doi: 10.3934/jimo.2019076

How's the performance of the optimized portfolios by safety-first rules: Theory with empirical comparisons

1. 

Faculty of Business, Ningbo University, Ningbo 315211, China

2. 

Southampton Statistical Sciences Research Institute, and Mathematical Sciences, University of Southampton, SO17 1BJ, UK

Received  July 2018 Revised  February 2019 Published  July 2019

Safety-first (SF) rules have been increasingly useful in particular for construction of optimal portfolios related to pension and other social insurance funds. How's the performance of the optimal portfolios constructed by different SF rules is an interesting practical question but yet less investigated theoretically. In this paper, we therefore analytically investigate the properties of the risky portfolios constructed by the three popular SF rules, denoted by the RSF, TSF and KSF, which are suggested and developed by A. D. Roy, L. G. Telser and S. Kataoka, respectively. Using Sharpe ratio as a measure of portfolio performance, we theoretically derive that the performance of an optimal portfolio constructed by the KSF approach depends on an acceptable level of extreme risk tolerance. The unique solution where the performance of the KSF portfolio is the same as that of the other two SF portfolios is found. By this we interestingly find that except this special case, under the finite optimal portfolios existent, the KSF portfolio always dominates the TSF portfolio in terms of the Sharpe ratio. In addition, in some market scenarios, even when the RSF and TSF portfolios do not exist in finite forms, the KSF rule can still apply to get a finite optimal portfolio. Moreover, in comparison with the RSF rule, a series of finite KSF portfolios can be interestingly constructed with their Sharpe ratios approaching to the maximum Sharpe ratio, which however cannot be reached by any corresponding finite RSF portfolio. Numerical comparisons of these rules by using a set of real data are further empirically demonstrated.

Citation: Yuanyao Ding, Zudi Lu. How's the performance of the optimized portfolios by safety-first rules: Theory with empirical comparisons. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2019076
References:
[1]

V. S. Bawa, Safety-first, stochastic dominance, and optimal portfolio choice, Journal of Financial and Quantitative Analysis, 13 (1978), 255-271.  doi: 10.2307/2330386.  Google Scholar

[2]

T. Bodnar and T. Zabolotskyy, How risky is the optimal portfolio which maximizes the sharpe ratio?, Asta Advances in Statistical Analysis, 101 (2017), 1-28.  doi: 10.1007/s10182-016-0270-3.  Google Scholar

[3]

Y. Ding and B. Zhang, Risky asset pricing based on safety first fund management, Quantitative Finance, 9 (2009a), 353-361.  doi: 10.1080/14697680802392488.  Google Scholar

[4]

Y. Ding and B. Zhang, Optimal portfolio of safety-first models, Journal of Statistical Planning and Inference, 139 (2009b), 2952-2962.  doi: 10.1016/j.jspi.2009.01.018.  Google Scholar

[5]

Y. Ding and Z. Lu, The optimal portfolios based on a modified safety-first rule with risk-free saving, Journal of Industrial and Management Optimization, 12 (2016), 83-102.  doi: 10.3934/jimo.2016.12.83.  Google Scholar

[6]

R. B. DurandH. JafarpourC. Kluppelberg and R. Maller, Maximize the Sharpe Ratio and Minimize a VaR, The Journal of Wealth Management, 13 (2010), 91-102.   Google Scholar

[7]

M. Engels, Portfolio Optimization: Beyond Markowitz, Master's thesis, Leiden University, Netherlands, 2004. Google Scholar

[8]

N. Gressis and W. A. Remaley, Comment: ``Safety first – an expected utility principle", Jounal of Financial and Quantitative Analysis, 9 (1974), 1057-1061.   Google Scholar

[9]

H. Hagigi and B. Kluger, Assessing return and risk of pension funds–portfolios by the Telser safety-first approach, Journal of Business Finance and Accounting, 14 (1987), 241-253.   Google Scholar

[10]

M. R. Haley and M. K. McGee, Tilting safety first and the Sharpe portfolio, Finance Research Letters, 3 (2006), 173-180.   Google Scholar

[11]

S. Kataoka, A stochastic programming model, Econometrica, 31 (1963), 181-196.  doi: 10.2307/1910956.  Google Scholar

[12]

H. Levy and M. Sarnat, Safety first–an expected utility principle, Journal of Financial and Quantitative Analysis, 7 (1972), 1829-1834.   Google Scholar

