FORECASTING DAILY VOLATILITY USING RANGE-BASED DATA

Users of agricultural markets frequently need to establish accurate representations of expected future volatility. The fact that range-based volatility estimators are highly efficient has been acknowledged in the literature. However, it is not clear whether using range-based data leads to better risk management decisions. This paper compares the performance of GARCH models, range-based GARCH models, and log-range based ARMA models in terms of their forecasting abilities. The realized volatility will be used as the forecasting evaluation criteria. The conclusion helps establish an efficient forecasting framework for volatility models.


Subject(s):
Issue Date:
2004
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/20377
Total Pages:
25
Series Statement:
Selected Paper




 Record created 2017-04-01, last modified 2017-04-26

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