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

Haibin Xie

Journal of Risk

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.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-risk/7554106/range ... gressive-range-model (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ4:7554106

Access Statistics for this article

More articles in Journal of Risk from Journal of Risk
Bibliographic data for series maintained by Thomas Paine ().