Investment portfolio based on value at risk (VaR) and conditional value at risk (CVaR) with emphasis on the role of return distribution

Document Type : Original Article

Authors
1 Department of Industrial Management & Economic, Science and Research Branch
2 Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Industrial Engineering, Firoozkoh Branch, Islamic Azad University, Firoozkoh, Iran
4 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,Tehran, Iran
10.30495/jik.2024.77119.4480
Abstract
In this study, VaR and CvaR portfolio performance was compared based on return probability distributions. For this purpose, the stock returns of 28 active symbols of chemical products group of Tehran Stock Exchange during the period from April 2019 to August 2023 were used. The results based on the solution of linear programming with the particle swarm optimization algorithm showed that the formation of portfolio based on VaR/CVaR leads to higher performance (return to risk ratio) compared to the minimum variance method. The separation of the portfolio performance results based on the probability distribution of stock returns showed that the VaR/CVaR based on the empirical distribution of data and at the confidence levels of 90, 95 and 99 percent, provides more favorable performance and the ratio of return to portfolio risk for the portfolio resulting from the empirical distribution of the data is higher.Regardless of the usefulness of VaR and CvaR methods in portfolio formation compared to minimum variance portfolios, calculating the return distribution based on the empirical distribution is desirable in the Iranian capital market.
Keywords

  • ·        بابالویان، شهرام؛ چگینی، مژگان (1394). مقایسه مدل مارکویتز و ارزش در معرض ریسک شرطی و کاربرد آنها در تشکیل سبد سرمایه‌گذاری در بورس اوراق بهادارتهران. کنفرانس بین المللی پژوهشهای نوین در مدیریت و مهندسی صنایع.

    ·        خادم پور آرانی، عباس، کیقبادی، امیر رضا، معدنچی زاج، مهدی؛ زمردیان، غلامرضا (1401). مدل تلفیقی چند هدفه و اقتصادسنجی جهت بهینه‌سازی پرتفوی سهام. پژوهش‌های حسابداری مالی و حسابرسی، 14(54)، 263-292.

    ·        فلاح شمس، میرفیض؛ صادقی، امیر (1400). بهینه‌سازی پورتفوی با استفاده از رویکرد کاپولا و ارزش در معرض ریسک چند متغیره شرطی در بورس اوراق بهادار تهران. دانش سرمایه‌گذاری، 10(40)، 205-226.

    • فلاح‌پور، سعید، راعی، رضا، فدائی‌نژاد، محمداسماعیل؛ مناجاتی، رضا (1398). ارائه مدلی جهت بهینه‌سازی فعال سبد سهام با استفاده از ارزش در معرض ریسک شرطی؛ کاربردی از رویکرد مدل‌های ناهمسانی واریانس شرطی بر اساس رویکرد الگورتیم DE. دانش سرمایه‌گذاری، 8(30)، 37-50.
    • Aljinovi´c, Z.; Marasovi´c, B.; Šestanovi´c, T. (2021). Cryptocurrency Portfolio Selection—A Multicriteria Approach. Mathematics, 9, 1677.
    • Batrancea, L.; Rathnaswamy, M.K.; Batrancea, I. (2021). A Panel Data Analysis on Determinants of Economic Growth in Seven Non BCBS Countries. J. Knowl. Econ. 13, 1651–1665.
    • Benati, S.; Conde, E. (2021). A relative robust approach on expected returns with bounded CVaR for portfolio selection. Eur. J. Oper. Res. 296, 332–352.
    • Bodnar, T.; Lindholm, M.; Niklasson, V.; Thorsén, E. (2022). Bayesian portfolio selection using VaR and CVaR. Appl. Math. Comput. 427, 127120.
    • Burney, S.M.; Jilani, T.; Tariq, H.; Amjad, U. (2019). A Portfolio Optimization Algorithm Using Fuzzy Granularity Based Clustering. Broad Res. Artifical Intell. Neurosci. 10, 159–173.
    • Chen, Z.P.; Lin, R.Y. (2006). Mutual fund performance evaluation using data envelopment analysis with new risk measures. OR Spectr, 28, 375–398.
    • Chyzak, F., Nielsen, F. (2019). A closed-form formula for the Kullback-Leibler divergence between Cauchy distributions. arXiv:1905.10965
    • Gabrielli, P.; Aboutalebi, R.; Sansavini, G. (2022). Mitigating financial risk of corporate power purchase agreements via. Energy Econ., 109, 105980.
    • Kaucic, M. (2019). Equity portfolio managment with cardinality constraints and risk parity control using multi-objective particale swarm optimization. Comput. Oper. Res., 109, 300–316.
    • Lamb, J.D.; Tee, K.-H. (2012). Data envelopment analysis models of investment funds. Eur. J. Oper. Res. 216, 687–696.
    • Li, M.; Lei, D.; Cai, J. (2019). Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives. Swarm Evol. Comput., 49, 34–43.
    • Liu, C.; Yin, Y. (2018). Particle swarm optimised analysis of investment decision. Cogn. Syst. Res., 52, 685–690.
    • Markowitz, H. (1952). Portfolio selection. J. Financ. 7, 77–91.
    • Strub, M.S.; Li, D.; Cui, X.; Gao, J. (2019). Discrete-Time Mean-CVaR Portfolio Selection and Time-Consistency. J. Econ. Dyn. Control., 108, 103751.
    • Xiao, H.; Ren, T.; Zhou, Z.; Liu, W. (2021). Parameter uncertainty in estimation of portfolio efficiency: Evidence from an interval diversification-consistent DEA approach. Omega, 103, 102357
    • Yin, Z.; Gao, Q. (2019). A Novel Imperialist Competitive Algorithm for Scheme Configuration Rules Extraction of Product Service. Procedia Cirp, 80, 762–767
    • Zhang, H. (2020). Optimization of risk control in financial markets based on particale swarm optimization algorithm. J. Comput. Appl. Math, 368, 112530.
    • Zhou, Z.; Gao, M.; Xiao, H.; Wang, R.; Liu, W. (2021). Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources. Omega, 104, 102479.