American Institute of Mathematical Sciences

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March  2020, 16(2): 759-775. doi: 10.3934/jimo.2018177

Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration

Han Yang 1, , Jia Yue 2, and  Nan-jing Huang 1,,

1. 

Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, China
2. 

Department of Economic Mathematics, Southwestern University of Finance and Economics, Chengdu, Sichuan, 610074, China

* Corresponding author: Nan-jing Huang

Received  August 2017 Revised  August 2018 Published  December 2018

Fund Project: This work was supported by the National Natural Science Foundation of China (11471230, 11671282, 11801462)

In this paper, we consider a multi-objective robust cross-market mixed portfolio optimization model under hierarchical risk integration in the international financial market consisting of finite sub-markets. It is difficult to describe the dependent structure accurately by the traditional copula theory because of the dependent structures of the risk assets in finite sub-markets are different usually. By employing the hierarchical risk integration method, we establish the multi-objective robust cross-market mixed portfolio model in which the worst-case value at risk is used as the risk measurement and the transaction costs, skewness and investment proportion limitation are all considered. We provide a new algorithm to calculate the worst-case value at risk of the cross-market mixed portfolio and give a numerical experiment to show the superiority of the model considered in this paper.

Keywords: Cross-market mixed portfolio, hierarchical risk integration, multi-objective robust optimization, worst-case value at risk.
Mathematics Subject Classification: 91G50, 91G60, 91B30, 62H20, 62E17, 62P99, 65C20.
Citation: Han Yang, Jia Yue, Nan-jing Huang. Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration. Journal of Industrial & Management Optimization, 2020, 16 (2) : 759-775. doi: 10.3934/jimo.2018177
References:
[1]

P. Arbenz, C. Hummel and G. Mainik, Copula based hierarchical risk aggregation through sample reordering, Insurance Mathematics and Economics, 51 (2012), 122-133.  doi: 10.1016/j.insmatheco.2012.03.009.  Google Scholar

[2]

E. D. Arditti and H. Levy, Portfolio efficient analysis in three monents-the multi-period case, Journal of Finance, 30 (1975), 797-809.   Google Scholar

[3]

P. Artzner, F. Delbaen, J. M. Eber and D. Heath, Coherent measures of risk, Mathematical Finance, 9 (1999), 203-228.  doi: 10.1111/1467-9965.00068.  Google Scholar

[4]

I. Bajeux-Besnainou, R. Belhaj, D. Maillard and R. Portait, Portfolio optimization under tracking error and weight constraints, Journal of Financial Research, 34 (2011), 295-330.   Google Scholar

[5]

A. Burcu, D. Fatma and H. Soyuer, Mean-variance-skewness-kurtosis approach to portfolio optimization: an application in Istanbul stock exchange, Ege Academic Review, 11 (2011), 9-17.   Google Scholar

[6]

Y. P. Chang, J. X. Lin and C. T. Yu, Calculating value-at-risk using the granularity adjustment method in the portfolio credit risk model with random loss given default, Journal of Economics and Management, 12 (2016), 157-176.   Google Scholar

[7]

P. Chunhachinda, K. Dandapani, S. Hamid and A. J. Prakash, Portfolio selection and skewness: Evidence from international stock markets, Journal of Banking and Finance, 21 (1997), 143-167.   Google Scholar

[8]

R. Diestel, Graph Theory, Springer-Verlag, New York, 2000. doi: 10.1007/b100033.  Google Scholar

[9]

K. W. Ding, M. H. Wang and N. J. Huang, Distributionally robust chance constrained problem under interval distribution information, Optimlization Letters, 12 (2018), 1315-1328.  doi: 10.1007/s11590-017-1160-7.  Google Scholar

[10]

E. Eberlein and U. Keller, Hyperbolic distributions in finance, Bernoulli, 1 (2000), 281-299.   Google Scholar

[11]

