1\; \; \text{Our model} $ 0.5458 0.5466 0.5493
$ g_2\; \; \text{Target-based model} $ 0.0036 0.0028 0.0027
$ {{\rm{Var}}}(R_n) = 0.8 $ $ g_1\; \; \text{Our model} $ 0.5198 0.5167 0.5250
$ g_2\; \; \text{Target-based model} $ 0.0037 0.0029 0.0027
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n) = 0.4 $ $ g_1\; \; \text{Our model} $ 0.3042 0.4329 0.5427
$ g_2\; \; \text{Target-based model} $ 0.0001 0.0004 0.0018
$ {{\rm{Var}}}(R_n) = 0.6 $ $ g_1\; \; \text{Our model} $ 0.3530 0.4405 0.5220
$ g_2\; \; \text{Target-based model} $ 0.0003 0.00045 0.0019
$ {{\rm{Var}}}(R_n) = 0.8 $ $ g_1\; \; \text{Our model} $ 0.3617 0.4293 0.5033
$ g_2\; \; \text{Target-based model} $ 0.00005 0.0003 0.0013
The first decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n) = 0.4 $ $ g_1\; \; \text{Our model} $ 0.5717 0.5725 0.5762
$ g_2\; \; \text{Target-based model} $ 0.0038 0.0045 0.0040
$ {{\rm{Var}}}(R_n) = 0.6 $ $ g_1\; \; \text{Our model} $ 0.5458 0.5466 0.5493
$ g_2\; \; \text{Target-based model} $ 0.0036 0.0028 0.0027
$ {{\rm{Var}}}(R_n) = 0.8 $ $ g_1\; \; \text{Our model} $ 0.5198 0.5167 0.5250
$ g_2\; \; \text{Target-based model} $ 0.0037 0.0029 0.0027
The second decision mechanism of targets
Final target $ 1.5\zeta_0 $ä$ _{75} $ $ 1.7\zeta_0 $ä$ _{75} $ $ 2\zeta_0 $ä$ _{75} $
$ {{\rm{Var}}}(R_n) = 0.4 $ $ g_1\; \; \text{Our model} $ 0.3042 0.4329 0.5427
$ g_2\; \; \text{Target-based model} $ 0.0001 0.0004 0.0018
$ {{\rm{Var}}}(R_n) = 0.6 $ $ g_1\; \; \text{Our model} $ 0.3530 0.4405 0.5220
$ g_2\; \; \text{Target-based model} $ 0.0003 0.00045 0.0019
$ {{\rm{Var}}}(R_n) = 0.8 $ $ g_1\; \; \text{Our model} $ 0.3617 0.4293 0.5033
$ g_2\; \; \text{Target-based model} $ 0.00005 0.0003 0.0013
Table 6.  The frequencies of bankruptcies–20000 simulations
The first decision mechanism of targets
$ \eta_T=1.5\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 16355(0.8177) 19774(0.9887)
$ S\neq 0 $ 3645(0.1823) 226(0.0113)
Our model Target-based model
Mean of $ S $ 0.6833 0.0181
$ \eta_T=2.0\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 14424(0.7212) 18909(0.9455)
$ S\neq 0 $ 5576(0.2788) 1091(0.0546)
Our model Target-based model
Mean of $ S $ 1.3019 0.1135
The second decision mechanism of targets
$ \eta_T=1.5\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 16270(0.8135) 19667(0.9833)
$ S\neq 0 $ 3730(0.1865) 333(0.0167)
Our model Target-based model
Mean of $ S $ 0.8719 0.0297
$ \eta_T=2.0\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 14118(0.7059) 18726(0.9363)
$ S\neq 0 $ 5882(0.2941) 1274(0.0637)
Our model Target-based model
Mean of $ S $ 1.6798 0.1457
The first decision mechanism of targets
$ \eta_T=1.5\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 16355(0.8177) 19774(0.9887)
$ S\neq 0 $ 3645(0.1823) 226(0.0113)
Our model Target-based model
Mean of $ S $ 0.6833 0.0181
$ \eta_T=2.0\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 14424(0.7212) 18909(0.9455)
$ S\neq 0 $ 5576(0.2788) 1091(0.0546)
Our model Target-based model
Mean of $ S $ 1.3019 0.1135
The second decision mechanism of targets
$ \eta_T=1.5\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 16270(0.8135) 19667(0.9833)
$ S\neq 0 $ 3730(0.1865) 333(0.0167)
Our model Target-based model
Mean of $ S $ 0.8719 0.0297
$ \eta_T=2.0\zeta_0 $ä$ _{75} $
Events Our model Target-based model
Number of simulations(Frequencies) Number of simulations(Frequencies)
$ S=0 $ 14118(0.7059) 18726(0.9363)
$ S\neq 0 $ 5882(0.2941) 1274(0.0637)
Our model Target-based model
Mean of $ S $ 1.6798 0.1457
Table 7.  Comparison summary
The first decision mechanism of targets
Comparison items Our model Target-based model Times
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.2774 0.5394 0.5143
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.5717 0.0038 150.4
Bankruptcy probabilities
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.1823 0.0113 16.63
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2535 0.3975 0.6377
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.5762 0.0040 144.05
Bankruptcy probabilities
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2788 0.0546 5.106
The second decision mechanism of targets
Comparison items Our model Target-based model Times
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.2759 0.5125 0.5383
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.3042 0.0001 3042
Bankruptcy probabilities
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.1865 0.0167 11.17
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2523 0.3760 0.6710
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.5427 0.0018 301.5
Bankruptcy probabilities
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2941 0.0637 4.617
The first decision mechanism of targets
Comparison items Our model Target-based model Times
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.2774 0.5394 0.5143
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.5717 0.0038 150.4
Bankruptcy probabilities
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.1823 0.0113 16.63
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2535 0.3975 0.6377
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.5762 0.0040 144.05
Bankruptcy probabilities
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2788 0.0546 5.106
The second decision mechanism of targets
Comparison items Our model Target-based model Times
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.2759 0.5125 0.5383
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.3042 0.0001 3042
Bankruptcy probabilities
$ \eta_T=1.5\zeta_0 $ä$ _{75} $ 0.1865 0.0167 11.17
Frequencies that $ X_{T}/ $ä$ _{75} $$<\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2523 0.3760 0.6710
Frequencies that $ X_{T}/ $ä$ _{75} $$ \geq k\zeta_0 $
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.5427 0.0018 301.5
Bankruptcy probabilities
$ \eta_T=2.0\zeta_0 $ä$ _{75} $ 0.2941 0.0637 4.617
[1]

