
-
Previous Article
Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle
- JIMO Home
- This Issue
-
Next Article
Independent sales or bundling? Decisions under different market-dominant powers
Worst-case analysis of Gini mean difference safety measure
Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India |
The paper introduces the worst-case portfolio optimization models within the robust optimization framework for maximizing return through either the mean or median metrics. The risk in the portfolio is quantified by Gini mean difference. We put forward the worst-case models under the mixed and interval+polyhedral uncertainty sets. The proposed models turn out to be linear and mixed integer linear programs under the mixed uncertainty set, and semidefinite program under interval+polyhedral uncertainty set. The performance comparison of the proposed models on the listed stocks of Euro Stoxx 50, Dow Jones Global Titans 50, S & P Asia 50, consistently exhibit advantage over their conventional non-robust counterpart models on various risk parameters including the standard deviation, worst return, value at risk, conditional value at risk and maximum drawdown of the portfolio.
References:
[1] |
P. Artzner, F. Delbaen, J.-M. Eber and D. Heath,
Coherent measures of risk, Math. Finance, 9 (1999), 203-228.
doi: 10.1111/1467-9965.00068. |
[2] |
A. Ben-Tal, D. Den Hertog and J.-P. Vial,
Deriving robust counterparts of nonlinear uncertain inequalities, Math. Program., 149 (2015), 265-299.
doi: 10.1007/s10107-014-0750-8. |
[3] |
S. Benati,
Using medians in portfolio optimization, J. Oper. Res. Soc., 66 (2015), 720-731.
doi: 10.1057/jors.2014.57. |
[4] |
M. Berkhouch, G. Lakhnati and M. B. Righi,
Extended gini-type measures of risk and variability, Appl. Math. Finance, 25 (2018), 295-314.
doi: 10.1080/1350486X.2018.1538806. |
[5] |
D. Bertsimas and M. Sim,
The price of robustness, Oper. Res., 52 (2004), 35-53.
doi: 10.1287/opre.1030.0065. |
[6] |
M. J. Best and R. R. Grauer,
On the sensitivity of mean-variance-efficient portfolios to changes in asset means: Some analytical and computational results, Rev. Financial Studies, 4 (1991), 315-342.
doi: 10.1093/rfs/4.2.315. |
[7] |
F. Black and R. Litterman,
Global portfolio optimization, Financial Analysts Journal, 48 (1992), 28-43.
doi: 10.2469/faj.v48.n5.28. |
[8] |
B. Bower and P. Wentz, Portfolio optimization: MAD vs. Markowitz, Rose-Hulman Undergraduate Mathematics Journal, 6 (2005), 3. Google Scholar |
[9] |
C. Chen and R. H. Kwon,
Robust portfolio selection for index tracking, Comput. Oper. Res., 39 (2012), 829-837.
doi: 10.1016/j.cor.2010.08.019. |
[10] |
W. Chen and S. Tan,
Robust portfolio selection based on asymmetric measures of variability of stock returns, J. Comput. Appl. Math., 232 (2009), 295-304.
doi: 10.1016/j.cam.2009.06.010. |
[11] |
L. El Ghaoui, M. Oks and F. Oustry,
Worst-case value-at-risk and robust portfolio optimization: A conic programming approach, Oper. Res., 51 (2003), 543-556.
doi: 10.1287/opre.51.4.543.16101. |
[12] |
M. Feng, A. Wächter and J. Staum,
Practical algorithms for value-at-risk portfolio optimization problems, Quantitative Finance Lett., 3 (2015), 1-9.
doi: 10.1080/21649502.2014.995214. |
[13] |
E. Furman, R. Wang and R. Zitikis,
Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks, J. Banking Finance, 83 (2017), 70-84.
doi: 10.2139/ssrn.2836281. |
[14] |
C. Gerstenberger and D. Vogel,
On the efficiency of Gini's mean difference, Stat. Methods Appl., 24 (2015), 569-596.
doi: 10.1007/s10260-015-0315-x. |
[15] |
M. Gharakhani, F. Zarea Fazlelahi and S. Sadjadi,
A robust optimization approach for index tracking problem, J. Computer Sci., 10 (2014), 2450-2463.
doi: 10.3844/jcssp.2014.2450.2463. |
[16] |
D. Goldfarb and G. Iyengar,
Robust portfolio selection problems, Math. Oper. Res., 28 (2003), 1-38.
doi: 10.1287/moor.28.1.1.14260. |
[17] |
J.-Y. Gotoh, K. Shinozaki and A. Takeda,
Robust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures, Quant. Finance, 13 (2013), 1621-1635.
doi: 10.1080/14697688.2012.738930. |
[18] |
J. A. Hall, B. W. Brorsen and S. H. Irwin,
The distribution of futures prices: A test of the stable paretian and mixture of normals hypotheses, J. Financial Quantitative Anal., 24 (1989), 105-116.
doi: 10.2307/2330751. |
[19] |
R. Ji, M. A. Lejeune and S. Y. Prasad,
Properties, formulations, and algorithms for portfolio optimization using mean-Gini criteria, Ann. Oper. Res., 248 (2017), 305-343.
doi: 10.1007/s10479-016-2230-4. |
[20] |
M. Kapsos, N. Christofides and B. Rustem,
Worst-case robust Omega ratio, European J. Oper. Res., 234 (2014), 499-507.
doi: 10.1016/j.ejor.2013.04.025. |
[21] |
G. Kara, A. Özmen and G.-W. Weber,
Stability advances in robust portfolio optimization under parallelepiped uncertainty, CEJOR Cent. Eur. J. Oper. Res., 27 (2019), 241-261.
doi: 10.1007/s10100-017-0508-5. |
[22] |
C. Keating and W. F. Shadwick, A universal performance measure, J. Performance Measurement, 6 (2002), 59-84. Google Scholar |
[23] |
H. Konno and H. Yamazaki,
Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market, Management Sci., 37 (1991), 519-531.
doi: 10.1287/mnsc.37.5.519. |
[24] |
R. H. Kwon and D. Wu,
Factor-based robust index tracking, Optim. Eng., 18 (2017), 443-466.
doi: 10.1007/s11081-016-9314-5. |
[25] |
P. Li, Y. Han and Y. Xia,
Portfolio optimization using asymmetry robust mean absolute deviation model, Finance Res. Lett., 18 (2016), 353-362.
doi: 10.1016/j.frl.2016.05.014. |
[26] |
B. G. Lindsay, Mixture models: Theory, geometry and applications, in NSF-CBMS Regional Conference Series in Probability and Statistics, (1995), 1–163. Google Scholar |
[27] |
S.-T. Liu,
The mean-absolute deviation portfolio selection problem with interval-valued returns, J. Comput. Appl. Math., 235 (2011), 4149-4157.
