Abstract
We propose a new estimation scheme for estimation of the volatility parameters of a semimartingale with jumps based on a jump detection filter. Our filter uses all of the data to analyze the relative size of increments and to discriminate jumps more precisely. We construct quasi-maximum likelihood estimators and quasi-Bayesian estimators and show limit theorems for them including \(L^p\)-estimates of the error and asymptotic mixed normality based on the framework of the quasi-likelihood analysis. The global jump filters do not need a restrictive condition for the distribution of the small jumps. By numerical simulation, we show that our “global” method obtains better estimates of the volatility parameter than the previous “local” methods.
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This work was in part supported by CREST JPMJCR14D7 Japan Science and Technology Agency; Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research No. 17H01702 (Scientific Research); and a Cooperative Research Program of the Institute of Statistical Mathematics.
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Inatsugu, H., Yoshida, N. Global jump filters and quasi-likelihood analysis for volatility. Ann Inst Stat Math 73, 555–598 (2021). https://doi.org/10.1007/s10463-020-00768-x
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DOI: https://doi.org/10.1007/s10463-020-00768-x