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Comovements and asymmetric tail dependence in state housing prices in the USA: A nonparametric approach

Author

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  • Haitao Huang
  • Liang Peng
  • Vincent W. Yao

Abstract

We reexamine the methods used in estimating comovements among US regional home prices and find that there are insufficient moments to ensure a normal limit necessary for employing the quasi‐maximum likelihood estimator. Hence we propose applying the self‐weighted quasi‐maximum exponential likelihood estimator and a bootstrap method to test and account for the asymmetry of comovements as well as different magnitudes across state pairs. Our results reveal interstate asymmetric tail dependence based on observed house price indices rather than residuals from fitting autoregressive–generalized autoregressive conditional heteroskedasticity (AR‐GARCH) models.

Suggested Citation

  • Haitao Huang & Liang Peng & Vincent W. Yao, 2019. "Comovements and asymmetric tail dependence in state housing prices in the USA: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 843-849, August.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:5:p:843-849
    DOI: 10.1002/jae.2703
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    Cited by:

    1. Mo Zhou & Liang Peng & Rongmao Zhang, 2021. "Empirical likelihood test for the application of swqmele in fitting an arma‐garch model," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 222-239, March.

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