M. R. Fengler and O. Okhrin, Managing risk with a realized copula parameter, Computational Statistics and Data Analysis, 100 (2016), 131-152.  doi: 10.1016/j.csda.2014.07.011.  Google Scholar

[12]

L. Garlappi, R. Uppal and T. Wang, Portfolio selection with parameter and model uncertainty: A multi-prior approach, Review of Financial Studies, 20 (2007), 41-81.   Google Scholar

[13]

L. E. Ghaoui, M. Oks and F. Oustry, Worst-case value-at-risk and robust portfolio optimization: A conic programming approach, Operations Research, 51 (2003), 543-556.  doi: 10.1287/opre.51.4.543.16101.  Google Scholar

[14]

A. Ghorbel and A. Trabelsi, Energy portfolio risk management using time-varying extreme value copula methods, Economic Modelling, 38 (2014), 470-485.   Google Scholar

[15]

C. Giorgio, Tail estimation and mean-VaR portfolio sdeetion in market subject to financial instability, Journal of Banking and Finance, 26 (2002), 1355-1382.   Google Scholar

[16]

D. Goldfarb and G. Iyengar, Robust portfolio selection problems, Mathematics of Operations Research, 28 (2003), 1-38.  doi: 10.1287/moor.28.1.1.14260.  Google Scholar

[17]

Y. Huang and S. H. Fang, Mean-variance analysis of arbitrage portfolios under investment proportion limitations, Chinese Journal of Engineering Mathematics, 24 (2007), 662-668.   Google Scholar

[18]

P. Jana, T. K. Roy and S. K. Mazumder, Multi-objective possibilistic model for portfolio selection with transaction cost, Journal of Computational and Applied Mathematics, 228 (2009), 188-196.  doi: 10.1016/j.cam.2008.09.008.  Google Scholar

[19]

I. Kakouris and B. Rustem, Robust portfolio optimization with copulas, European Journal of Operational Research, 235 (2014), 28-37.  doi: 10.1016/j.ejor.2013.12.022.  Google Scholar

[20]

C. H. Kimberling, A probabilistic interpretation of complete monotonicity, Aequationes Mathematicae, 10 (1974), 152-164.  doi: 10.1007/BF01832852.  Google Scholar

[21]

H. Konno and K. I. Suzuki, A mean-variance-skewness portfolio optimization model, Journal of the Operations Research Society of Japan, 38 (1995), 173-187.   Google Scholar

[22]

K. K. Lai, L. Yu and S. Wang, Mean-variance-skewness-kurtosis-based portfolio optimization, International Multi-Symposiums on Computer and Computational Sciences, 2 (2006), 292-297.   Google Scholar

[23]

J. Li and J. Xu, Multi-objective portfolio selection model with fuzzy random returns and a compromise approach-based genetic algorithm, Information Sciences, 220 (2013), 507-521.  doi: 10.1016/j.ins.2012.07.005.  Google Scholar

[24]

X. Li, Z. Qin and S. Kar, Mean-variance-skewness model for portfolio selection with fuzzy returns, European Journal of Operational Research, 202 (2010), 239-247.   Google Scholar

[25]

X. H. Li and Y. P. You, A note on allocation of portfolio shares of random assets with Archimedean copula, Annals of Operations Research, 212 (2014), 155-167.  doi: 10.1007/s10479-012-1137-y.  Google Scholar

[26]

M. S. Lobo and S. Boyd, The worst-case risk of a portfolio, Journal of Economic Dynamics and Control, 26 (2000), 1159-1193.   Google Scholar

[27]

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

[28]

R. B. Nelsen, An Introduction to Copulas, 2$^{nd}$ edition, Springer, Berlin, 2006.  Google Scholar

[29]

M. H. A. Davis and A. R. Norman, Portfolio selection with transaction costs, Mathematics of Operations Research, 15 (1988), 676-713.  doi: 10.1287/moor.15.4.676.  Google Scholar

[30]

H. Sak, W. Hormann and J. Leydold, Efficient risk simulation for liner asset portfolios in the t-copula model, European Journal of Operational Research, 202 (2010), 802-809.   Google Scholar