doi: 10.1016/j.cam.2011.03.008. |
[28] |
R. Mansini, W. Ogryczak and M. G. Speranza,
Conditional value at risk and related linear programming models for portfolio optimization, Ann. Oper. Res., 152 (2007), 227-256.
doi: 10.1007/s10479-006-0142-4. |
[29] |
R. Mansini, W. Ogryczak and M. G. Speranza, Tail Gini's risk measures and related linear programming models for portfolio optimization, in HERCMA Conference Proceedings, CD, LEA Publishers, Athens, 2007. Google Scholar |
[30] |
H. Markowitz,
Portfolio selection, J. Finance, 7 (1952), 77-91.
doi: 10.1111/j.1540-6261.1952.tb01525.x. |
[31] |
Y. Moon and T. Yao,
A robust mean absolute deviation model for portfolio optimization, Comput. Oper. Res., 38 (2011), 1251-1258.
doi: 10.1016/j.cor.2010.10.020. |
[32] |
K. Natarajan, D. Pachamanova and M. Sim,
Constructing risk measures from uncertainty sets, Oper. Res., 57 (2009), 1129-1141.
doi: 10.1287/opre.1080.0683. |
[33] |
W. Ogryczak, Risk measurement: Mean absolute deviation versus Gini's mean difference, in Decision Theory and Optimization in Theory and Practice–Proc. 9th Workshop GOR WG Chemnitz, 1999, 33–51. Google Scholar |
[34] |
D. Peel and G. J. McLachlan, Robust mixture modelling using the t distribution, Statistics Comput., 10 (2000), 339-348. Google Scholar |
[35] |
K. Postek, D. den Hertog and B. Melenberg,
Computationally tractable counterparts of distributionally robust constraints on risk measures, SIAM Rev., 58 (2016), 603-650.
doi: 10.1137/151005221. |
[36] |
R. T. Rockafellar and S. Uryasev,
Optimization of conditional value-at-risk, J. Risk, 2 (2000), 21-42.
doi: 10.1007/978-1-4757-6594-6_17. |
[37] |
M. Rudolf, H.-J. Wolter and H. Zimmermann,
A linear model for tracking error minimization, J. Banking Finance, 23 (1999), 85-103.
doi: 10.1016/S0378-4266(98)00076-4. |
[38] |
R. Sehgal and A. Mehra, Robust reward–risk ratio portfolio optimization, Internat. Transactions Oper. Res., (2019).
doi: 10.1111/itor.12652. |
[39] |
R. N. Sengupta and R. Kumar,
Robust and reliable portfolio optimization formulation of a chance constrained problem, Foundations Comput. Decision Sci., 42 (2017), 83-117.
doi: 10.1515/fcds-2017-0004. |
[40] |
H. Shalit and S. Yitzhaki,
Mean-Gini, portfolio theory, and the pricing of risky assets, J. Finance, 39 (1984), 1449-1468.
doi: 10.1111/j.1540-6261.1984.tb04917.x. |
[41] |
H. Shalit and S. Yitzhaki,
The mean-Gini efficient portfolio frontier, J. Financial Res., 28 (2005), 59-75.
doi: 10.1111/j.1475-6803.2005.00114.x. |
[42] |
A. Sharma, S. Agrawal and A. Mehra,
Enhanced indexing for risk averse investors using relaxed second order stochastic dominance, Optim. Eng., 18 (2017), 407-442.
doi: 10.1007/s11081-016-9329-y. |
[43] |
A. Sharma, S. Utz and A. Mehra,
Omega-CVaR portfolio optimization and its worst case analysis, OR Spectrum, 39 (2017), 505-539.
doi: 10.1007/s00291-016-0462-y. |
[44] |
W. F. Sharpe, Mean-absolute-deviation characteristic lines for securities and portfolios, Management Sci., 18 (1971), B–1.
doi: 10.1287/mnsc.18.2.B1. |
[45] |
S. Yitzhaki, Stochastic dominance, mean variance, and Gini's mean difference, American Economic Review, 72 (1982), 178-185. Google Scholar |
[46] |
M. R. Young,
A minimax portfolio selection rule with linear programming solution, Management Sci., 44 (1998), 673-683.
doi: 10.1287/mnsc.44.5.673. |
[47] |
X. Zheng, X. Sun, D. Li and Y. Xu,
On zero duality gap in nonconvex quadratic programming problems, J. Global Optim., 52 (2012), 229-242.
doi: 10.1007/s10898-011-9660-y. |
[48] |
S. Zhu and M. Fukushima,
Worst-case conditional value-at-risk with application to robust portfolio management, Oper. Res., 57 (2009), 1155-1168.
doi: 10.1287/opre.1080.0684. |
[49] |
S. Zhu, D. Li and S. Wang,
Robust portfolio selection under downside risk measures, Quant. Finance, 9 (2009), 869-885.
doi: 10.1080/14697680902852746. |
show all references
References:
[1] |
P. Artzner, F. Delbaen, J.-M. Eber and D. Heath,
Coherent measures of risk, Math. Finance, 9 (1999), 203-228.
doi: 10.1111/1467-9965.00068. |
[2] |
A. Ben-Tal, D. Den Hertog and J.-P. Vial,
Deriving robust counterparts of nonlinear uncertain inequalities, Math. Program., 149 (2015), 265-299.
doi: 10.1007/s10107-014-0750-8. |
[3] |
S. Benati,
Using medians in portfolio optimization, J. Oper. Res. Soc., 66 (2015), 720-731.
doi: 10.1057/jors.2014.57. |
[4] |
M. Berkhouch, G. Lakhnati and M. B. Righi,
Extended gini-type measures of risk and variability, Appl. Math. Finance, 25 (2018), 295-314.
doi: 10.1080/1350486X.2018.1538806. |
[5] |
D. Bertsimas and M. Sim,
The price of robustness, Oper. Res., 52 (2004), 35-53.
doi: 10.1287/opre.1030.0065. |
[6] |
M. J. Best and R. R. Grauer,
On the sensitivity of mean-variance-efficient portfolios to changes in asset means: Some analytical and computational results, Rev. Financial Studies, 4 (1991), 315-342.
doi: 10.1093/rfs/4.2.315. |
[7] |
F. Black and R. Litterman,
Global portfolio optimization, Financial Analysts Journal, 48 (1992), 28-43.
doi: 10.2469/faj.v48.n5.28. |
[8] |
B. Bower and P. Wentz, Portfolio optimization: MAD vs. Markowitz, Rose-Hulman Undergraduate Mathematics Journal, 6 (2005), 3. Google Scholar |
[9] |
C. Chen and R. H. Kwon,
Robust portfolio selection for index tracking, Comput. Oper. Res., 39 (2012), 829-837.
