TY - JOUR
T1 - Threshold selection in jump-discriminant filter for discretely observed jump processes
AU - Shimizu, Yasutaka
N1 - Funding Information:
Acknowledgments The author expresses his sincere thanks to a referee for his/her accurate comments and instructions with the careful reading, which substantially improved the paper. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 2009, no. 21740073; Japan Science and Technology Agency, CREST; Cooperative Research Meetings, the Institute of Statistical Mathematics: no. 21-2053–21-2055.
PY - 2010
Y1 - 2010
N2 - Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is used to infer the continuous part and the jump part separately. Although there are several choices for the filter, statistics constructed via filters are often sensitive to the choice. This paper presents some numerical procedures for selecting a suitable filter based on observations.
AB - Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is used to infer the continuous part and the jump part separately. Although there are several choices for the filter, statistics constructed via filters are often sensitive to the choice. This paper presents some numerical procedures for selecting a suitable filter based on observations.
KW - Asymptotic unbiasedness
KW - Integrated-volatility
KW - Jump-discriminant filter
KW - Plug-in method
KW - Threshold estimation
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U2 - 10.1007/s10260-010-0134-z
DO - 10.1007/s10260-010-0134-z
M3 - Article
AN - SCOPUS:77955091692
SN - 1618-2510
VL - 19
SP - 355
EP - 378
JO - Statistical Methods and Applications
JF - Statistical Methods and Applications
IS - 3
ER -