[31]

A. Sklar, Fonctions de répartition à n dimensions et leurs marges, Publications de l'Institut de Statistique de l'Université de Paris, 8 (1959), 229-231.   Google Scholar

[32]

B. Schweizer and A. Sklar, Probabilistic Metric Spaces, North-Holland Publishing, Amsterdam, 1983.  Google Scholar

[33]

H. Soleimani, H. R. Golmakani and M. H. Salimi, Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm, Expert Systems with Applications, 36 (2009), 5058-5063.   Google Scholar

[34]

J. B. Su, Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market, Economic Modelling, 46 (2015), 204-224.   Google Scholar

[35]

G. Szego, Measures of risk, European Journal of Operational Research, 163 (2005), 5-19.  doi: 10.1016/j.ejor.2003.12.016.  Google Scholar

[36]

H. A. L. Thi and M. Moeini, Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm, Journal of Optimization Theory and Applications, 161 (2014), 199-224.  doi: 10.1007/s10957-012-0197-0.  Google Scholar

[37]

M. H. Wang, J. Yue and N. J. Huang, Robust mean variance portfolio selection model in the jump-diffusion financial market with an intractable claim, Optimization, 66 (2017), 1219-1234.  doi: 10.1080/02331934.2017.1310212.  Google Scholar

[38] D. V. Widder, The Laplace Transform, Princeton University Press, Princeton, 1941.   Google Scholar
[39]

M. Wu, N. J. Huang and B. S. Lee, Portfolio selection with transaction costs and different borrowing and lending interest rates in varying capital structure, Nonlinear Analysis Forum, 9 (2004), 175-200.   Google Scholar

[40]

H. Yang, W. Zhou and K. W. Ding, Markowitz investment portfolio model with transaction costs and capital budget in varying capital structure, Nonlinear Analysis Forum, 19 (2014), 119-127.   Google Scholar

[41]

H. X. Yao and Z. F. Li, Portfolio model and its explicit expressions of portfolio efficient frontier with minimum investment proportion constraint, OR Transactions, 13 (2009), 119-128.   Google Scholar

[42]

X. L. Zhan and S. Y. Zhang, Risk analysis of financial portfolio based on copula-SV model, Journal of Systems and Management, 16 (2007), 302-306.   Google Scholar

[43]

W. G. Zhang, Y. J. Liu and W. J. Xu, A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs, European Journal of Operational Research, 222 (2012), 341-349.  doi: 10.1016/j.ejor.2012.04.023.  Google Scholar

[44]

Q. Zhou, Z. Chen and R. Ming, Copula-based grouped risk aggregation under mixed operation, Applications of Mathematics, 61 (2016), 103-120.  doi: 10.1007/s10492-016-0124-z.  Google Scholar

[45]

S. Zymler, D. Kuhn and B. Rustem, Worst-case Value at Risk of nonlinear portfolios, Management Science, 59 (2013), 172-188.   Google Scholar

show all references

References:
[1]

P. Arbenz, C. Hummel and G. Mainik, Copula based hierarchical risk aggregation through sample reordering, Insurance Mathematics and Economics, 51 (2012), 122-133.  doi: 10.1016/j.insmatheco.2012.03.009.  Google Scholar

[2]

E. D. Arditti and H. Levy, Portfolio efficient analysis in three monents-the multi-period case, Journal of Finance, 30 (1975), 797-809.   Google Scholar

[3]

P. Artzner, F. Delbaen, J. M. Eber and D. Heath, Coherent measures of risk, Mathematical Finance, 9 (1999), 203-228.  doi: 10.1111/1467-9965.00068.  Google Scholar

[4]

I. Bajeux-Besnainou, R. Belhaj, D. Maillard and R. Portait, Portfolio optimization under tracking error and weight constraints, Journal of Financial Research, 34 (2011), 295-330.   Google Scholar

[5]

