Title: Robust quantile time series in financial time series models
Authors: Valderio Anselmo Reisen - DEST-CCE-UFES (Brazil) [presenting]
Pascal Bondon - CentraleSupelec (France)
Ian Danilevicz - CentraleSupelec (France)
Abstract: The quantile regression and the m-regression methods which are widely used for time-independent data are invoked. We propose a robust quantile estimator for short and long memory time series, as frequently found in financial data. Asymptotic results of the estimator are established for Gaussian time series. Monte Carlo simulations illustrate the proposed methodology's performance under different scenarios of time series with additive outliers and asymmetric errors. As an application, the method is used to model the S\&P 500 index. As an additional contribution, the methodology is introduced in mixed models with time series covariates. In this context, a real data set collected in the Greater Vit\'oria area, Brazil, is analyzed to quantify the impact of the particular matter (PM2.5) levels on the health of children with asthma problems.