The current work undertakes an overview of the forecasting volatility with high frequency data topic, attempting to answer to the fundamental latency problem of return volatility. It surveys the most relevant aspects of the volatility topic, suggesting advantages and disadvantages of each alternative in modeling. It reviews the concept of realized volatility and explains why forecasting of volatility is more effective when the model contains a measure of intraday data. A discrete and a continuous time model are defined. Sampling methods at different frequencies are reviewed, and the impact of microstructure noise is considered. Details on procedures employed in the literature with respect to modeling and forecasting using realized models are discussed, while an empirical exercise will prove the advantages of using measures of high frequency data.

Item Type:	Refereed Article
Keywords:	High frequency, Volatility, Modeling, Forecasting, Realized measures, Microstructure noise
Research Division:	Commerce, Management, Tourism and Services
Research Group:	Banking, finance and investment
Research Field:	Financial econometrics
Objective Division:	Economic Framework
Objective Group:	Measurement standards and calibration services
Objective Field:	Measurement standards and calibration services not elsewhere classified
UTAS Author:	Matei, M (Dr Marius Matei)
ID Code:	99869
Year Published:	2011
Web of Science® Times Cited:	1
Deposited By:	Economics and Finance
Deposited On:	2015-04-14
Last Modified:	2015-05-11
Downloads:	0