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Range-based volatility forecasting: an extended conditional autoregressive range model

Haibin Xie and Xinyu Wu

  • An extended conditional autoregressive range (EXCARR) model is proposed to describe the conditional mean of the high-low price range.
  • The EXCARR model not only takes the CARR model as a special case but also captures the asymmetric behaviors between the upward range and the downward range.
  • Empirical results demonstrate the superiority of EXCARR over both the CARR model and the asymmetric CARR model.
  • The outperformance of EXCARR increases with the asymmetry between the upward range and the downward range. 

This paper proposes an extended conditional autoregressive range (EXCARR) model to describe the range-based volatility dynamics of financial assets. Our EXCARR model not only takes the conditional autoregressive range (CARR) model as a special case but also considers the asymmetry between the upward range and the downward range. Empirical studies performed on a variety of stock indexes show that the EXCARR model outperforms not only the CARR model but also the asymmetric CARR (ACARR) model in both in-sample and out-of-sample forecasting. Hence, our EXCARR model provides a new benchmark for range-based volatility forecasting.

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