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The Black-Litterman model: the definition of views based on volatility forecasts

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  • Andi Duqi
  • Leonardo Franci
  • Giuseppe Torluccio

Abstract

This article aims to implement a portfolio optimization strategy considering two fundamental aspects: the empirical regularities observed in the time series of stock returns, and the views of portfolio managers about these regularities. From an analytical point of view, all the results are examined through an application of the approach of Black and Litterman (1992). In particular, our innovative contribution to the extant literature is the use of the EGARCH-M (exponential GARCH-in-mean) model to formulate a volatility forecast of returns used as an input for determining some subjective views to be included in the Black-Litterman model. The bets of the portfolio manager thus enter into the mechanism of generating expectations about the vector of returns, revealing information about investment opportunities. The results show that the Black-Litterman (BL) model using the EGARCH inputs produces allocations with potentially sizeable benefits. Greater reliance on the implied BL excess returns, in setting the allocations, result in higher risk-return ratio.

Suggested Citation

  • Andi Duqi & Leonardo Franci & Giuseppe Torluccio, 2014. "The Black-Litterman model: the definition of views based on volatility forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 24(19), pages 1285-1296, October.
    • Handle: RePEc:taf:apfiec:v:24:y:2014:i:19:p:1285-1296
    DOI: 10.1080/09603107.2014.925056
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    References listed on IDEAS

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    1. Giulio Palomba, 2008. "Multivariate GARCH models and the Black-Litterman approach for tracking error constrained portfolios: an empirical analysis," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 379-413.
    2. James H. Stock & Mark W. Watson, 2003. "How did leading indicator forecasts perform during the 2001 recession?," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 89(Sum), pages 71-90.
    3. Shawkat M.Hammoudeh & Yuan Yuan & Michael McAleer, 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," Working Papers in Economics 10/33, University of Canterbury, Department of Economics and Finance.
    4. Francesco Giurda & Elias Tzavalis, 2004. "Is the Currency Risk Priced in Equity Markets?," Working Papers 511, Queen Mary University of London, School of Economics and Finance.
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    Cited by:

    1. Çela, Eranda & Hafner, Stephan & Mestel, Roland & Pferschy, Ulrich, 2021. "Mean-variance portfolio optimization based on ordinal information," Journal of Banking & Finance, Elsevier, vol. 122(C).
    2. Frieder Meyer-Bullerdiek, 2021. "Out-of-sample performance of the Black-Litterman model," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 10(2), pages 1-2.
    3. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    4. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).

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