[13]

Z. F. Li and G. J. Chen, Some discussions on Telser's safety-first model for portfolio selection (in Chinese), Theory and Practice of System Engineering, 36 (2005), 8-14.   Google Scholar

[14]

Z. F. LiJ. Yao and D. Li, Behavior patterns of investment strategies under Roy's safety-first principle, The Quarterly Review of Economics and Finance, 50 (2010), 167-179.  doi: 10.1016/j.qref.2009.11.004.  Google Scholar

[15]

R. A. Maller and D. A. Turkington, New light on the portfolio allocation problem, Mathematical Methods of Operational Research, 56 (2002), 501-511.  doi: 10.1007/s001860200211.  Google Scholar

[16]

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

[17]

V. I. Norkin and S. V. Boyko, Safety-first portfolio selection, Cybernetics and Systems Analysis, 48 (2012), 180-191.  doi: 10.1007/s10559-012-9396-9.  Google Scholar

[18]

Y. Okhrin and W. Schmid, Distributional properties of portfolio weights, Journal of Econometrics, 134 (2006), 235-256.  doi: 10.1016/j.jeconom.2005.06.022.  Google Scholar

[19]

L. S. Ortobelli and S. T. Rachev, Safety-first analysis and stable paretian approach to portfolio choice theory, Mathematical and Computer Modelling, 34 (2001), 1037-1072.  doi: 10.1016/S0895-7177(01)00116-9.  Google Scholar

[20]

D. H. Pyle and S. J. Turnovsky, Safety-first and expected utility maximization in mean-standard deviation portfolio analysis, The Review of Economics and Statistics, 52 (1970), 75-81.   Google Scholar

[21]

A. D. Roy, Safety-first and the holding of assets, Econometrica, 20 (1952), 431-449.  doi: 10.2307/1907413.  Google Scholar

[22]

W. F. Sharpe, The Sharpe Ratio, Journal of Portfolio Management, 21 (1994), 49-58.  doi: 10.3905/jpm.1994.409501.  Google Scholar

[23]

L. G. Telser, Safety first and hedging, Review of Economic Studies, 23 (1955), 1-16.  doi: 10.2307/2296146.  Google Scholar

[24]

S. Wang and Y. Xia, Portfolio Selection and Asset Pricing, Springer-Verlag, Berlin Heidelberg New York, Printed in Germany, 2002. doi: 10.1007/978-3-642-55934-1.  Google Scholar

show all references

References:
[1]

V. S. Bawa, Safety-first, stochastic dominance, and optimal portfolio choice, Journal of Financial and Quantitative Analysis, 13 (1978), 255-271.  doi: 10.2307/2330386.  Google Scholar

[2]

T. Bodnar and T. Zabolotskyy, How risky is the optimal portfolio which maximizes the sharpe ratio?, Asta Advances in Statistical Analysis, 101 (2017), 1-28.  doi: 10.1007/s10182-016-0270-3.  Google Scholar

[3]

Y. Ding and B. Zhang, Risky asset pricing based on safety first fund management, Quantitative Finance, 9 (2009a), 353-361.  doi: 10.1080/14697680802392488.  Google Scholar

[4]

Y. Ding and B. Zhang, Optimal portfolio of safety-first models, Journal of Statistical Planning and Inference, 139 (2009b), 2952-2962.  doi: 10.1016/j.jspi.2009.01.018.  Google Scholar

[5]

Y. Ding and Z. Lu, The optimal portfolios based on a modified safety-first rule with risk-free saving, Journal of Industrial and Management Optimization, 12 (2016), 83-102.  doi: 10.3934/jimo.2016.12.83.  Google Scholar

[6]

R. B. DurandH. JafarpourC. Kluppelberg and R. Maller, Maximize the Sharpe Ratio and Minimize a VaR, The Journal of Wealth Management, 13 (2010), 91-102.   Google Scholar

[7]

M. Engels, Portfolio Optimization: Beyond Markowitz, Master's thesis, Leiden University, Netherlands, 2004. Google Scholar

[8]

N. Gressis and W. A. Remaley, Comment: ``Safety first – an expected utility principle", Jounal of Financial and Quantitative Analysis, 9 (1974), 1057-1061.   Google Scholar

[9]

