Conditional Value-at-Risk-Based Portfolio Optimization for Multi-Asset Treasury Management in Nigerian Banks

References

[1] C. Acerbi and D. Tasche, “Expected Shortfall: A Natural Coherent Alternative to Value at Risk,” Economic Notes, vol. 31, no. 2, pp. 379–388, 2002.

[2] T. S. Alotaibi, L. Dalla Valle, and M. J. Craven, “The Worst Case GARCH-Copula CVaR Approach for Portfolio Optimisation: Evidence from Financial Markets,” Journal of Risk and Financial Management, vol. 15, no. 10, p. 482, 2022.

[3] P. Artzner, F. Delbaen, J. Eber, and D. Heath, “Coherent Measures of Risk,” Mathematical Finance, vol. 9, no. 3, pp. 203–228, 1999.

[4] M. Buczynski and M. Chlebus, “GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks,” Computational Economics, vol. 63, no. 5, pp. 1949–1979, 2024.

[5] G. Chakraborty, G. R. Chandrashekhar, and G. Balasubramanian, “Measurement of Extreme Market Risk: Insights from a Comprehensive Literature Review,” Cogent Economics & Finance, vol. 9, no. 1, p. 1920150, 2021.

[6] K. Dowd, Measuring Market Risk, 2nd ed. Chichester: Wiley, 2008.

[7] Z. Fan, R. Ji, and M. A. Lejeune, “Distributionally Robust Portfolio Optimization under Marginal and Copula Ambiguity,” Journal of Optimization Theory and Applications, vol. 203, no. 3, pp. 2870–2907, 2024.

[8] A. Georgantas, M. Doumpos, and C. Zopounidis, “Robust Optimization Approaches for Portfolio Selection: A Comparative Analysis,” Annals of Operations Research, vol. 339, no. 3, pp. 1205–1221, 2024.

[9] J. C. Hull, Options, Futures, and Other Derivatives, 10th ed. Pearson, 2017.

[10] Y. Jiang, J. Olmo, and M. Atwi, “Dynamic Robust Portfolio Selection under Market Distress,” The North American Journal of Economics and Finance, vol. 69, p. 102037, 2024.

[11] P. Krokhmal, S. Uryasev, and J. Palmquist, “Portfolio Optimization with Conditional Value-at-Risk Objective and Constraints,” The Journal of Risk, vol. 4, no. 2, pp. 43–68, 2001.

[12] S. Lotfi and S. A. Zenios, “Robust Mean-to-CvaR Optimization under Ambiguity in Distribution Means and Covariance,” Review of Managerial Science, vol. 18, no. 7, pp. 2115–2140, 2024.

[13] H. Markowitz, “Portfolio Selection,” The Journal of Finance, vol. 7, no. 1, pp. 77–91, 1952.

[14] S. Mitra and T. Ji, “Risk Measures in Quantitative Finance,” International Journal of Banking, Accounting and Finance, vol. 1, no. 2, pp. 125–143, 2010.

[15] K. Nakagawa, M. Abe, and S. Kuroki, “Doubly Robust Mean-CVaR Portfolio,” arXiv preprint arXiv:2309.11693, 2023.

[16] M. Nasini, M. Labbé, and L. Brotcorne, “Multi-market Portfolio Optimization with Conditional Value-at-Risk,” European Journal of Operational Research, vol. 300, no. 1, pp. 350–365, 2022.

[17] M. N. Alonso, “Portfolio Optimization under Multiple Risk Constraints: A Unified Theoretical and Empirical Framework,” Technical Report, Artificial Intelligence Finance Institute, 2025.

[18] M. N. Alonso, “Conditional Value-at-Risk Portfolio Optimization: The Kusuoka Representation,” Technical Report, Artificial Intelligence Finance Institute, 2025.

[19] R. T. Rockafellar and S. Uryasev, “Optimization of Conditional Value-at-Risk,” The Journal of Risk, vol. 2, no. 3, pp. 21–41, 2000.

[20] F. A. Sortino and R. van der Meer, “Downside Risk: Capturing What’s at Stake in Investment Situations,” The Journal of Portfolio Management, 1991.

[21] U. A. Theiler, “Risk-Return Management Approach for the Bank Portfolio,” 2004.

[22] J. Tobin, “Liquidity Preference as Behavior Towards Risk,” The Review of Economic Studies, vol. 25, no. 2, pp. 65–86, 1958.

[23] S. Uryasev, U. A. Theiler, and G. Serraino, “Risk-Return Optimization with Different Risk-Aggregation Strategies,” The Journal of Risk Finance, vol. 11, no. 2, pp. 129–146, 2010.

@article{1715062,
      author = {Opeyemi A. Oyenekan, Adeyemi O. Akeju},
      title = {Conditional Value-at-Risk-Based Portfolio Optimization for Multi-Asset Treasury Management in Nigerian Banks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {3090-3098},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715062.pdf},
      abstract = {Portfolio optimization remains a central problem in financial mathematics, with the classical Mean–Variance framework widely applied in asset allocation decisions. De- spite its popularity, variance as a risk measure fails to adequately capture extreme losses and tail risk, which are critical in volatile financial markets. This study extends the traditional portfolio optimization framework by incorporating Conditional Value at Risk (CVaR) as an alternative risk measure and explicitly accounting for transaction costs. Using historical asset return data of the Nigerian financial market, portfolios are constructed under both Mean–Variance and Mean–CVaR optimization frameworks, evaluated under frictionless conditions and scenarios involving proportional trans- action costs. The optimization problems are solved numerically using Python-based computational techniques, and efficient frontiers are generated for comparative analysis. Furthermore, stress testing is conducted under adverse market scenarios, including foreign exchange shocks and interest rate spikes, to assess portfolio robustness. The results indicate that Mean–CVaR optimized portfolios exhibit greater stability and lower downside risk under stressed conditions, particularly when transaction costs are present. These findings highlight the practical relevance of CVaR-based optimization in realistic portfolio management and risk control settings.},
      keywords = {Portfolio Optimization, Conditional Value at Risk, Mean–Variance Model, Transaction Costs},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715062}
  }