TY - JOUR
T1 - Asymmetric autoregressive models
T2 - statistical aspects and a financial application under COVID-19 pandemic
AU - Liu, Yonghui
AU - Mao, Chaoxuan
AU - Leiva, Víctor
AU - Liu, Shuangzhe
AU - Silva Neto, Waldemiro A.
N1 - Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
Funding Information:
The research of Y. Liu was supported by the Natural Science Foundation of China [grant number 11271259]. The research of V. Leiva was partially supported by the National Agency for Research and Development (ANID) of the Chilean government [grant number FONDECYT 1200525]. The authors thank the editors and reviewers for their constructive comments on an earlier version of this manuscript.
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using the local influence technique with four perturbation schemes. By employing Monte Carlo simulations, we evaluate the statistical behavior of the corresponding estimators and of the local influence technique. An illustration with financial data updated until 2020, analyzed using the methodology introduced in the present work, is presented as an example of effective applications, from where it is possible to explain atypical cases from the COVID-19 pandemic.
AB - In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using the local influence technique with four perturbation schemes. By employing Monte Carlo simulations, we evaluate the statistical behavior of the corresponding estimators and of the local influence technique. An illustration with financial data updated until 2020, analyzed using the methodology introduced in the present work, is presented as an example of effective applications, from where it is possible to explain atypical cases from the COVID-19 pandemic.
KW - Expectation-maximization algorithm
KW - Monte Carlo simulation
KW - local influence
KW - maximum likelihood methods
KW - non-normality
KW - times-series models
UR - http://www.scopus.com/inward/record.url?scp=85104894604&partnerID=8YFLogxK
U2 - 10.1080/02664763.2021.1913103
DO - 10.1080/02664763.2021.1913103
M3 - Review article
SN - 0266-4763
VL - 49
SP - 1323
EP - 1347
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 5
ER -