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Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation

Author

Listed:
  • Peter Nystrup

    (DTU Compute, Asmussens Allé)

  • Bo William Hansen

    (DTU Compute, Asmussens Allé)

  • Henrik Madsen

    (DTU Compute, Asmussens Allé)

  • Erik Lindström

    (DTU Compute, Asmussens Allé)

Abstract

The purpose of dynamic asset allocation (DAA) is to overcome the challenge that changing market conditions present to traditional strategic asset allocation by adjusting portfolio weights to take advantage of favorable conditions and reduce potential drawdowns. This article proposes a new approach to DAA that is based on detection of change points without fitting a model with a fixed number of regimes to the data, without estimating any parameters and without assuming a specific distribution of the data. It is examined whether DAA is most profitable when based on changes in the Chicago Board Options Exchange Volatility Index or change points detected in daily returns of the S&P 500 index. In an asset universe consisting of the S&P 500 index and cash, it is shown that a dynamic strategy based on detected change points significantly improves the Sharpe ratio and reduces the drawdown risk when compared with a static, fixed-weight benchmark.

Suggested Citation

  • Peter Nystrup & Bo William Hansen & Henrik Madsen & Erik Lindström, 2016. "Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 361-374, September.
    • Handle: RePEc:pal:assmgt:v:17:y:2016:i:5:d:10.1057_jam.2016.12
    DOI: 10.1057/jam.2016.12
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    References listed on IDEAS

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    Cited by:

    1. Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
    2. Matúš Maciak & Sebastiano Vitali, 2024. "Using interpolated implied volatility for analysing exogenous market changes," Computational Management Science, Springer, vol. 21(1), pages 1-21, June.
    3. David Hallac & Peter Nystrup & Stephen Boyd, 2019. "Greedy Gaussian segmentation of multivariate time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 727-751, September.
    4. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach," Papers 2402.05272, arXiv.org, revised Sep 2024.
    5. Peter Nystrup & Henrik Madsen & Erik Lindström, 2018. "Dynamic portfolio optimization across hidden market regimes," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 83-95, January.

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