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Nonlinear Least Squares Estimation of Log-ACD Models

  • Zhao Chen
  • Wei Liu
  • Christina Dan Wang
  • Wu-qing Wu
  • Yao-hua Wu
Article
  • 5 Downloads

Abstract

This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.

Keywords

Log-ACD model nonlinear least squares estimation Log-GARCH model heavy-tail 

2000 MR Subject Classification

62M10 

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Copyright information

© Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Zhao Chen
    • 1
  • Wei Liu
    • 2
  • Christina Dan Wang
    • 3
  • Wu-qing Wu
    • 4
  • Yao-hua Wu
    • 5
  1. 1.School of Data ScienceFudan UniversityShanghaiChina
  2. 2.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  3. 3.New York University ShanghaiShanghaiChina
  4. 4.School of BusinessRenmin University of ChinaBeijingChina
  5. 5.Department of Statistics and FinanceUniversity of Science and Technology of ChinaHefeiChina

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