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Forecast Evaluation of Explanatory Models of Financial Variability [Dataset]

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Forecast Evaluation of Explanatory Models of Financial Variability [Dataset]
 
Identifier https://doi.org/10.7910/DVN/84EP9B
 
Creator Genaro Sucarrat
 
Publisher Harvard Dataverse
 
Description A practice that has become widespread and widely endorsed is that of evaluating
forecasts of financial variability obtained from discrete time models by comparing
them with high-frequency ex post estimates (e.g. realised volatility) based on continuous
time theory. In explanatory financial variability modelling this raises several
methodological and practical issues, which suggests an alternative approach is needed.
The contribution of this study is twofold. First, the finite sample properties of
operational and practical procedures for the forecast evaluation of exp
lanatory discrete
time models of financial variability are studied. Second, based on the simulation results
a simple but general framework is proposed and illustrated. The illustration provides an
example of where an explanatory model outperforms realised volatility ex post.
 
Subject Financial variability
Financial volatility
Forecasting
Explanatory modelling
Exchange rates
 
Date 2009
 
Type aggregate data