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A copula–GARCH model for macro asset allocation of a portfolio with commodities

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  • Luca Riccetti

Abstract

Many authors have suggested that the mean-variance criterion, conceived by Markowitz (The Journal of Finance 7(1):77–91, 1952 ), is not optimal for asset allocation, because the investor expected utility function is better proxied by a function that uses higher moments and because returns are distributed in a non-Normal way, being asymmetric and/or leptokurtic, so the mean-variance criterion cannot correctly proxy the expected utility with non-Normal returns. In Riccetti (The use of copulas in asset allocation: when and how a copula model can be useful? LAP Lambert, Saarbrücken 2010 ), a copula–GARCH model is applied and it is found that copulas are not useful for choosing among stock indices, but can be useful in a macro asset allocation model, that is, for choosing stock and bond composition of portfolios. In this paper I apply that copula–GARCH model for the macro asset allocation of portfolios containing a commodity component. I find that the copula model appears to be useful and better than the mean-variance one for the macro asset allocation also in presence of a commodity index, even if it is not better than GARCH models on independent univariate series, probably because of the low correlation of the commodity index returns to the stock, the bond and the exchange rate returns. Copyright Springer-Verlag 2013

Suggested Citation

  • Luca Riccetti, 2013. "A copula–GARCH model for macro asset allocation of a portfolio with commodities," Empirical Economics, Springer, vol. 44(3), pages 1315-1336, June.
    • Handle: RePEc:spr:empeco:v:44:y:2013:i:3:p:1315-1336
    DOI: 10.1007/s00181-012-0577-1
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    Cited by:

    1. Luca RICCETTI, 2011. "A Copula-GARCH Model for Macro Asset Allocation of a Portfolio with Commodities: an Out-of-Sample Analysis," Working Papers 355, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Marina Brogi & Valentina Lagasio & Luca Riccetti, 2021. "Systemic risk measurement: bucketing global systemically important banks," Annals of Finance, Springer, vol. 17(3), pages 319-351, September.
    3. Abdallah Ben Saida & Jean-luc Prigent, 2018. "On the robustness of portfolio allocation under copula misspecification," Annals of Operations Research, Springer, vol. 262(2), pages 631-652, March.
    4. Huang, Wanling & Mollick, André Varella & Nguyen, Khoa Huu, 2016. "U.S. stock markets and the role of real interest rates," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 231-242.
    5. So, Mike K.P. & Chan, Thomas W.C. & Chu, Amanda M.Y., 2022. "Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management," Journal of Econometrics, Elsevier, vol. 227(1), pages 151-167.
    6. Wanling Huang & André Varella Mollick & Khoa Huu Nguyen, 2017. "Dynamic responses and tail-dependence among commodities, the US real interest rate and the dollar," Empirical Economics, Springer, vol. 53(3), pages 959-997, November.
    7. Massimo Biasin & Roy Cerqueti & Emanuela Giacomini & Nicoletta Marinelli & Anna Grazia Quaranta & Luca Riccetti, 2019. "Macro Asset Allocation with Social Impact Investments," Sustainability, MDPI, vol. 11(11), pages 1-19, June.
    8. Jain, Prachi & Maitra, Debasish, 2023. "Risk implications of dependence in the commodities: A copula-based analysis," Global Finance Journal, Elsevier, vol. 57(C).
    9. Sukcharoen, Kunlapath & Leatham, David J., 2017. "Hedging downside risk of oil refineries: A vine copula approach," Energy Economics, Elsevier, vol. 66(C), pages 493-507.

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    More about this item

    Keywords

    Commodities; Portfolio choice; Asset allocation; Copula; GARCH; C52; C53; C58; G11; G17;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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