Érudit | Dépôt de documents >
CIRANO - Centre interuniversitaire de recherche en analyse des organisations >
Cahiers scientifiques >

Please use this identifier to cite or link to this item:

Title: Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies
Authors: Ghysels, Eric
Santa-Clara, Pedro
Valkanov, Rossen
Issue Date: 2004-05
Publisher: Centre interuniversitaire de recherche en analyse des organisations (CIRANO)
Series/Report no.: Série scientifique (CIRANO);2004s-19
Scientific series (CIRANO);2004s-19
Abstract: Nous utilisons les régressions MIDAS (Mixed Data Sampling) dans le contexte de prévision de volatilité mesurée par incréments de la variation quadratique. Nous trouvons que la 'realized power' (Barndorff-Nielsen and Shephard) est le meilleur régresseur pour prévoir la variation quadratique future.

We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sufficient to capture the persistence in volatility. These findings hold both in- and out-of-sample.
ISSN: 1198-8177
Appears in Collections:Cahiers scientifiques

Files in This Item:

2004s-19.pdf (Adobe PDF ; 461.16 kB)

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.


About Érudit | Subscriptions | RSS | Terms of Use | Contact us |

Consortium Érudit ©  2016