H. Hagigi and B. Kluger, Assessing return and risk of pension funds–portfolios by the Telser safety-first approach, Journal of Business Finance and Accounting, 14 (1987), 241-253.   Google Scholar

[10]

M. R. Haley and M. K. McGee, Tilting safety first and the Sharpe portfolio, Finance Research Letters, 3 (2006), 173-180.   Google Scholar

[11]

S. Kataoka, A stochastic programming model, Econometrica, 31 (1963), 181-196.  doi: 10.2307/1910956.  Google Scholar

[12]

H. Levy and M. Sarnat, Safety first–an expected utility principle, Journal of Financial and Quantitative Analysis, 7 (1972), 1829-1834.   Google Scholar

[13]

Z. F. Li and G. J. Chen, Some discussions on Telser's safety-first model for portfolio selection (in Chinese), Theory and Practice of System Engineering, 36 (2005), 8-14.   Google Scholar

[14]

Z. F. LiJ. Yao and D. Li, Behavior patterns of investment strategies under Roy's safety-first principle, The Quarterly Review of Economics and Finance, 50 (2010), 167-179.  doi: 10.1016/j.qref.2009.11.004.  Google Scholar

[15]

R. A. Maller and D. A. Turkington, New light on the portfolio allocation problem, Mathematical Methods of Operational Research, 56 (2002), 501-511.  doi: 10.1007/s001860200211.  Google Scholar

[16]

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

[17]

V. I. Norkin and S. V. Boyko, Safety-first portfolio selection, Cybernetics and Systems Analysis, 48 (2012), 180-191.  doi: 10.1007/s10559-012-9396-9.  Google Scholar

[18]

Y. Okhrin and W. Schmid, Distributional properties of portfolio weights, Journal of Econometrics, 134 (2006), 235-256.  doi: 10.1016/j.jeconom.2005.06.022.  Google Scholar

[19]

L. S. Ortobelli and S. T. Rachev, Safety-first analysis and stable paretian approach to portfolio choice theory, Mathematical and Computer Modelling, 34 (2001), 1037-1072.  doi: 10.1016/S0895-7177(01)00116-9.  Google Scholar

[20]

D. H. Pyle and S. J. Turnovsky, Safety-first and expected utility maximization in mean-standard deviation portfolio analysis, The Review of Economics and Statistics, 52 (1970), 75-81.   Google Scholar

[21]

A. D. Roy, Safety-first and the holding of assets, Econometrica, 20 (1952), 431-449.  doi: 10.2307/1907413.  Google Scholar

[22]

W. F. Sharpe, The Sharpe Ratio, Journal of Portfolio Management, 21 (1994), 49-58.  doi: 10.3905/jpm.1994.409501.  Google Scholar

[23]

L. G. Telser, Safety first and hedging, Review of Economic Studies, 23 (1955), 1-16.  doi: 10.2307/2296146.  Google Scholar

[24]

S. Wang and Y. Xia, Portfolio Selection and Asset Pricing, Springer-Verlag, Berlin Heidelberg New York, Printed in Germany, 2002. doi: 10.1007/978-3-642-55934-1.  Google Scholar

