Using Conditional Autoregressive Range Model to Forecast Volatility of the Stock Indices

DOI

10.2991/jcis.2006.175How to use a DOI?

Keywords

CARR, GARCH, Range, Volatility, Leverage Effect.

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/jcis.2006.175
ID  - Chou2006/10
ER  -