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Range-based volatility forecasting: a multiplicative component conditional autoregressive range model

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  • Haibin Xie

Abstract

To capture the "long-memory" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the long-memory effect well. An empirical study is performed on the Standard & Poor's 500 index, and the results show that the MCCARR model outperforms both conditional autoregressive range and hheterogeneous autoregressive models for in-sample and out-of-sample volatility forecasting.

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  • Haibin Xie, . "Range-based volatility forecasting: a multiplicative component conditional autoregressive range model," Journal of Risk, Journal of Risk.
    • Handle: RePEc:rsk:journ4:7554106
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