doi: 10.1016/j.cor.2010.08.019. |
[10] |
W. Chen and S. Tan,
Robust portfolio selection based on asymmetric measures of variability of stock returns, J. Comput. Appl. Math., 232 (2009), 295-304.
doi: 10.1016/j.cam.2009.06.010. |
[11] |
L. El Ghaoui, M. Oks and F. Oustry,
Worst-case value-at-risk and robust portfolio optimization: A conic programming approach, Oper. Res., 51 (2003), 543-556.
doi: 10.1287/opre.51.4.543.16101. |
[12] |
M. Feng, A. Wächter and J. Staum,
Practical algorithms for value-at-risk portfolio optimization problems, Quantitative Finance Lett., 3 (2015), 1-9.
doi: 10.1080/21649502.2014.995214. |
[13] |
E. Furman, R. Wang and R. Zitikis,
Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks, J. Banking Finance, 83 (2017), 70-84.
doi: 10.2139/ssrn.2836281. |
[14] |
C. Gerstenberger and D. Vogel,
On the efficiency of Gini's mean difference, Stat. Methods Appl., 24 (2015), 569-596.
doi: 10.1007/s10260-015-0315-x. |
[15] |
M. Gharakhani, F. Zarea Fazlelahi and S. Sadjadi,
A robust optimization approach for index tracking problem, J. Computer Sci., 10 (2014), 2450-2463.
doi: 10.3844/jcssp.2014.2450.2463. |
[16] |
D. Goldfarb and G. Iyengar,
Robust portfolio selection problems, Math. Oper. Res., 28 (2003), 1-38.
doi: 10.1287/moor.28.1.1.14260. |
[17] |
J.-Y. Gotoh, K. Shinozaki and A. Takeda,
Robust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures, Quant. Finance, 13 (2013), 1621-1635.
doi: 10.1080/14697688.2012.738930. |
[18] |
J. A. Hall, B. W. Brorsen and S. H. Irwin,
The distribution of futures prices: A test of the stable paretian and mixture of normals hypotheses, J. Financial Quantitative Anal., 24 (1989), 105-116.
doi: 10.2307/2330751. |
[19] |
R. Ji, M. A. Lejeune and S. Y. Prasad,
Properties, formulations, and algorithms for portfolio optimization using mean-Gini criteria, Ann. Oper. Res., 248 (2017), 305-343.
doi: 10.1007/s10479-016-2230-4. |
[20] |
M. Kapsos, N. Christofides and B. Rustem,
Worst-case robust Omega ratio, European J. Oper. Res., 234 (2014), 499-507.
doi: 10.1016/j.ejor.2013.04.025. |
[21] |
G. Kara, A. Özmen and G.-W. Weber,
Stability advances in robust portfolio optimization under parallelepiped uncertainty, CEJOR Cent. Eur. J. Oper. Res., 27 (2019), 241-261.
doi: 10.1007/s10100-017-0508-5. |
[22] |
C. Keating and W. F. Shadwick, A universal performance measure, J. Performance Measurement, 6 (2002), 59-84. Google Scholar |
[23] |
H. Konno and H. Yamazaki,
Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market, Management Sci., 37 (1991), 519-531.
doi: 10.1287/mnsc.37.5.519. |
[24] |
R. H. Kwon and D. Wu,
Factor-based robust index tracking, Optim. Eng., 18 (2017), 443-466.
doi: 10.1007/s11081-016-9314-5. |
[25] |
P. Li, Y. Han and Y. Xia,
Portfolio optimization using asymmetry robust mean absolute deviation model, Finance Res. Lett., 18 (2016), 353-362.
doi: 10.1016/j.frl.2016.05.014. |
[26] |
B. G. Lindsay, Mixture models: Theory, geometry and applications, in NSF-CBMS Regional Conference Series in Probability and Statistics, (1995), 1–163. Google Scholar |
[27] |
S.-T. Liu,
The mean-absolute deviation portfolio selection problem with interval-valued returns, J. Comput. Appl. Math., 235 (2011), 4149-4157.
doi: 10.1016/j.cam.2011.03.008. |
[28] |
R. Mansini, W. Ogryczak and M. G. Speranza,
Conditional value at risk and related linear programming models for portfolio optimization, Ann. Oper. Res., 152 (2007), 227-256.
doi: 10.1007/s10479-006-0142-4. |
[29] |
R. Mansini, W. Ogryczak and M. G. Speranza, Tail Gini's risk measures and related linear programming models for portfolio optimization, in HERCMA Conference Proceedings, CD, LEA Publishers, Athens, 2007. Google Scholar |
[30] |
H. Markowitz,
Portfolio selection, J. Finance, 7 (1952), 77-91.
doi: 10.1111/j.1540-6261.1952.tb01525.x. |
[31] |
Y. Moon and T. Yao,
A robust mean absolute deviation model for portfolio optimization, Comput. Oper. Res., 38 (2011), 1251-1258.
doi: 10.1016/j.cor.2010.10.020. |
[32] |
K. Natarajan, D. Pachamanova and M. Sim,
Constructing risk measures from uncertainty sets, Oper. Res., 57 (2009), 1129-1141.
doi: 10.1287/opre.1080.0683. |
[33] |
W. Ogryczak, Risk measurement: Mean absolute deviation versus Gini's mean difference, in Decision Theory and Optimization in Theory and Practice–Proc. 9th Workshop GOR WG Chemnitz, 1999, 33–51. Google Scholar |
[34] |
D. Peel and G. J. McLachlan, Robust mixture modelling using the t distribution, Statistics Comput., 10 (2000), 339-348. Google Scholar |
[35] |
K. Postek, D. den Hertog and B. Melenberg,
Computationally tractable counterparts of distributionally robust constraints on risk measures, SIAM Rev., 58 (2016), 603-650.
doi: 10.1137/151005221. |
[36] |
R. T. Rockafellar and S. Uryasev,
Optimization of conditional value-at-risk, J. Risk, 2 (2000), 21-42.
doi: 10.1007/978-1-4757-6594-6_17. |
[37] |
M. Rudolf, H.-J. Wolter and H. Zimmermann,
A linear model for tracking error minimization, J. Banking Finance, 23 (1999), 85-103.
doi: 10.1016/S0378-4266(98)00076-4. |
[38] |
R. Sehgal and A. Mehra, Robust reward–risk ratio portfolio optimization, Internat. Transactions Oper. Res., (2019).
doi: 10.1111/itor.12652. |
[39] |
R. N. Sengupta and R. Kumar,
Robust and reliable portfolio optimization formulation of a chance constrained problem, Foundations Comput. Decision Sci., 42 (2017), 83-117.
