报告地点：精正楼306

报告人：朱倩倩 副教授（上海财经大学）

报告摘要：This paper proposes the asymmetric linear double autoregression, which jointly models the conditional mean and conditional heteroscedasticity characterized by asymmetric effects. A sufficient condition is established for the existence of a strictly stationary solution. With a quasi-maximum likelihood estimation (QMLE) procedure introduced, a Bayesian information criterion (BIC) and its modified version are proposed for model selection. To detect asymmetric effects in the volatility, the Wald, Lagrange multiplier and quasi-likelihood ratio test statistics are put forward, and their limiting distributions are established under both null and local alternative hypotheses. Moreover, a mixed portmanteau test is constructed to check the adequacy of the fitted model. All asymptotic properties of inference tools including QMLE, BICs, asymmetric tests and the mixed portmanteau test, are established without any moment condition on the data process, which makes the new model and its inference tools applicable for heavy-tailed data. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical application to S&P500 Index illustrates the usefulness of the new model.

报告人介绍：

  朱倩倩，上海财经大学统计与管理学院副教授，博士生导师。2017年在香港大学取得博士学位并加入上海财经大学。主要研究方向为时间序列分析，研究成果主要发表在《Journal of the Royal Statistical Society, Series B》、《Journal of Econometrics》、《Econometric Theory》、《Statistica Sinica》及《Journal of Business & Economic Statistics》等国际权威期刊上。主持国家自然科学基金青年项目、上海市“浦江人才计划”团队项目和“晨光计划”项目。