Figure 1.  RSF portfolio, KSF portfolio and TSF portfolio
Figure 2.  Characteristics of Sharpe ratio of KSF portfolio vs. $ \alpha $ when $ R_b <\frac{B}{C} $
Figure 3.  Comparison of $ P(X'R\le R_b) $ among the SF Portfolios
Figure 4.  Comparison of $ P(X'R\le R_d) $ between KSF and RSF Portfolios
Figure 5.  Comparison of Sharpe ratios of RSF, KSF and TSF portfolios
Figure 6.  Extreme risks of KSF and TSF Portfolios with Equal Sharpe Ratios
Figure 7.  Characteristics of Sharpe ratio of KSF portfolio vs. $ \alpha $ when $ R_b >\frac{B}{C} $
Table 1.  Expected Returns and Covariance of Three Risky Assets
Covariance 1B0010 1B0011 1B0006 Returns
1B0010 0.00863 0.00657 0.00830 0.00497
1B0011 0.00657 0.00609 0.00648 0.01214
1B0006 0.00830 0.00648 0.01390 0.00613
Covariance 1B0010 1B0011 1B0006 Returns
1B0010 0.00863 0.00657 0.00830 0.00497
1B0011 0.00657 0.00609 0.00648 0.01214
1B0006 0.00830 0.00648 0.01390 0.00613
Table 2.  Some results of RSF, TSF and KSF portfolios
Panel Rule $ P(R'X\le R_d) $ $ P(R'X\le R_b) $ $ X_1 $ $ X_2 $ $ X_3 $ $ SR $
RSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
1 TSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
KSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
RSF 0.41019 0.40512 -2.51889 3.51014 0.00875 0.24011
2 TSF 0.41297 0.41000 -4.95741 5.94221 0.01520 0.22754
KSF 0.41000 0.40532 -2.83436 3.82477 0.00959 0.23960
RSF 0.41778 0.40512 -2.51889 3.51014 0.00875 0.24011
3 TSF 0.42074 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.41650 0.40657 -3.51776 4.50637 0.01139 0.23638
RSF 0.42255 0.40512 -2.51889 3.51014 0.00875 0.24011
4 TSF 0.42393 0.42000 -13.26792 14.23075 0.03718 0.20189
KSF 0.42000 0.40813 -4.17332 5.16010 0.01313 0.23235
RSF 0.11226 0.40512 -2.51889 3.51014 0.00875 0.24011
5 TSF N 0.05000 N N N N
KSF 0.05000 0.42963 -0.52073 1.51727 0.00347 0.17730
RSF 0.20922 0.40512 -2.51889 3.51014 0.00875 0.24011
6 TSF N 0.15000 N N N N
KSF 0.15000 0.42565 -0.64983 1.64602 0.00381 0.18745
RSF 0.40016 0.40512 -2.51889 3.51014 0.00875 0.24011
7 TSF N 0.35000 N N N N
KSF 0.35000 0.41119 -1.33611 2.33049 0.00562 0.22449
RSF 0.43132 0.40512 -2.51889 3.51014 0.00875 0.24011
8 TSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
KSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
RSF 0.32258 0.40512 -2.51889 3.51014 0.00875 0.24011
9 TSF 0.38805 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.30000 0.41650 -1.01839 2.01361 0.00478 0.21086
RSF 0.24886 0.40512 -2.51889 3.51014 0.00875 0.24011
10 TSF 0.40088 0.42330 -22.61759 23.55569 0.06190 0.19346
KSF 0.20000 0.42330 -0.73281 1.72878 0.00403 0.19346
RSF 0.16529 0.40512 -2.51889 3.51014 0.00875 0.24011
11 TSF 0.42240 0.42768 -160.68449 161.25747 0.42702 0.18229
KSF 0.10000 0.42768 -0.58263 1.57900 0.00363 0.18229
Panel Rule $ P(R'X\le R_d) $ $ P(R'X\le R_b) $ $ X_1 $ $ X_2 $ $ X_3 $ $ SR $
RSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
1 TSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
KSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
RSF 0.41019 0.40512 -2.51889 3.51014 0.00875 0.24011
2 TSF 0.41297 0.41000 -4.95741 5.94221 0.01520 0.22754
KSF 0.41000 0.40532 -2.83436 3.82477 0.00959 0.23960