doi: 10.1515/fcds-2017-0004. |
[40] |
H. Shalit and S. Yitzhaki,
Mean-Gini, portfolio theory, and the pricing of risky assets, J. Finance, 39 (1984), 1449-1468.
doi: 10.1111/j.1540-6261.1984.tb04917.x. |
[41] |
H. Shalit and S. Yitzhaki,
The mean-Gini efficient portfolio frontier, J. Financial Res., 28 (2005), 59-75.
doi: 10.1111/j.1475-6803.2005.00114.x. |
[42] |
A. Sharma, S. Agrawal and A. Mehra,
Enhanced indexing for risk averse investors using relaxed second order stochastic dominance, Optim. Eng., 18 (2017), 407-442.
doi: 10.1007/s11081-016-9329-y. |
[43] |
A. Sharma, S. Utz and A. Mehra,
Omega-CVaR portfolio optimization and its worst case analysis, OR Spectrum, 39 (2017), 505-539.
doi: 10.1007/s00291-016-0462-y. |
[44] |
W. F. Sharpe, Mean-absolute-deviation characteristic lines for securities and portfolios, Management Sci., 18 (1971), B–1.
doi: 10.1287/mnsc.18.2.B1. |
[45] |
S. Yitzhaki, Stochastic dominance, mean variance, and Gini's mean difference, American Economic Review, 72 (1982), 178-185. Google Scholar |
[46] |
M. R. Young,
A minimax portfolio selection rule with linear programming solution, Management Sci., 44 (1998), 673-683.
doi: 10.1287/mnsc.44.5.673. |
[47] |
X. Zheng, X. Sun, D. Li and Y. Xu,
On zero duality gap in nonconvex quadratic programming problems, J. Global Optim., 52 (2012), 229-242.
doi: 10.1007/s10898-011-9660-y. |
[48] |
S. Zhu and M. Fukushima,
Worst-case conditional value-at-risk with application to robust portfolio management, Oper. Res., 57 (2009), 1155-1168.
doi: 10.1287/opre.1080.0684. |
[49] |
S. Zhu, D. Li and S. Wang,
Robust portfolio selection under downside risk measures, Quant. Finance, 9 (2009), 869-885.
doi: 10.1080/14697680902852746. |





period | weekly average |
weakly sd |
1-9 weeks | 3.606 | 19.559 |
10-18 weeks | 7.111 | 42.395 |
19-27 weeks | -2.078 | 23.281 |
period | weekly average |
weakly sd |
1-9 weeks | 3.606 | 19.559 |
10-18 weeks | 7.111 | 42.395 |
19-27 weeks | -2.078 | 23.281 |
average | 0.458 | 1.541 | -0.143 | 0.127 | |
sd | 21.973 | 22.432 | 21.183 | 22.164 | |
median | -0.404 | 3.035 | 1.032 | -0.717 | |
min | -59.257 | -67.184 | -57.742 | -51.915 | |
(DI) | max | 55.669 | 63.717 | 57.2492 | 65.108 |
neg returns | 80 | 72 | 77 | 82 | |
53.369 | 55.862 | 50.934 | 49.827 | ||
50.236 | 48.982 | 46.816 | 47.84 | ||
49.945 | 44.695 | 43.091 | 46.16 | ||
40.909 | 35.241 | 38.74 | 43.921 | ||
MD | 213.407 | 156.96 | 173.27 | 224.3 | |
average | 5.348 | 5.315 | 3.652 | 3.934 | |
sd | 24.384 | 23.277 | 19.905 | 21.320 | |
median | 7.812 | 5.212 | 5.081 | 5.634 | |
min | -69.647 | -82.617 | -71.400 | -52.323 | |
(DII) | max | 145.054 | 104.310 | 63.899 | 81.426 |
neg returns | 56 | 57 | 55 | 61 | |
57.914 | 53.802 | 57.633 | 47.689 | ||
49.836 | 47.188 | 48.391 | 43.867 | ||
43.523 | 38.888 | 37.368 | 41.209 | ||
33.153 | 34.584 | 31.053 | 37.39 | ||
MD | 118.11 | 146.64 | 111.057 | 149.49 | |
average | 4.188 | 5.561 | 3.968 | 2.365 | |
sd | 23.907 | 30.314 | 19.597 | 24.278 | |
median | 2.817 | 3.380 | 4.128 | 2.592 | |
min | -93.670 | -108.797 | -82.422 | -88.448 | |
(DIII) | max | 87.815 | 79.020 | 62.667 | 74.162 |
neg returns | 68 | 72 | 62 | 70 | |
61.937 | 73.741 | 52.024 | 63.487 | ||
51.816 | 61.994 | 40.559 | 52.514 | ||
46.458 | 47.761 | 28.247 | 51.358 | ||
28.933 | 41.034 | 22.291 | 32.79 | ||
MD | 157.024 | 106.34 | 190.67 | 188.84 | |
average | 1.926 | 1.852 | 1.120 | 0.232 | |
sd | 23.337 | 25.528 | 21.178 | 23.817 | |
median | 3.853 | 2.276 | 1.655 | 1.307 | |
min | -65.330 | -80.298 | -67.686 | -68.472 | |
(DIV) | max | 54.355 | 72.800 | 50.498 | 60.039 |
neg returns | 71 | 70 | 73 | 76 | |
57.727 | 61.945 | 58.193 | 58.013 | ||
50.202 | 56.738 | 50.081 | 52.9 | ||
52.377 | 52.549 | 48.15 | 48.715 | ||
36.036 | 48.283 | 31.815 | 43.657 | ||
MD | 180.98 | 233.87 | 170.43 | 191.327 |
average | 0.458 | 1.541 | -0.143 | 0.127 | |
sd | 21.973 | 22.432 | 21.183 | 22.164 | |
median | -0.404 | 3.035 | 1.032 | -0.717 | |
min | -59.257 | -67.184 | -57.742 | -51.915 | |
(DI) | max | 55.669 | 63.717 | 57.2492 | 65.108 |
neg returns | 80 | 72 | 77 | 82 | |
53.369 | 55.862 | 50.934 | 49.827 | ||
50.236 | 48.982 | 46.816 | 47.84 | ||
49.945 | 44.695 | 43.091 | 46.16 | ||
40.909 | 35.241 | 38.74 | 43.921 | ||
MD | 213.407 | 156.96 | 173.27 | 224.3 | |
average | 5.348 | 5.315 | 3.652 | 3.934 | |
sd | 24.384 | 23.277 | 19.905 | 21.320 | |
median | 7.812 | 5.212 | 5.081 | 5.634 | |
