Journal of Risk
ISSN:
1465-1211 (print)
1755-2842 (online)
Editor-in-chief: Farid AitSahlia
Range-based volatility forecasting: a multiplicative component conditional autoregressive range model
Need to know
- We propose a multiplicative component conditional autoregressive range (MCCARR) model to capture the "long-memory" effect in volatility.
- We show both theoretically and empirically that the MCCARR model can well capture the "long-memory" effect.
- An empirical study performed on the S&P 500 index shows that the MCCARR model outperforms not only the CARR model but also the HAR model.
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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Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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