RSF 0.41778 0.40512 -2.51889 3.51014 0.00875 0.24011
3 TSF 0.42074 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.41650 0.40657 -3.51776 4.50637 0.01139 0.23638
RSF 0.42255 0.40512 -2.51889 3.51014 0.00875 0.24011
4 TSF 0.42393 0.42000 -13.26792 14.23075 0.03718 0.20189
KSF 0.42000 0.40813 -4.17332 5.16010 0.01313 0.23235
RSF 0.11226 0.40512 -2.51889 3.51014 0.00875 0.24011
5 TSF N 0.05000 N N N N
KSF 0.05000 0.42963 -0.52073 1.51727 0.00347 0.17730
RSF 0.20922 0.40512 -2.51889 3.51014 0.00875 0.24011
6 TSF N 0.15000 N N N N
KSF 0.15000 0.42565 -0.64983 1.64602 0.00381 0.18745
RSF 0.40016 0.40512 -2.51889 3.51014 0.00875 0.24011
7 TSF N 0.35000 N N N N
KSF 0.35000 0.41119 -1.33611 2.33049 0.00562 0.22449
RSF 0.43132 0.40512 -2.51889 3.51014 0.00875 0.24011
8 TSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
KSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
RSF 0.32258 0.40512 -2.51889 3.51014 0.00875 0.24011
9 TSF 0.38805 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.30000 0.41650 -1.01839 2.01361 0.00478 0.21086
RSF 0.24886 0.40512 -2.51889 3.51014 0.00875 0.24011
10 TSF 0.40088 0.42330 -22.61759 23.55569 0.06190 0.19346
KSF 0.20000 0.42330 -0.73281 1.72878 0.00403 0.19346
RSF 0.16529 0.40512 -2.51889 3.51014 0.00875 0.24011
11 TSF 0.42240 0.42768 -160.68449 161.25747 0.42702 0.18229
KSF 0.10000 0.42768 -0.58263 1.57900 0.00363 0.18229
Table 3.  Expected Returns and Covariance of Five Risky Assets
Covariance 1B0012 1B0013 1B0010 1B0011 1B0006 Returns
1B0012 8.62E-05 1.07E-04 -4.22E-05 -5.38E-05 -5.25E-05 0.00246
1B0013 1.07E-04 1.72E-04 -1.62E-04 -1.61E-04 -5.79E-05 0.00351
1B0010 -4.22E-05 -1.62E-04 0.00863 0.00657 0.00830 0.00497
1B0011 -5.38E-05 -1.61E-04 0.00657 0.00609 0.00648 0.01214
1B0006 -5.25E-05 -5.79E-05 0.00830 0.00648 0.01390 0.00613
Covariance 1B0012 1B0013 1B0010 1B0011 1B0006 Returns
1B0012 8.62E-05 1.07E-04 -4.22E-05 -5.38E-05 -5.25E-05 0.00246
1B0013 1.07E-04 1.72E-04 -1.62E-04 -1.61E-04 -5.79E-05 0.00351
1B0010 -4.22E-05 -1.62E-04 0.00863 0.00657 0.00830 0.00497
1B0011 -5.38E-05 -1.61E-04 0.00657 0.00609 0.00648 0.01214
1B0006 -5.25E-05 -5.79E-05 0.00830 0.00648 0.01390 0.00613
Table 4.  Expected Returns and Covariance of Five Risky Assets
weight $ \alpha $
0.10000 0.20000 0.30000 0.35000 0.36000 0.37000 0.37984
$ X_1 $ 1.21251 1.07523 0.76327 0.20470 -0.11043 -0.81445 -93.92237
$ X_2 $ -0.24424 -0.11734 0.17103 0.68736 0.97865 1.62943 87.69587
$ X_3 $ -0.05544 -0.07397 -0.11610 -0.19151 -0.23406 -0.32912 -12.90054
$ X_4 $ 0.08093 0.11220 0.18327 0.31053 0.38232 0.54272 21.75489
$ X_5 $ 0.00624 0.00388 -0.00148 -0.01107 -0.01648 -0.02858 -1.62785
$ SR $ -0.21947 -0.16942 -0.06622 0.05969 0.10400 0.16515 0.30287
weight $ \alpha $
0.10000 0.20000 0.30000 0.35000 0.36000 0.37000 0.37984
$ X_1 $ 1.21251 1.07523 0.76327 0.20470 -0.11043 -0.81445 -93.92237
$ X_2 $ -0.24424 -0.11734 0.17103 0.68736 0.97865 1.62943 87.69587
$ X_3 $ -0.05544 -0.07397 -0.11610 -0.19151 -0.23406 -0.32912 -12.90054
$ X_4 $ 0.08093 0.11220 0.18327 0.31053 0.38232 0.54272 21.75489
$ X_5 $ 0.00624 0.00388 -0.00148 -0.01107 -0.01648 -0.02858 -1.62785
$ SR $ -0.21947 -0.16942 -0.06622 0.05969 0.10400 0.16515 0.30287
[1]