min | -69.647 | -82.617 | -71.400 | -52.323 | |
(DII) | max | 145.054 | 104.310 | 63.899 | 81.426 |
neg returns | 56 | 57 | 55 | 61 | |
57.914 | 53.802 | 57.633 | 47.689 | ||
49.836 | 47.188 | 48.391 | 43.867 | ||
43.523 | 38.888 | 37.368 | 41.209 | ||
33.153 | 34.584 | 31.053 | 37.39 | ||
MD | 118.11 | 146.64 | 111.057 | 149.49 | |
average | 4.188 | 5.561 | 3.968 | 2.365 | |
sd | 23.907 | 30.314 | 19.597 | 24.278 | |
median | 2.817 | 3.380 | 4.128 | 2.592 | |
min | -93.670 | -108.797 | -82.422 | -88.448 | |
(DIII) | max | 87.815 | 79.020 | 62.667 | 74.162 |
neg returns | 68 | 72 | 62 | 70 | |
61.937 | 73.741 | 52.024 | 63.487 | ||
51.816 | 61.994 | 40.559 | 52.514 | ||
46.458 | 47.761 | 28.247 | 51.358 | ||
28.933 | 41.034 | 22.291 | 32.79 | ||
MD | 157.024 | 106.34 | 190.67 | 188.84 | |
average | 1.926 | 1.852 | 1.120 | 0.232 | |
sd | 23.337 | 25.528 | 21.178 | 23.817 | |
median | 3.853 | 2.276 | 1.655 | 1.307 | |
min | -65.330 | -80.298 | -67.686 | -68.472 | |
(DIV) | max | 54.355 | 72.800 | 50.498 | 60.039 |
neg returns | 71 | 70 | 73 | 76 | |
57.727 | 61.945 | 58.193 | 58.013 | ||
50.202 | 56.738 | 50.081 | 52.9 | ||
52.377 | 52.549 | 48.15 | 48.715 | ||
36.036 | 48.283 | 31.815 | 43.657 | ||
MD | 180.98 | 233.87 | 170.43 | 191.327 |
(DI) | |||||
average | -6.039 | -6.859 | 1.1184 | -4.035 | |
sd | 43.196 | 52.886 | 24.373 | 40.885 | |
median | -19.16 | -18.64 | -2.775 | -10.72 | |
UP-DOWN | min | -80.7 | -87.88 | -41.22 | -64.31 |
max | 112.33 | 139.21 | 67.997 | 109.74 | |
neg returns | 12 | 13 | 11 | 12 | |
MD | 151.538 | 151.107 | 52.69 | 95.87 | |
average | -3.582 | -1.58 | -3.62 | -2.333 | |
sd | 39.36 | 44.553 | 38.817 | 39.861 | |
median | -8.466 | -3.124 | -7.898 | -7.301 | |
DOWN-DOWN | min | -76.31 | -122.3 | -77.25 | -93.32 |
max | 116.87 | 73.054 | 113.99 | 80.892 | |
neg returns | 12 | 12 | 12 | 11 | |
MD | 209.357 | 241.872 | 206.61 | 240.81 | |
average | 4.5948 | 6.5533 | 6.3614 | 9.0281 | |
sd | 17.897 | 24.9 | 18.545 | 21.693 | |
median | 0.7725 | 4.0728 | 6.5631 | 11.843 | |
DOWN-UP | min | -26.7 | -32.28 | -17.9 | -29.67 |
max | 42.982 | 60.549 | 50.299 | 37.346 | |
neg returns | 9 | 7 | 9 | 7 | |
MD | 36.5 | 41.1155 | 31.584 | 29.673 | |
average | 4.5768 | 4.0462 | 5.1412 | 5.5771 | |
sd | 19.647 | 28.427 | 18.428 | 16.52 | |
median | 2.6147 | 5.4864 | 0.6005 | 6.9434 | |
UP-UP | min | -28.86 | -39.84 | -25.75 | -35.76 |
max | 36.136 | 50.381 | 37.729 | 29.958 | |
neg returns | 10 | 8 | 10 | 6 | |
MD | 48.181 | 87.913 | 35.078 | 40.38 | |
(DII) | |||||
average | -5.029 | -3.828 | -5.007 | 1.4072 | |
sd | 66.172 | 72.696 | 61.369 | 58.82 | |
median | -5.895 | -0.19 | 8.9474 | 3.7326 | |
UP-DOWN | min | -179.4 | -193.8 | -154.4 | -150.6 |
max | 129.46 | 146.11 | 126.09 | 140.28 | |
neg returns | 10 | 10 | 9 | 9 | |
MD | 235.221 | 236.219 | 222.13 | 150.582 | |
average | -1.545 | 0.006 | -2.109 | -2.249 | |
sd | 34.09 | 30.876 | 35.189 | 33.007 | |
median | -4.819 | -3.358 | -4.806 | -4.546 | |
DOWN-DOWN | min | -67.06 | -48.79 | -61.39 | -90.7 |
max | 115.75 | 72.059 | 122.57 | 76.009 | |
neg returns | 11 | 11 | 12 | 13 | |
MD | 162.256 | 113.9822 | 173.88 | 159.283 | |
average | -6.301 | -6.031 | -2.023 | -4.701 | |
sd | 17.096 | 24.228 | 15.888 | 19.831 | |
median | -4.875 | -9.129 | -3.986 | -6.138 | |
DOWN-UP | min | -37.04 | -47 | -33.08 | -39.68 |
max | 28.022 | 53.495 | 38.54 | 58.312 | |
neg returns | 15 | 12 | 13 | 14 | |
MD | 132.87 | 142.629 | 92.60 | 119.743 | |
average | 2.7559 | 2.8771 | 5.6723 | 7.6174 | |
sd | 21.022 | 28.97 | 12.812 | 18.045 | |
median | 5.1358 | 2.4316 | 4.687 | 4.2454 | |
UP-UP | min | -38.74 | -46.22 | -18.15 | -31.4 |
max | 51.007 | 65.855 | 36.726 | 48.432 | |
neg returns | 9 | 8 | 6 | 6 | |
MD | 47.811 | 109.512 | 18.517 | 36.649 |
(DI) | |||||
average | -6.039 | -6.859 | 1.1184 | -4.035 | |
sd | 43.196 | 52.886 | 24.373 | 40.885 | |
median | -19.16 | -18.64 | -2.775 | -10.72 | |
UP-DOWN | min | -80.7 | -87.88 | -41.22 | -64.31 |
max | 112.33 | 139.21 | 67.997 | 109.74 | |
neg returns | 12 | 13 | 11 | 12 | |
MD | 151.538 | 151.107 | 52.69 | 95.87 | |
average | -3.582 | -1.58 | -3.62 | -2.333 | |
sd | 39.36 | 44.553 | 38.817 | 39.861 | |
median | -8.466 | -3.124 | -7.898 | -7.301 | |
DOWN-DOWN | min | -76.31 | -122.3 | -77.25 | -93.32 |
max | 116.87 | 73.054 | 113.99 | 80.892 | |
neg returns | 12 | 12 | 12 | 11 | |
MD | 209.357 | 241.872 | 206.61 | 240.81 | |
average | 4.5948 | 6.5533 | 6.3614 | 9.0281 | |
