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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