Qian Zhao, Rongming Wang, Jiaqin Wei. Time-inconsistent consumption-investment problem for a member in a defined contribution pension plan. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1557-1585. doi: 10.3934/jimo.2016.12.1557

[2]

Chuangwei Lin, Li Zeng, Huiling Wu. Multi-period portfolio optimization in a defined contribution pension plan during the decumulation phase. Journal of Industrial & Management Optimization, 2019, 15 (1) : 401-427. doi: 10.3934/jimo.2018059

[3]

Rein Luus. Optimal control of oscillatory systems by iterative dynamic programming. Journal of Industrial & Management Optimization, 2008, 4 (1) : 1-15. doi: 10.3934/jimo.2008.4.1

[4]

Ryan Loxton, Qun Lin. Optimal fleet composition via dynamic programming and golden section search. Journal of Industrial & Management Optimization, 2011, 7 (4) : 875-890. doi: 10.3934/jimo.2011.7.875

[5]

Haiying Liu, Wenjie Bi, Kok Lay Teo, Naxing Liu. Dynamic optimal decision making for manufacturers with limited attention based on sparse dynamic programming. Journal of Industrial & Management Optimization, 2019, 15 (2) : 445-464. doi: 10.3934/jimo.2018050

[6]

Ellina Grigorieva, Evgenii Khailov. Optimal control of a commercial loan repayment plan. Conference Publications, 2005, 2005 (Special) : 345-354. doi: 10.3934/proc.2005.2005.345

[7]

Lin He, Zongxia Liang. Optimal pension decision under heterogeneous health statuses and bequest motives. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1641-1659. doi: 10.3934/jimo.2017011

[8]

Yan-An Hwang, Yu-Hsien Liao. Reduction and dynamic approach for the multi-choice Shapley value. Journal of Industrial & Management Optimization, 2013, 9 (4) : 885-892. doi: 10.3934/jimo.2013.9.885

[9]

Haibo Jin, Long Hai, Xiaoliang Tang. An optimal maintenance strategy for multi-state systems based on a system linear integral equation and dynamic programming. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-26. doi: 10.3934/jimo.2018188

[10]

Fengjun Wang, Qingling Zhang, Bin Li, Wanquan Liu. Optimal investment strategy on advertisement in duopoly. Journal of Industrial & Management Optimization, 2016, 12 (2) : 625-636. doi: 10.3934/jimo.2016.12.625

[11]

Xin Jiang, Kam Chuen Yuen, Mi Chen. Optimal investment and reinsurance with premium control. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-17. doi: 10.3934/jimo.2019080

[12]

Andrzej Nowakowski, Jan Sokolowski. On dual dynamic programming in shape control. Communications on Pure & Applied Analysis, 2012, 11 (6) : 2473-2485. doi: 10.3934/cpaa.2012.11.2473

[13]

Jérôme Renault. General limit value in dynamic programming. Journal of Dynamics & Games, 2014, 1 (3) : 471-484. doi: 10.3934/jdg.2014.1.471

[14]

Alina Toma, Bruno Sixou, Françoise Peyrin. Iterative choice of the optimal regularization parameter in TV image restoration. Inverse Problems & Imaging, 2015, 9 (4) : 1171-1191. doi: 10.3934/ipi.2015.9.1171

[15]

Rongfei Liu, Dingcheng Wang, Jiangyan Peng. Infinite-time ruin probability of a renewal risk model with exponential Levy process investment and dependent claims and inter-arrival times. Journal of Industrial & Management Optimization, 2017, 13 (2) : 995-1007. doi: 10.3934/jimo.2016058

[16]

Jingzhen Liu, Lihua Bai, Ka-Fai Cedric Yiu. Optimal investment with a value-at-risk constraint. Journal of Industrial & Management Optimization, 2012, 8 (3) : 531-547. doi: 10.3934/jimo.2012.8.531

[17]

Jingzhen Liu, Ka-Fai Cedric Yiu, Kok Lay Teo. Optimal investment-consumption problem with constraint. Journal of Industrial & Management Optimization, 2013, 9 (4) : 743-768. doi: 10.3934/jimo.2013.9.743

[18]

Zuo Quan Xu, Fahuai Yi. An optimal consumption-investment model with constraint on consumption. Mathematical Control & Related Fields, 2016, 6 (3) : 517-534. doi: 10.3934/mcrf.2016014

[19]

Lei Sun, Lihong Zhang. Optimal consumption and investment under irrational beliefs. Journal of Industrial & Management Optimization, 2011, 7 (1) : 139-156. doi: 10.3934/jimo.2011.7.139

[20]

Haili Yuan, Yijun Hu. Optimal investment for an insurer under liquid reserves. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2019114

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (18)
  • HTML views (171)
  • Cited by (0)

Other articles
by authors

[Back to Top]