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A Quantile Regression Approach to Estimate the Variance of Financial Returns

Author

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  • Dirk G Baur
  • Thomas Dimpfl

Abstract

We propose to estimate the conditional variance of a time series of financial returns through a quantile autoregressive (AR) model and demonstrate that it contains all information commonly captured in two separate equations for the mean and variance of a generalized AR conditional heteroscedasticity-type model. We show that the inter-quantile range spanned by conditional quantile estimates identifies the asymmetric response of volatility to lagged returns, resulting in wider conditional densities for negative returns than for positive returns. Finally, we estimate the conditional variance based on the estimated conditional density and illustrate its accuracy in a forecast evaluation.

Suggested Citation

  • Dirk G Baur & Thomas Dimpfl, 2019. "A Quantile Regression Approach to Estimate the Variance of Financial Returns," Journal of Financial Econometrics, Oxford University Press, vol. 17(4), pages 616-644.
  • Handle: RePEc:oup:jfinec:v:17:y:2019:i:4:p:616-644.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nby026
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    Citations

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    Cited by:

    1. Libo Yin & Jing Nie & Liyan Han, 2021. "Intermediary capital risk and commodity futures volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 577-640, May.

    More about this item

    Keywords

    asymmetric volatility; inter-quantile range; quantile autoregression; variance estimation;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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