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Time-Varying Factor Model Components for Effective Momentum Strategy

Author

Listed:
  • Jamie Cross

    (Melbourne Business School)

  • Lennart Hoogerheide

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Herman van Dijk

    (Erasmus University Rotterdam and Tinbergen Institute)

Abstract

Determining a plausible number of components in a factor model is a nontrivial issue in case of weak data, sparse model restrictions and diffuse prior information. We discuss the issue of structural parametric identification in a static factor model and introduce orthogonal restrictions which imply that inference is independent of the order of the dependent variables. Given that financial and economic relations vary over time, we propose the use of predictive likelihoods in combination with moving window estimation in order to determine a plausible time-varying number of factor model components. Results are presented on a residual momentum strategy based on a time-varying latent factor model which outperforms a standard momentum strategy using a portfolio of US industrial stocks.

Suggested Citation

  • Jamie Cross & Lennart Hoogerheide & Herman van Dijk, 2024. "Time-Varying Factor Model Components for Effective Momentum Strategy," Tinbergen Institute Discussion Papers 24-068/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20240068
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    References listed on IDEAS

    as
    1. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
    2. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2017. "Bayesian analysis of boundary and near-boundary evidence in econometric models with reduced rank," Working Paper 2017/11, Norges Bank.
    3. Knut Are Aastveit & Jamie Cross & Francesco Furlanetto & Herman K. Van Dijk, 2024. "Taylor Rules with Endogenous Regimes," Tinbergen Institute Discussion Papers 24-030/III, Tinbergen Institute.
    4. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023. "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2023. "Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 523-537, April.
    6. Tobias J. Moskowitz & Mark Grinblatt, 1999. "Do Industries Explain Momentum?," Journal of Finance, American Finance Association, vol. 54(4), pages 1249-1290, August.
    7. Ardia, David & Baştürk, Nalan & Hoogerheide, Lennart & van Dijk, Herman K., 2012. "A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3398-3414.
    8. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    9. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
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