A. Burcu, D. Fatma and H. Soyuer, Mean-variance-skewness-kurtosis approach to portfolio optimization: an application in Istanbul stock exchange, Ege Academic Review, 11 (2011), 9-17.   Google Scholar

[6]

Y. P. Chang, J. X. Lin and C. T. Yu, Calculating value-at-risk using the granularity adjustment method in the portfolio credit risk model with random loss given default, Journal of Economics and Management, 12 (2016), 157-176.   Google Scholar

[7]

P. Chunhachinda, K. Dandapani, S. Hamid and A. J. Prakash, Portfolio selection and skewness: Evidence from international stock markets, Journal of Banking and Finance, 21 (1997), 143-167.   Google Scholar

[8]

R. Diestel, Graph Theory, Springer-Verlag, New York, 2000. doi: 10.1007/b100033.  Google Scholar

[9]

K. W. Ding, M. H. Wang and N. J. Huang, Distributionally robust chance constrained problem under interval distribution information, Optimlization Letters, 12 (2018), 1315-1328.  doi: 10.1007/s11590-017-1160-7.  Google Scholar

[10]

E. Eberlein and U. Keller, Hyperbolic distributions in finance, Bernoulli, 1 (2000), 281-299.   Google Scholar

[11]

M. R. Fengler and O. Okhrin, Managing risk with a realized copula parameter, Computational Statistics and Data Analysis, 100 (2016), 131-152.  doi: 10.1016/j.csda.2014.07.011.  Google Scholar

[12]

L. Garlappi, R. Uppal and T. Wang, Portfolio selection with parameter and model uncertainty: A multi-prior approach, Review of Financial Studies, 20 (2007), 41-81.   Google Scholar

[13]

L. E. Ghaoui, M. Oks and F. Oustry, Worst-case value-at-risk and robust portfolio optimization: A conic programming approach, Operations Research, 51 (2003), 543-556.  doi: 10.1287/opre.51.4.543.16101.  Google Scholar

[14]

A. Ghorbel and A. Trabelsi, Energy portfolio risk management using time-varying extreme value copula methods, Economic Modelling, 38 (2014), 470-485.   Google Scholar

[15]

C. Giorgio, Tail estimation and mean-VaR portfolio sdeetion in market subject to financial instability, Journal of Banking and Finance, 26 (2002), 1355-1382.   Google Scholar

[16]

D. Goldfarb and G. Iyengar, Robust portfolio selection problems, Mathematics of Operations Research, 28 (2003), 1-38.  doi: 10.1287/moor.28.1.1.14260.  Google Scholar

[17]

Y. Huang and S. H. Fang, Mean-variance analysis of arbitrage portfolios under investment proportion limitations, Chinese Journal of Engineering Mathematics, 24 (2007), 662-668.   Google Scholar

[18]

P. Jana, T. K. Roy and S. K. Mazumder, Multi-objective possibilistic model for portfolio selection with transaction cost, Journal of Computational and Applied Mathematics, 228 (2009), 188-196.  doi: 10.1016/j.cam.2008.09.008.  Google Scholar

[19]

I. Kakouris and B. Rustem, Robust portfolio optimization with copulas, European Journal of Operational Research, 235 (2014), 28-37.  doi: 10.1016/j.ejor.2013.12.022.  Google Scholar

[20]

C. H. Kimberling, A probabilistic interpretation of complete monotonicity, Aequationes Mathematicae, 10 (1974), 152-164.  doi: 10.1007/BF01832852.  Google Scholar

[21]

H. Konno and K. I. Suzuki, A mean-variance-skewness portfolio optimization model, Journal of the Operations Research Society of Japan, 38 (1995), 173-187.   Google Scholar

[22]

K. K. Lai, L. Yu and S. Wang, Mean-variance-skewness-kurtosis-based portfolio optimization, International Multi-Symposiums on Computer and Computational Sciences, 2 (2006), 292-297.   Google Scholar

[23]

