Abstract
In this paper, the problem of the nonparametric estimation of the expectile regression model for strong mixing functional time series data is investigated. To be more precise, we establish the almost complete consistency and the asymptotic normality of the kernel-type expectile regression estimator under some mild conditions. The usefulness of our theoretical results in the financial time series analysis is discussed. Further, we provide some practical algorithms to select the smoothing parameter or to construct the confidence intervals using the bootstrap techniques. In addition, a simulation study is carried out to verify the small sample behaviour of the proposed approach. Finally, we give an empirical example using the daily returns of the stock index SP500.