Speaker：Deyuan Li(Fudan University)

Time：2019-04-08, 16:00

Location：Conference Room 108 at Experiment Building at Haiyun Campus

Abstract: A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients for the conditional quantile functions, establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study the analysis of U.S. retail gasoline price.