NONPARAMETRIC KERNEL ESTIMATION OF MULTIPLE HEDGE RATIOS

It is possible for the traditional hedge ratio estimation to produce erroneous guidance to risk managers because of the restrictive assumptions. This study adopts nonparametric locally polynomial kernel estimation to exclude the assumptions. Results from the hog complex find that hedge ratios estimated by local polynomial kernel regression outperform naïve and GARCH models. Because of the potential assumption violations associated with the estimation and implementation of hedge ratios by GARCH models, LPK is a reasonable alternative for estimating hedge ratios to manage price risks.


Issue Date:
2000
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/21737
Total Pages:
23
Series Statement:
Selected Paper




 Record created 2017-04-01, last modified 2017-08-24

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