sd | 17.897 | 24.9 | 18.545 | 21.693 | |
median | 0.7725 | 4.0728 | 6.5631 | 11.843 | |
DOWN-UP | min | -26.7 | -32.28 | -17.9 | -29.67 |
max | 42.982 | 60.549 | 50.299 | 37.346 | |
neg returns | 9 | 7 | 9 | 7 | |
MD | 36.5 | 41.1155 | 31.584 | 29.673 | |
average | 4.5768 | 4.0462 | 5.1412 | 5.5771 | |
sd | 19.647 | 28.427 | 18.428 | 16.52 | |
median | 2.6147 | 5.4864 | 0.6005 | 6.9434 | |
UP-UP | min | -28.86 | -39.84 | -25.75 | -35.76 |
max | 36.136 | 50.381 | 37.729 | 29.958 | |
neg returns | 10 | 8 | 10 | 6 | |
MD | 48.181 | 87.913 | 35.078 | 40.38 | |
(DII) | |||||
average | -5.029 | -3.828 | -5.007 | 1.4072 | |
sd | 66.172 | 72.696 | 61.369 | 58.82 | |
median | -5.895 | -0.19 | 8.9474 | 3.7326 | |
UP-DOWN | min | -179.4 | -193.8 | -154.4 | -150.6 |
max | 129.46 | 146.11 | 126.09 | 140.28 | |
neg returns | 10 | 10 | 9 | 9 | |
MD | 235.221 | 236.219 | 222.13 | 150.582 | |
average | -1.545 | 0.006 | -2.109 | -2.249 | |
sd | 34.09 | 30.876 | 35.189 | 33.007 | |
median | -4.819 | -3.358 | -4.806 | -4.546 | |
DOWN-DOWN | min | -67.06 | -48.79 | -61.39 | -90.7 |
max | 115.75 | 72.059 | 122.57 | 76.009 | |
neg returns | 11 | 11 | 12 | 13 | |
MD | 162.256 | 113.9822 | 173.88 | 159.283 | |
average | -6.301 | -6.031 | -2.023 | -4.701 | |
sd | 17.096 | 24.228 | 15.888 | 19.831 | |
median | -4.875 | -9.129 | -3.986 | -6.138 | |
DOWN-UP | min | -37.04 | -47 | -33.08 | -39.68 |
max | 28.022 | 53.495 | 38.54 | 58.312 | |
neg returns | 15 | 12 | 13 | 14 | |
MD | 132.87 | 142.629 | 92.60 | 119.743 | |
average | 2.7559 | 2.8771 | 5.6723 | 7.6174 | |
sd | 21.022 | 28.97 | 12.812 | 18.045 | |
median | 5.1358 | 2.4316 | 4.687 | 4.2454 | |
UP-UP | min | -38.74 | -46.22 | -18.15 | -31.4 |
max | 51.007 | 65.855 | 36.726 | 48.432 | |
neg returns | 9 | 8 | 6 | 6 | |
MD | 47.811 | 109.512 | 18.517 | 36.649 |
(DIII) | |||||
average | -0.2681 | -3.322 | 0.499 | -0.047 | |
sd | 19.1129 | 35.824 | 18.919 | 18.732 | |
median | 2.61737 | 1.5277 | 2.3188 | -0.6 | |
UP-DOWN | min | -44.737 | -55.41 | -40.08 | -45.67 |
max | 34.6569 | 77.502 | 35.718 | 32.997 | |
neg returns | 9 | 10 | 9 | 11 | |
MD | 74.493 | 159.476 | 68.053 | 64.932 | |
average | -3.1162 | 0.4164 | -4.551 | -4.109 | |
sd | 21.5749 | 28.782 | 19.827 | 21.41 | |
median | 2.12342 | -0.945 | -2.471 | -2.008 | |
DOWN-DOWN | min | -48.014 | -48.23 | -63.18 | -66.7 |
max | 30.412 | 66.157 | 25.402 | 25.383 | |
neg returns | 9 | 10 | 11 | 10 | |
MD | 119.543 | 106.662 | 117.954 | 122.734 | |
average | 11.2417 | 30.789 | 4.1896 | 8.3275 | |
sd | 20.3046 | 48.878 | 16.079 | 19.256 | |
median | 11.6296 | 39.14 | 7.2174 | 8.4051 | |
DOWN-UP | min | -27.77 | -71 | -33.04 | -40.3 |
max | 41.2838 | 98.964 | 25.703 | 46.593 | |
neg returns | 4 | 6 | 7 | 5 | |
MD | 27.77 | 78.851 | 36.124 | 48.21 | |
average | 1.05369 | -3.017 | 0.2775 | -0.183 | |
sd | 38.8387 | 42.249 | 34.946 | 31.665 | |
median | 2.73823 | 3.0081 | 1.0448 | 3.6441 | |
UP-UP | min | -77.25 | -79.58 | -69.68 | -59.33 |
max | 66.1491 | 66.947 | 56.4 | 40.851 | |
neg returns | 9 | 10 | 10 | 9 | |
MD | 131.452 | 166.753 | 127.845 | 124.788 | |
(DIV) | |||||
average | -23.233 | -28.74 | -24.08 | -23.28 | |
sd | 94.011 | 107.97 | 87.816 | 88.499 | |
median | -27.104 | -37.74 | -26.68 | -29.55 | |
UP-DOWN | min | -197.61 | -222.9 | -192.2 | -170 |
max | 224.871 | 271.88 | 225.58 | 216.6 | |
neg returns | 13 | 13 | 13 | 14 | |
MD | 517.924 | 576.729 | 507.51 | 489.529 | |
average | 0.58685 | 1.1156 | -0.835 | 0.5133 | |
sd | 28.4234 | 58.191 | 29.021 | 33.774 | |
median | -3.4995 | -1.348 | -2.763 | -7.861 | |
DOWN-DOWN | min | -36.563 | -79.36 | -46.89 | -47.11 |
max | 65.827 | 152.43 | 70.592 | 96.089 | |
neg returns | 11 | 10 | 10 | 11 | |
MD | 137.989 | 285.251 | 160.875 | 164.657 | |
average | 7.63253 | 3.1941 | 6.4851 | 10.284 | |
sd | 16.2213 | 15.549 | 15.238 | 19.419 | |
median | 8.10496 | 2.7367 | 9.7434 | 10.025 | |
DOWN-UP | min | -27.858 | -24.52 | -24.91 | -30.12 |
max | 47.6872 | 28.554 | 34.307 | 39.199 | |
neg returns | 5 | 7 | 7 | 7 | |
MD | 42.35 | 40.646 | 35.51 | 53.741 | |
average | 34.0544 | 30.368 | 17.863 | 15.659 | |
sd | 56.3158 | 63.198 | 47.088 | 67.943 | |
median | 51.5261 | 43.3 | 20.626 | 19.016 | |
UP-UP | min | -124.52 | -136.7 | -97.17 | -109.3 |
max | 100.258 | 152.26 | 111.24 | 174.18 | |
neg returns | 4 | 7 | 6 | 7 | |
MD | 124.517 | 144.539 | 97.174 | 139.654 |
(DIII) | |||||
average | -0.2681 | -3.322 | 0.499 | -0.047 | |
