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Optimal commodity asset allocation with a coherent market risk modeling

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  • Al Janabi, Mazin A.M.

Abstract

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.

Suggested Citation

  • Al Janabi, Mazin A.M., 2012. "Optimal commodity asset allocation with a coherent market risk modeling," Review of Financial Economics, Elsevier, vol. 21(3), pages 131-140.
    • Handle: RePEc:eee:revfin:v:21:y:2012:i:3:p:131-140
    DOI: 10.1016/j.rfe.2012.06.007
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    References listed on IDEAS

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    8. Sohnke M. Bartram, 2005. "The Impact of Commodity Price Risk on Firm Value - An Empirical Analysis of Corporate Commodity Price Exposures," Multinational Finance Journal, Multinational Finance Journal, vol. 9(3-4), pages 161-187, September.
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    Cited by:

    1. Aboura, Sofiane & Chevallier, Julien, 2014. "Cross-market spillovers with ‘volatility surprise’," Review of Financial Economics, Elsevier, vol. 23(4), pages 194-207.
    2. D. Sykes Wilford, 2012. "True Markowitz or assumptions we break and why it matters," Review of Financial Economics, John Wiley & Sons, vol. 21(3), pages 93-101, September.
    3. Wilford, D. Sykes, 2012. "True Markowitz or assumptions we break and why it matters," Review of Financial Economics, Elsevier, vol. 21(3), pages 93-101.
    4. Al Janabi, Mazin A.M. & Arreola Hernandez, Jose & Berger, Theo & Nguyen, Duc Khuong, 2017. "Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1121-1131.
    5. Al Janabi, Mazin A.M., 2014. "Optimal and investable portfolios: An empirical analysis with scenario optimization algorithms under crisis market prospects," Economic Modelling, Elsevier, vol. 40(C), pages 369-381.
    6. Al Janabi, Mazin A.M. & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2019. "Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    7. repec:ipg:wpaper:2014-469 is not listed on IDEAS

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    More about this item

    Keywords

    Commodity; Financial crisis; Financial engineering; Liquidity risk; Portfolio management;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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