J. Li and J. Xu, Multi-objective portfolio selection model with fuzzy random returns and a compromise approach-based genetic algorithm, Information Sciences, 220 (2013), 507-521.  doi: 10.1016/j.ins.2012.07.005.  Google Scholar

[24]

X. Li, Z. Qin and S. Kar, Mean-variance-skewness model for portfolio selection with fuzzy returns, European Journal of Operational Research, 202 (2010), 239-247.   Google Scholar

[25]

X. H. Li and Y. P. You, A note on allocation of portfolio shares of random assets with Archimedean copula, Annals of Operations Research, 212 (2014), 155-167.  doi: 10.1007/s10479-012-1137-y.  Google Scholar

[26]

M. S. Lobo and S. Boyd, The worst-case risk of a portfolio, Journal of Economic Dynamics and Control, 26 (2000), 1159-1193.   Google Scholar

[27]

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

[28]

R. B. Nelsen, An Introduction to Copulas, 2$^{nd}$ edition, Springer, Berlin, 2006.  Google Scholar

[29]

M. H. A. Davis and A. R. Norman, Portfolio selection with transaction costs, Mathematics of Operations Research, 15 (1988), 676-713.  doi: 10.1287/moor.15.4.676.  Google Scholar

[30]

H. Sak, W. Hormann and J. Leydold, Efficient risk simulation for liner asset portfolios in the t-copula model, European Journal of Operational Research, 202 (2010), 802-809.   Google Scholar

[31]

A. Sklar, Fonctions de répartition à n dimensions et leurs marges, Publications de l'Institut de Statistique de l'Université de Paris, 8 (1959), 229-231.   Google Scholar

[32]

B. Schweizer and A. Sklar, Probabilistic Metric Spaces, North-Holland Publishing, Amsterdam, 1983.  Google Scholar

[33]

H. Soleimani, H. R. Golmakani and M. H. Salimi, Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm, Expert Systems with Applications, 36 (2009), 5058-5063.   Google Scholar

[34]

J. B. Su, Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market, Economic Modelling, 46 (2015), 204-224.   Google Scholar

[35]

G. Szego, Measures of risk, European Journal of Operational Research, 163 (2005), 5-19.  doi: 10.1016/j.ejor.2003.12.016.  Google Scholar

[36]

H. A. L. Thi and M. Moeini, Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm, Journal of Optimization Theory and Applications, 161 (2014), 199-224.  doi: 10.1007/s10957-012-0197-0.  Google Scholar

[37]

M. H. Wang, J. Yue and N. J. Huang, Robust mean variance portfolio selection model in the jump-diffusion financial market with an intractable claim, Optimization, 66 (2017), 1219-1234.  doi: 10.1080/02331934.2017.1310212.  Google Scholar

[38] D. V. Widder, The Laplace Transform, Princeton University Press, Princeton, 1941.   Google Scholar
[39]

M. Wu, N. J. Huang and B. S. Lee, Portfolio selection with transaction costs and different borrowing and lending interest rates in varying capital structure, Nonlinear Analysis Forum, 9 (2004), 175-200.   Google Scholar

[40]

H. Yang, W. Zhou and K. W. Ding, Markowitz investment portfolio model with transaction costs and capital budget in varying capital structure, Nonlinear Analysis Forum, 19 (2014), 119-127.   Google Scholar

[41]

H. X. Yao and Z. F. Li, Portfolio model and its explicit expressions of portfolio efficient frontier with minimum investment proportion constraint, OR Transactions, 13 (2009), 119-128.   Google Scholar

[42]

X. L. Zhan and S. Y. Zhang, Risk analysis of financial portfolio based on copula-SV model, Journal of Systems and Management, 16 (2007), 302-306.   Google Scholar

[43]

W. G. Zhang, Y. J. Liu and W. J. Xu, A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs, European Journal of Operational Research, 222 (2012), 341-349.  doi: 10.1016/j.ejor.2012.04.023.  Google Scholar

[44]

