|Appears in Collections:||Accounting and Finance Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models|
|Author(s):||Kambouroudis, Dimos S|
|Citation:||Kambouroudis DS, McMillan D & Tsakou K (2016) Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models, Journal of Futures Markets, 36 (12), pp. 1127-1163.|
|Abstract:||We investigate the information content of implied volatility forecasts for stock index return volatility. Using different autoregressive models, we examine whether implied volatility forecasts contain information for future volatility beyond that in GARCH and realized volatility models. Results show implied volatility follows a predictable pattern and confirm the existence of a contemporaneous relationship between implied volatility and index returns. Individually, implied volatility performs worse than alternate forecasts, however, a model that combines an asymmetric GARCH model with implied and realized volatility through (asymmetric) ARMA models is preferred model for forecasting volatility. This evidence is further supported by consideration of value-at-risk.|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is the peer reviewed version of the following article: Kambouroudis, D. S., McMillan, D. G. and Tsakou, K. (2016), Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models. Journal of Futures Markets, 36: 1127–1163. doi:10.1002/fut.21783, which has been published in final form at https://doi.org/10.1002/fut.21783. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.|
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