sd | 19.1129 | 35.824 | 18.919 | 18.732 | |
median | 2.61737 | 1.5277 | 2.3188 | -0.6 | |
UP-DOWN | min | -44.737 | -55.41 | -40.08 | -45.67 |
max | 34.6569 | 77.502 | 35.718 | 32.997 | |
neg returns | 9 | 10 | 9 | 11 | |
MD | 74.493 | 159.476 | 68.053 | 64.932 | |
average | -3.1162 | 0.4164 | -4.551 | -4.109 | |
sd | 21.5749 | 28.782 | 19.827 | 21.41 | |
median | 2.12342 | -0.945 | -2.471 | -2.008 | |
DOWN-DOWN | min | -48.014 | -48.23 | -63.18 | -66.7 |
max | 30.412 | 66.157 | 25.402 | 25.383 | |
neg returns | 9 | 10 | 11 | 10 | |
MD | 119.543 | 106.662 | 117.954 | 122.734 | |
average | 11.2417 | 30.789 | 4.1896 | 8.3275 | |
sd | 20.3046 | 48.878 | 16.079 | 19.256 | |
median | 11.6296 | 39.14 | 7.2174 | 8.4051 | |
DOWN-UP | min | -27.77 | -71 | -33.04 | -40.3 |
max | 41.2838 | 98.964 | 25.703 | 46.593 | |
neg returns | 4 | 6 | 7 | 5 | |
MD | 27.77 | 78.851 | 36.124 | 48.21 | |
average | 1.05369 | -3.017 | 0.2775 | -0.183 | |
sd | 38.8387 | 42.249 | 34.946 | 31.665 | |
median | 2.73823 | 3.0081 | 1.0448 | 3.6441 | |
UP-UP | min | -77.25 | -79.58 | -69.68 | -59.33 |
max | 66.1491 | 66.947 | 56.4 | 40.851 | |
neg returns | 9 | 10 | 10 | 9 | |
MD | 131.452 | 166.753 | 127.845 | 124.788 | |
(DIV) | |||||
average | -23.233 | -28.74 | -24.08 | -23.28 | |
sd | 94.011 | 107.97 | 87.816 | 88.499 | |
median | -27.104 | -37.74 | -26.68 | -29.55 | |
UP-DOWN | min | -197.61 | -222.9 | -192.2 | -170 |
max | 224.871 | 271.88 | 225.58 | 216.6 | |
neg returns | 13 | 13 | 13 | 14 | |
MD | 517.924 | 576.729 | 507.51 | 489.529 | |
average | 0.58685 | 1.1156 | -0.835 | 0.5133 | |
sd | 28.4234 | 58.191 | 29.021 | 33.774 | |
median | -3.4995 | -1.348 | -2.763 | -7.861 | |
DOWN-DOWN | min | -36.563 | -79.36 | -46.89 | -47.11 |
max | 65.827 | 152.43 | 70.592 | 96.089 | |
neg returns | 11 | 10 | 10 | 11 | |
MD | 137.989 | 285.251 | 160.875 | 164.657 | |
average | 7.63253 | 3.1941 | 6.4851 | 10.284 | |
sd | 16.2213 | 15.549 | 15.238 | 19.419 | |
median | 8.10496 | 2.7367 | 9.7434 | 10.025 | |
DOWN-UP | min | -27.858 | -24.52 | -24.91 | -30.12 |
max | 47.6872 | 28.554 | 34.307 | 39.199 | |
neg returns | 5 | 7 | 7 | 7 | |
MD | 42.35 | 40.646 | 35.51 | 53.741 | |
average | 34.0544 | 30.368 | 17.863 | 15.659 | |
sd | 56.3158 | 63.198 | 47.088 | 67.943 | |
median | 51.5261 | 43.3 | 20.626 | 19.016 | |
UP-UP | min | -124.52 | -136.7 | -97.17 | -109.3 |
max | 100.258 | 152.26 | 111.24 | 174.18 | |
neg returns | 4 | 7 | 6 | 7 | |
MD | 124.517 | 144.539 | 97.174 | 139.654 |
average | -9.9697 | -17.83 | -9.51 | -5.5184 | -8.024 | |
sd | 7.95041 | 12.595 | 5.629 | 19.863 | 12.937 | |
(DI) | med | -8.499 | -13.149 | -7.412 | -6.5741 | -6.643 |
min | -20.925 | -36.073 | -17.7 | -27.913 | -25.07 | |
max | -1.9555 | -8.9473 | -5.514 | 18.987 | 6.2582 | |
average | -1.505 | -0.2718 | -0.303 | -5.5184 | -1.547 | |
sd | 15.727 | 16.813 | 15.154 | 19.863 | 17.94 | |
(DII) | med | 4.52 | 0.3627 | 2.1474 | -6.5741 | 1.4017 |
min | -24.706 | -20.442 | -20.77 | -27.913 | -26.07 | |
max | 9.631 | 18.629 | 15.258 | 18.987 | 17.077 | |
average | 4.60903 | 4.288 | 0.1445 | 0.3184 | 5.2191 | |
sd | 23.0986 | 28.943 | 17.512 | 25.937 | 17.18 | |
(DIII) | med | 8.31506 | 9.593 | 3.2971 | 2.3598 | 9.9122 |
min | -24.436 | -33.33 | -21.39 | -33.356 | -18.75 | |
max | 26.2417 | 31.299 | 15.372 | 29.91 | 19.8 | |
average | -12.383 | -18.126 | -11.54 | -18.126 | -7.655 | |
sd | 14.3273 | 24.613 | 18.997 | 24.613 | 18.297 | |
(DIV) | med | -13.526 | -6.7408 | -9.971 | -6.7408 | -6.39 |
min | -28.661 | -54.996 | -35.76 | -54.996 | -30.83 | |
max | 6.17971 | -4.0262 | 9.5596 | -4.0262 | 12.992 |
average | -9.9697 | -17.83 | -9.51 | -5.5184 | -8.024 | |
sd | 7.95041 | 12.595 | 5.629 | 19.863 | 12.937 | |
(DI) | med | -8.499 | -13.149 | -7.412 | -6.5741 | -6.643 |
min | -20.925 | -36.073 | -17.7 | -27.913 | -25.07 | |
max | -1.9555 | -8.9473 | -5.514 | 18.987 | 6.2582 | |
average | -1.505 | -0.2718 | -0.303 | -5.5184 | -1.547 | |
sd | 15.727 | 16.813 | 15.154 | 19.863 | 17.94 | |
(DII) | med | 4.52 | 0.3627 | 2.1474 | -6.5741 | 1.4017 |
min | -24.706 | -20.442 | -20.77 | -27.913 | -26.07 | |
max | 9.631 | 18.629 | 15.258 | 18.987 | 17.077 | |
average | 4.60903 | 4.288 | 0.1445 | 0.3184 | 5.2191 | |
sd | 23.0986 | 28.943 | 17.512 | 25.937 | 17.18 | |
(DIII) | med | 8.31506 | 9.593 | 3.2971 | 2.3598 | 9.9122 |
min | -24.436 | -33.33 | -21.39 | -33.356 | -18.75 | |
max | 26.2417 | 31.299 | 15.372 | 29.91 | 19.8 | |
