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Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

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  • Xingyu Dai
  • Dongna Zhang
  • Chi Keung Marco Lau
  • Qunwei Wang

Abstract

This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.

Suggested Citation

  • Xingyu Dai & Dongna Zhang & Chi Keung Marco Lau & Qunwei Wang, 2023. "Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2167-2196, December.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:8:p:2167-2196
    DOI: 10.1002/for.3010
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    as
    1. Gabrielli, Paolo & Aboutalebi, Reyhaneh & Sansavini, Giovanni, 2022. "Mitigating financial risk of corporate power purchase agreements via portfolio optimization," Energy Economics, Elsevier, vol. 109(C).
    2. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    3. Rehman, Mobeen Ur & Vo, Xuan Vinh & McIver, Ron & Kang, Sang Hoon, 2022. "Sensitivity of US sectoral returns to energy commodities under different investment horizons and market conditions," Energy Economics, Elsevier, vol. 108(C).
    4. Hou, Yang & Li, Steven, 2013. "Hedging performance of Chinese stock index futures: An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 109-131.
    5. Lau, Chi Keung & Pal, Shreya & Mahalik, Mantu Kumar & Gozgor, Giray, 2022. "Economic globalization convergence in high and low globalized developing economies: Implications for the post Covid-19 era," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 1027-1039.
    6. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    7. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    8. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    9. Dai, Xingyu & Xiao, Ling & Wang, Qunwei & Dhesi, Gurjeet, 2021. "Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS," Energy Policy, Elsevier, vol. 156(C).
    10. Wang, Erhong & Gozgor, Giray & Mahalik, Mantu Kumar & Patel, Gupteswar & Hu, Guoheng, 2022. "Effects of institutional quality and political risk on the renewable energy consumption in the OECD countries," Resources Policy, Elsevier, vol. 79(C).
    11. Mawuli Segnon & Mark Trede, 2018. "Forecasting market risk of portfolios: copula-Markov switching multifractal approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(14), pages 1123-1143, September.
    12. Gozgor, Giray & Lau, Chi Keung Marco & Sheng, Xin & Yarovaya, Larisa, 2019. "The role of uncertainty measures on the returns of gold," Economics Letters, Elsevier, vol. 185(C).
    13. Qunwei Wang & Ye Hang & Jin‐Li Hu & Ching‐Ren Chiu, 2018. "An alternative metafrontier framework for measuring the heterogeneity of technology," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(5), pages 427-445, August.
    14. Massimo Pierro & Jack Mosevich, 2011. "Effects of skewness and kurtosis on portfolio rankings," Quantitative Finance, Taylor & Francis Journals, vol. 11(10), pages 1449-1453.
    15. Hang, Ye & Sun, Jiasen & Wang, Qunwei & Zhao, Zengyao & Wang, Yizhong, 2015. "Measuring energy inefficiency with undesirable outputs and technology heterogeneity in Chinese cities," Economic Modelling, Elsevier, vol. 49(C), pages 46-52.
    16. Bangzhu Zhu & Shunxin Ye & Kaijian He & Julien Chevallier & Rui Xie, 2019. "Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach," Annals of Operations Research, Springer, vol. 281(1), pages 373-395, October.
    17. S. Geissel & H. Graf & J. Herbinger & F. T. Seifried, 2022. "Portfolio optimization with optimal expected utility risk measures," Annals of Operations Research, Springer, vol. 309(1), pages 59-77, February.
    18. Fima Klebaner & Zinoviy Landsman & Udi Makov & Jing Yao, 2017. "Optimal portfolios with downside risk," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 315-325, March.
    19. Sangbae Kim & Francis In, 2010. "Portfolio allocation and the investment horizon: a multiscaling approach," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 443-453.
    20. Jahangir Sultan & Antonios K. Alexandridis & Mohammad Hasan & Xuxi Guo, 2019. "Hedging performance of multiscale hedge ratios," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1613-1632, December.
    21. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
    22. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    23. Gozgor, Giray & Demir, Ender & Belas, Jaroslav & Yesilyurt, Serkan, 2019. "Does economic uncertainty affect domestic credits? an empirical investigation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    24. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    25. Ren, Xiaohang & Zhang, Xiao & Yan, Cheng & Gozgor, Giray, 2022. "Climate policy uncertainty and firm-level total factor productivity: Evidence from China," Energy Economics, Elsevier, vol. 113(C).
    26. Wang, H. & Pan, Chen & Wang, Qunwei & Zhou, P., 2020. "Assessing sustainability performance of global supply chains: An input-output modeling approach," European Journal of Operational Research, Elsevier, vol. 285(1), pages 393-404.
    27. Young Shin Kim, 2022. "Portfolio optimization and marginal contribution to risk on multivariate normal tempered stable model," Annals of Operations Research, Springer, vol. 312(2), pages 853-881, May.
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