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Disasters, Large Drawdowns, and Long-term Asset Management

Author

Listed:
  • Eric Jondeau

    (University of Lausanne - Faculty of Business and Economics (HEC Lausanne); Swiss Finance Institute)

  • Alexandre Pauli

    (University of Lausanne and Swiss Finance Institute)

Abstract

Long-term investors are often reluctant to invest in assets or strategies that can suffer from large drawdowns. A major challenge for such investors is to gain access to predictions of large drawdowns in order to precisely design strategies minimizing these drawdowns. In this paper, we describe a multivariate Markov-switching model framework that allows us to predict large drawdowns. We provide evidence that three regimes are necessary to capture the negative trends in expected returns that generate large drawdowns, and we correctly predict conditional drawdowns. In addition, investment strategies based on these models outperform model-free strategies based on the empirical distribution of drawdowns. These results hold within and out of the sample.

Suggested Citation

  • Eric Jondeau & Alexandre Pauli, 2021. "Disasters, Large Drawdowns, and Long-term Asset Management," Swiss Finance Institute Research Paper Series 21-37, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2137
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    More about this item

    Keywords

    Large drawdowns; Stock-market returns; Markov-switching model; Portfolio allocation model;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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