Using Conditional Autoregressive Range Model to Forecast Volatility of the Stock Indices
- https://doi.org/10.2991/jcis.2006.175How to use a DOI?
- CARR, GARCH, Range, Volatility, Leverage Effect.
This article compares the forecasting performance of the conditional autoregressive range (CARR) model with the commonly adopted GARCH model. Two major stock indices, FTSE 100 and Nikkei 225, are studies using the daily range data and daily close price data over the period 1990 to 2000. Our results suggest that improvements of the overall estimation are achieved when the CARR models are used. Moreover, we find that the CARR model gives better volatility forecasts than GARCH, as it can catch the extra informational contents of the intra-daily price variations. Finally, we also find that the inclusion of the lagged return and the lagged trading volume can significantly improve the forecasting ability of the CARR models. Our empirical results also significantly suggest the existence of a leverage effect in the U.K. and Japanese stock markets.
- © 2006, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Heng-Chih Chou AU - David Wang PY - 2006/10 DA - 2006/10 TI - Using Conditional Autoregressive Range Model to Forecast Volatility of the Stock Indices BT - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06) PB - Atlantis Press SP - 592 EP - 595 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.175 DO - https://doi.org/10.2991/jcis.2006.175 ID - Chou2006/10 ER -