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

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  • Haibin Xie
  • Xinyu Wu

Abstract

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.

Suggested Citation

  • Haibin Xie & Xinyu Wu, . "Range-based volatility forecasting: an extended conditional autoregressive range model," Journal of Risk, Journal of Risk.
    • Handle: RePEc:rsk:journ4:6310491
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