average | -12.383 | -18.126 | -11.54 | -18.126 | -7.655 | |
sd | 14.3273 | 24.613 | 18.997 | 24.613 | 18.297 | |
(DIV) | med | -13.526 | -6.7408 | -9.971 | -6.7408 | -6.39 |
min | -28.661 | -54.996 | -35.76 | -54.996 | -30.83 | |
max | 6.17971 | -4.0262 | 9.5596 | -4.0262 | 12.992 |
[1] |
Peng Zhang, Yongquan Zeng, Guotai Chi. Time-consistent multiperiod mean semivariance portfolio selection with the real constraints. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1663-1680. doi: 10.3934/jimo.2020039 |
[2] |
Andrea Tosin, Mattia Zanella. Uncertainty damping in kinetic traffic models by driver-assist controls. Mathematical Control & Related Fields, 2021 doi: 10.3934/mcrf.2021018 |
[3] |
Ardeshir Ahmadi, Hamed Davari-Ardakani. A multistage stochastic programming framework for cardinality constrained portfolio optimization. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 359-377. doi: 10.3934/naco.2017023 |
[4] |
Yue Qi, Xiaolin Li, Su Zhang. Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1531-1556. doi: 10.3934/jimo.2020033 |
[5] |
Reza Lotfi, Yahia Zare Mehrjerdi, Mir Saman Pishvaee, Ahmad Sadeghieh, Gerhard-Wilhelm Weber. A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 221-253. doi: 10.3934/naco.2020023 |
[6] |
Xianbang Chen, Yang Liu, Bin Li. Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1639-1661. doi: 10.3934/jimo.2020038 |
[7] |
Quan Hai, Shutang Liu. Mean-square delay-distribution-dependent exponential synchronization of chaotic neural networks with mixed random time-varying delays and restricted disturbances. Discrete & Continuous Dynamical Systems - B, 2021, 26 (6) : 3097-3118. doi: 10.3934/dcdsb.2020221 |
[8] |
Jinye Shen, Xian-Ming Gu. Two finite difference methods based on an H2N2 interpolation for two-dimensional time fractional mixed diffusion and diffusion-wave equations. Discrete & Continuous Dynamical Systems - B, 2021 doi: 10.3934/dcdsb.2021086 |
[9] |
Nizami A. Gasilov. Solving a system of linear differential equations with interval coefficients. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2739-2747. doi: 10.3934/dcdsb.2020203 |
[10] |
Khosro Sayevand, Valeyollah Moradi. A robust computational framework for analyzing fractional dynamical systems. Discrete & Continuous Dynamical Systems - S, 2021 doi: 10.3934/dcdss.2021022 |
[11] |
Hsin-Lun Li. Mixed Hegselmann-Krause dynamics. Discrete & Continuous Dynamical Systems - B, 2021 doi: 10.3934/dcdsb.2021084 |
[12] |
Elena K. Kostousova. External polyhedral estimates of reachable sets of discrete-time systems with integral bounds on additive terms. Mathematical Control & Related Fields, 2021 doi: 10.3934/mcrf.2021015 |
[13] |
Paul A. Glendinning, David J. W. Simpson. A constructive approach to robust chaos using invariant manifolds and expanding cones. Discrete & Continuous Dynamical Systems, 2021, 41 (7) : 3367-3387. doi: 10.3934/dcds.2020409 |
[14] |
Yuxin Tan, Yijing Sun. The Orlicz Minkowski problem involving $ 0 < p < 1 $: From one constant to an infinite interval. Discrete & Continuous Dynamical Systems - S, 2021 doi: 10.3934/dcdss.2021037 |
[15] |
Kai Kang, Taotao Lu, Jing Zhang. Financing strategy selection and coordination considering risk aversion in a capital-constrained supply chain. Journal of Industrial & Management Optimization, 2021 doi: 10.3934/jimo.2021042 |
[16] |
Xinfang Zhang, Jing Lu, Yan Peng. Decision framework for location and selection of container multimodal hubs: A case in china under the belt and road initiative. Journal of Industrial & Management Optimization, 2021 doi: 10.3934/jimo.2021061 |
[17] |
Nouressadat Touafek, Durhasan Turgut Tollu, Youssouf Akrour. On a general homogeneous three-dimensional system of difference equations. Electronic Research Archive, , () : -. doi: 10.3934/era.2021017 |
[18] |
Hongsong Feng, Shan Zhao. A multigrid based finite difference method for solving parabolic interface problem. Electronic Research Archive, , () : -. doi: 10.3934/era.2021031 |
[19] |
Amira Khelifa, Yacine Halim. Global behavior of P-dimensional difference equations system. Electronic Research Archive, , () : -. doi: 10.3934/era.2021029 |
[20] |
Demetres D. Kouvatsos, Jumma S. Alanazi, Kevin Smith. A unified ME algorithm for arbitrary open QNMs with mixed blocking mechanisms. Numerical Algebra, Control & Optimization, 2011, 1 (4) : 781-816. doi: 10.3934/naco.2011.1.781 |
2019 Impact Factor: 1.366
Tools
Metrics
Other articles
by authors
[Back to Top]