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Optimizing the Model Investment Portfolios Based on Coherent Risk Measures under Conditions of Asymmetric Financial Market Volatility Manoilenko O. V., Kuznetsova S. O., Koreshnikov F. Y.
Manoilenko, Oleksandr V., Kuznetsova, Svitlana O., and Koreshnikov, Fedir Yu. (2025) “Optimizing the Model Investment Portfolios Based on Coherent Risk Measures under Conditions of Asymmetric Financial Market Volatility.” The Problems of Economy 4:373–380. https://doi.org/10.32983/2222-0712-2025-4-373-380
Section: Finance and banking
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UDC 336.76
Abstract: This article is dedicated to the optimization of model investment portfolios by integrating asymmetric volatility forecasting using GJR-GARCH models, considering the minimization of Conditional Value at Risk (CVaR). The advantages and disadvantages of classical Markowitz theory are examined. The use of the coherent risk measure CVaR, which quantitatively evaluates losses in the distribution at the «tail», is explored and proposed in combination with the GJR-GARCH model. The GJR-GARCH model, in turn, accounts for the asymmetric volatility response to positive and negative market shocks. Empirical analysis is performed on global index data covering 2005–2023, which includes global crises and periods of high volatility. The proposed methodology comprises two stages. In the first stage, the dynamics of asset volatility are modeled using the EGARCH/GJR-GARCH framework, taking into account asymmetry. In the second stage, portfolio weights are optimized and risk is minimized. The formalization of the problem is carried out using linear programming. The results indicate that the GJR-CVaR model demonstrates better efficiency than traditional approaches, providing higher risk-adjusted returns, lower maximum drawdown, and a higher Sortino ratio, which, in turn, indicates the ability to respond proactively to market fluctuations and manage risks more effectively during periods of instability. The article demonstrates that an important direction in modern investing is the combination of asymmetric volatility models with coherent risk measures. This approach provides instruments for efficient portfolio risk management. Future research directions may include the development of multivariate volatility models to account for correlations between assets over time and the integration of scenario analysis approaches to assess the impact of changing market conditions on portfolio structure.
Keywords: finance, investing, investment portfolio, portfolio risk, volatility, financial markets, financial ecosystem.
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Manoilenko Oleksandr V. – Doctor of Sciences (Economics), Professor, Head of the Department, Department of Accounting and Finance, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine) Email: oleksandr.manoylenko@khpi.edu.ua Kuznetsova Svitlana O. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Accounting and Finance, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine) Email: svitlana.kuznetsova@khpi.edu.ua Koreshnikov Fedir Yu. – Postgraduate Student, Department of Accounting and Finance, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine) Email: Fedir.Koreshnykov@emmb.khpi.edu.ua
List of references in article
Artzner P., Delbaen F., Eber J. M. & Heath D. (1999). Coherent Measures of Risk. Mathematical Finance, 3(9), 203–228. https://doi.org/10.1111/1467-9965.00068
Bessler W. & Wolff D. (2015). Do Commodities add Value in Multi-Asset-Portfolios? An Out-of-Sample Analysis for different Investment Strategies. Journal of Banking and Finance, 60, 1–20. https://doi.org/10.1016/j.jbankfin.2015.06.021
Bessler W. & Wolff D. (2024). Portfolio Optimization with Sector Return Prediction Models. Journal of Risk and Financial Management, 6(17), Article 254. https://doi.org/10.3390/jrfm17060254
Chun D., Kang J. & Kim J. (2024). Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets. Financial Innovation, 10, Article 124. https://doi.org/10.1186/s40854-024-00648-w
Glosten L. R., Jagannathan R. & Runkle D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 5(48), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Gu S., Kelly B. & Xiu D. (2020). Empirical Asset Pricing via Machine Learning. Journal of Finance, 4(75), 2195–2243. https://doi.org/10.1111/jofi.12901
Hirina O. & Ivchenko V. (2023). Bahatokryterialna optymizatsiia struktury portfelia realnykh investytsii [Multi-criteria optimization of the real investment portfolio structure]. Economy and Society, 50. https://doi.org/10.32782/2524-0072/2023-50-37
Hulyk T. & Kravets V. (2024). Sfera zastosuvannia modeli CAPM u analizi metodiv otsinky ryzykiv [Scope of application of the CAPM model in the analysis of risk assessment methods]. Economy and Society, 69. https://doi.org/10.32782/2524-0072/2024-69-132
Kelly B. & Pruitt S. (2015). The three-pass regression filter: A new approach to forecasting using many predictors. Journal of Econometrics, 186, 294–316. https://doi.org/10.1016/j.jeconom.2015.02.011
Neely C. J., Rapach D. E., Tu J. & Zhou G. (2014). Forecasting the equity risk premium: The role of technical indicators. Management Science, 6(60), 1772–1791. https://doi.org/10.1287/mnsc.2013.1838
Rapach D. E., Strauss J. K. & Zhou G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23, 821–862.
Rockafellar R. T. & Uryasev S. (2000). Optimization of Conditional Value-at-Risk. The Journal of Risk, 1(3), 21–41. https://doi.org/10.21314/JOR.2000.038
Soleymani F. & Paquet E. (2021). Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket. Expert Systems with Applications, 182, Article 115127. https://doi.org/10.1016/j.eswa.2021.115127
Statista https://www.statista.com/
Sutiene K., Schwendner P. & Sipos C. (2024). Enhancing portfolio management using artificial intelligence: literature review. Frontiers in Artificial Intelligence, 7, Article 1371502. https://doi.org/10.3389/frai.2024.1371502
Tran P., Pham T. K. A., Phan H. T. & Nguyen C. V. (2024). Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam. Humanities and Social Sciences Communications, 11, Article 393. https://doi.org/10.1057/s41599-024-02807-x
Wang J. (2023). Application of Portfolio Price Forecasting Based on ARIMA-GARCH Model. Proceedings of ICAID, 9, 296–303. https://doi.org/10.2991/978-94-6463-222-4_31
Zakharkin O., Zakharkina L. & Serheiev A. (2025). Porivnialnyi analiz efektyvnosti ETF-fondiv yak instrumentiv pasyvnoho investuvannia [Comparative analysis of the efficiency of ETF funds as passive investment tools]. Problemy suchasnykh transformatsii. Seriia: Ekonomika ta upravlinnia, 18. https://doi.org/10.54929/2786-5738-2025-18-08-03
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