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A conditional autoregressive range model with gamma distribution for financial volatility modelling

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  • Xie, Haibin
  • Wu, Xinyu

Abstract

The commonly used conditional autoregressive range model with Weibull distribution (henceforth WCARR) suffers from serious inlier problem. We conjecture that this problem is due to a misspecified distribution to the disturbance, and propose a conditional autoregressive range model with gamma distribution (henceforth GCARR) to model the volatility of financial assets. In this paper, we first discuss the theoretical properties of the GCARR model and then compare its empirical performance with the WCARR. Empirical studies are performed on a broad set of stock indices in different countries over different time horizons. Consistent with the conjecture, we find that the GCARR model can reduce not only the inlier problem but also the outlier problem of the WCARR model. The results indicate that our GCARR model describes the dynamics of the range-based volatility better than the WCARR model and thus serves as a better benchmark.

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  • Xie, Haibin & Wu, Xinyu, 2017. "A conditional autoregressive range model with gamma distribution for financial volatility modelling," Economic Modelling, Elsevier, vol. 64(C), pages 349-356.
    • Handle: RePEc:eee:ecmode:v:64:y:2017:i:c:p:349-356
    DOI: 10.1016/j.econmod.2017.04.001
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    Cited by:

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    3. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
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    6. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

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