TY - JOUR
T1 - Diagnostic analytics for a GARCH model under skew-normal distributions
AU - Liu, Yonghui
AU - Wang, Jing
AU - Yao, Zhao
AU - Liu, Conan
AU - Liu, Shuangzhe
N1 - Funding Information:
The authors are very grateful to the Editor and the two anonymous reviewers for their constructive comments which led to an improved presentation of the manuscript. The research was supported by the National Social Science Fund of China [grant No. 19BTJ036].
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - In this paper, a generalized autoregressive conditional heteroskedasticity model under skew-normal distributions is studied. A maximum likelihood approach is taken and the parameters in the model are estimated based on the expectation-maximization algorithm. The statistical diagnostics is made through the local influence technique, with the normal curvature and diagnostics results established for the model under four perturbation schemes in identifying possible influential observations. A simulation study is conducted to evaluate the performance of our proposed method and a real-world application is presented as an illustrative example.
AB - In this paper, a generalized autoregressive conditional heteroskedasticity model under skew-normal distributions is studied. A maximum likelihood approach is taken and the parameters in the model are estimated based on the expectation-maximization algorithm. The statistical diagnostics is made through the local influence technique, with the normal curvature and diagnostics results established for the model under four perturbation schemes in identifying possible influential observations. A simulation study is conducted to evaluate the performance of our proposed method and a real-world application is presented as an illustrative example.
KW - Expectation-maximization algorithm
KW - GARCH model
KW - local influence technique
KW - maximum likelihood estimation
KW - Monte Carlo simulation
KW - skew-normal distribution
UR - http://www.scopus.com/inward/record.url?scp=85145050198&partnerID=8YFLogxK
U2 - 10.1080/03610918.2022.2157015
DO - 10.1080/03610918.2022.2157015
M3 - Article
AN - SCOPUS:85145050198
SN - 0361-0918
VL - 53
SP - 4850
EP - 4874
JO - Communications in Statistics: Simulation and Computation
JF - Communications in Statistics: Simulation and Computation
IS - 10
ER -