Yuanyao Ding, Zudi Lu. The optimal portfolios based on a modified safety-first rule with risk-free saving. Journal of Industrial & Management Optimization, 2016, 12 (1) : 83-102. doi: 10.3934/jimo.2016.12.83

[2]

Haixiang Yao, Zhongfei Li, Xun Li, Yan Zeng. Optimal Sharpe ratio in continuous-time markets with and without a risk-free asset. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1273-1290. doi: 10.3934/jimo.2016072

[3]

Shuang Li, Chuong Luong, Francisca Angkola, Yonghong Wu. Optimal asset portfolio with stochastic volatility under the mean-variance utility with state-dependent risk aversion. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1521-1533. doi: 10.3934/jimo.2016.12.1521

[4]

Yufei Sun, Grace Aw, Kok Lay Teo, Guanglu Zhou. Portfolio optimization using a new probabilistic risk measure. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1275-1283. doi: 10.3934/jimo.2015.11.1275

[5]

Li Xue, Hao Di. Uncertain portfolio selection with mental accounts and background risk. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1809-1830. doi: 10.3934/jimo.2018124

[6]

Liu Yang, Xiaojiao Tong, Yao Xiong, Feifei Shen. A smoothing SAA algorithm for a portfolio choice model based on second-order stochastic dominance measures. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-15. doi: 10.3934/jimo.2018198

[7]

Linyi Qian, Lyu Chen, Zhuo Jin, Rongming Wang. Optimal liability ratio and dividend payment strategies under catastrophic risk. Journal of Industrial & Management Optimization, 2018, 14 (4) : 1443-1461. doi: 10.3934/jimo.2018015

[8]

Yanqin Bai, Yudan Wei, Qian Li. An optimal trade-off model for portfolio selection with sensitivity of parameters. Journal of Industrial & Management Optimization, 2017, 13 (2) : 947-965. doi: 10.3934/jimo.2016055

[9]

Hui Meng, Fei Lung Yuen, Tak Kuen Siu, Hailiang Yang. Optimal portfolio in a continuous-time self-exciting threshold model. Journal of Industrial & Management Optimization, 2013, 9 (2) : 487-504. doi: 10.3934/jimo.2013.9.487

[10]

Yan Zeng, Zhongfei Li, Jingjun Liu. Optimal strategies of benchmark and mean-variance portfolio selection problems for insurers. Journal of Industrial & Management Optimization, 2010, 6 (3) : 483-496. doi: 10.3934/jimo.2010.6.483

[11]

Ping-Chen Lin. Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm. Journal of Industrial & Management Optimization, 2012, 8 (3) : 549-564. doi: 10.3934/jimo.2012.8.549

[12]

Linyi Qian, Wei Wang, Rongming Wang. Risk-minimizing portfolio selection for insurance payment processes under a Markov-modulated model. Journal of Industrial & Management Optimization, 2013, 9 (2) : 411-429. doi: 10.3934/jimo.2013.9.411

[13]

Han Yang, Jia Yue, Nan-jing Huang. Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-17. doi: 10.3934/jimo.2018177

[14]

Meng Xue, Yun Shi, Hailin Sun. Portfolio optimization with relaxation of stochastic second order dominance constraints via conditional value at risk. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-22. doi: 10.3934/jimo.2019071

[15]

Ioannis Baltas, Anastasios Xepapadeas, Athanasios N. Yannacopoulos. Robust portfolio decisions for financial institutions. Journal of Dynamics & Games, 2018, 5 (2) : 61-94. doi: 10.3934/jdg.2018006

[16]

Kevin Kuo, Phong Luu, Duy Nguyen, Eric Perkerson, Katherine Thompson, Qing Zhang. Pairs trading: An optimal selling rule. Mathematical Control & Related Fields, 2015, 5 (3) : 489-499. doi: 10.3934/mcrf.2015.5.489

[17]

Yue Qi, Zhihao Wang, Su Zhang. On analyzing and detecting multiple optima of portfolio optimization. Journal of Industrial & Management Optimization, 2018, 14 (1) : 309-323. doi: 10.3934/jimo.2017048

[18]

Ke Ruan, Masao Fukushima. Robust portfolio selection with a combined WCVaR and factor model. Journal of Industrial & Management Optimization, 2012, 8 (2) : 343-362. doi: 10.3934/jimo.2012.8.343

[19]

Tao Pang, Azmat Hussain. An infinite time horizon portfolio optimization model with delays. Mathematical Control & Related Fields, 2016, 6 (4) : 629-651. doi: 10.3934/mcrf.2016018

[20]

Hanqing Jin, Xun Yu Zhou. Continuous-time portfolio selection under ambiguity. Mathematical Control & Related Fields, 2015, 5 (3) : 475-488. doi: 10.3934/mcrf.2015.5.475

2018 Impact Factor: 1.025

Article outline

Figures and Tables

[Back to Top]