Q. Zhou, Z. Chen and R. Ming, Copula-based grouped risk aggregation under mixed operation, Applications of Mathematics, 61 (2016), 103-120.  doi: 10.1007/s10492-016-0124-z.  Google Scholar

[45]

S. Zymler, D. Kuhn and B. Rustem, Worst-case Value at Risk of nonlinear portfolios, Management Science, 59 (2013), 172-188.   Google Scholar

Figure 1.  The Structure of Financial Market
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Figure 2.  Cross-Market Mixed Portfolio
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Figure 3.  Index Trend from January 2010 to March 2017
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Figure 4.  Standardized Index Trend from January 2010 to March 2017
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Figure 5.  Rate of Return from January 2010 to December 2016
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Figure 6.  Return Curve of Model 4
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Table 1.  Moment information of daily return rate from January 2010 to December 2016
Name Mean(%) Variance Skewness Kurtosis
SZZS 0.0170 2.1816 -0.3702 7.6127
HSI 0.0072 1.3876 -0.0621 6.0437
DAX 0.0483 1.7579 -0.1611 5.2904
AEX 0.0243 1.3845 -0.1214 5.8596
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Name Mean(%) Variance Skewness Kurtosis
SZZS 0.0170 2.1816 -0.3702 7.6127
HSI 0.0072 1.3876 -0.0621 6.0437
DAX 0.0483 1.7579 -0.1611 5.2904
AEX 0.0243 1.3845 -0.1214 5.8596
Table 2.  Correlation Coefficient Matrix
SZZS HSI DAX AEX
SZZS 1.0000 0.5265 0.1463 0.1700
HSI 1.0000 0.3596 0.3900
DAX 1.0000 0.9146
AEX 1.0000
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SZZS HSI DAX AEX
SZZS 1.0000 0.5265 0.1463 0.1700
HSI 1.0000 0.3596 0.3900
DAX 1.0000 0.9146
AEX 1.0000
Table 3.  Rank Correlation Coefficient Kendall's $ \tau $
DJIA DAX SZZS N225
DJIA 1.0000 0.3575 0.0865 0.1060
DAX 1.0000 0.2321 0.2458
SZZS 1.0000 0.7144
N225 1.0000
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DJIA DAX SZZS N225
DJIA 1.0000 0.3575 0.0865 0.1060
DAX 1.0000 0.2321 0.2458
SZZS 1.0000 0.7144
N225 1.0000
Table 4.  Model Settings
Model 1 Model 2 Model 3 Model 4
Risk Measurement Variance WCVaR WCVaR WCVaR
Hierarchical Risk Integration NO NO YES YES
Skewness NO NO NO YES
Transaction Cost YES YES YES YES
Investment Proportion Limitation YES YES YES YES
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Model 1 Model 2 Model 3 Model 4
Risk Measurement Variance WCVaR WCVaR WCVaR
Hierarchical Risk Integration NO NO YES YES
Skewness NO NO NO YES
Transaction Cost YES YES YES YES
Investment Proportion Limitation YES YES YES YES
Table 5.  Statistic Comparison
Item Model 1 Model 2 Model 3 Model 4
WCVaR of optimal portfolio - 1.7816 1.7254 1.6914
Total Rate of Return(%) 15.4084 16.1731 17.6015 18.3447
Daily Rate of Return(%) 0.0518 0.0541 0.0586 0.0609
Variance 0.8661 0.8876 0.9833 1.0135
Daily Maximum Loss(%) -5.6033 -5.3498 -5.1381 -4.9078
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Item Model 1 Model 2 Model 3 Model 4
WCVaR of optimal portfolio - 1.7816 1.7254 1.6914
Total Rate of Return(%) 15.4084 16.1731 17.6015 18.3447
Daily Rate of Return(%) 0.0518 0.0541 0.0586 0.0609
Variance 0.8661 0.8876 0.9833 1.0135
Daily Maximum Loss(%) -5.6033 -5.3498 -5.1381 -4.9078
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