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Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach

Author

Listed:
  • Vladimir Rankovic
  • Mikica Drenovak
  • Branko Uroševic
  • Ranko Jelic

Abstract

In accordance with Basel Capital Accords, the Capital Requirements (CR) for market risk exposure of banks is a nonlinear function of Value-at-Risk (VaR). Importantly, the CR is calculated based on a bank’s actual portfolio, i.e. the portfolio represented by its current holdings. To tackle mean-VaR portfolio optimization within the actual portfolio framework (APF), we propose a novel mean-VaR optimization method where VaR is estimated using a univariate Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) volatility model. The optimization was performed by employing a Nondominated Sorting Genetic Algorithm (NSGA-II). On a sample of 40 large US stocks, our procedure provided superior mean-VaR trade-offs compared to those obtained from applying more customary mean-multivariate GARCH and historical VaR models. The results hold true in both low and high volatility samples.

Suggested Citation

  • Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    • Handle: RePEc:ces:ceswps:_5731
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    References listed on IDEAS

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    Cited by:

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    2. Yinpeng Zhang & Zhixin Liu & Xueying Yu, 2017. "The Diversification Benefits of Including Carbon Assets in Financial Portfolios," Sustainability, MDPI, vol. 9(3), pages 1-13, March.
    3. Yiwen Cui & Lei Li & Zijie Tang, 2021. "Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(4), pages 322-336, April.
    4. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    5. Zexuan Yin & Paolo Barucca, 2022. "Variational Heteroscedastic Volatility Model," Papers 2204.05806, arXiv.org.
    6. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Nonlinear spillover and portfolio allocation characteristics of energy equity sectors: Evidence from the United States and Canada," Review of International Economics, Wiley Blackwell, vol. 30(1), pages 1-33, February.
    7. Jolanta Tamošaitienė & Vahidreza Yousefi & Hamed Tabasi, 2021. "Project Portfolio Construction Using Extreme Value Theory," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    8. P. Kumar & Jyotirmayee Behera & A. K. Bhurjee, 2022. "Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 41-77, March.
    9. Sukono & Dedi Rosadi & Di Asih I Maruddani & Riza Andrian Ibrahim & Muhamad Deni Johansyah, 2024. "Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model," Mathematics, MDPI, vol. 12(2), pages 1-22, January.
    10. Kresta Aleš & Wang Anlan, 2020. "Portfolio Optimization Efficiency Test Considering Data Snooping Bias," Business Systems Research, Sciendo, vol. 11(2), pages 73-85, October.
    11. Drenovak, Mikica & Ranković, Vladimir & Urošević, Branko & Jelic, Ranko, 2022. "Mean-Maximum Drawdown Optimization of Buy-and-Hold Portfolios Using a Multi-objective Evolutionary Algorithm," Finance Research Letters, Elsevier, vol. 46(PA).
    12. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.
    13. Mladen Stamenković, 2023. "Where Did All The Papers Go? A Bibliometric Overview Of Publications In Economics From Serbia," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 68(236), pages 29-50, January –.

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    More about this item

    Keywords

    portfolio optimization; actual portfolios; value at risk; GARCH; NSGA-II;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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