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Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

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  • Haibin Xie

Abstract

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.

Suggested Citation

  • Haibin Xie, 2019. "Financial volatility modeling: The feedback asymmetric conditional autoregressive range model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 11-28, January.
    • Handle: RePEc:wly:jforec:v:38:y:2019:i:1:p:11-28
    DOI: 10.1002/for.2548
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    Cited by:

    1. Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
    2. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    3. 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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