CMStatistics 2022: Start Registration
View Submission - CFE
Title: Directional predictability tests Authors:  Carlos Velasco - Universidad Carlos III de Madrid (Spain) [presenting]
Weifeng Jin - Universidad Carlos III de Madrid (Spain)
Abstract: New tests of predictability for non-Gaussian sequences are proposed that may display general nonlinear dependence in higher-order properties. We test the null of martingale difference against parametric alternatives, which can introduce linear or nonlinear dependence as generated by ARMA and all-pass restricted ARMA models, respectively. We also develop tests to check for linear predictability under the white noise null hypothesis parameterized by an all-pass model driven by martingale difference innovations and tests of non-linear predictability on ARMA residuals. Our Lagrange Multiplier tests are developed from a loss function based on pairwise dependence measures that identify the predictability of levels. We provide asymptotic and finite sample analysis of the properties of the new tests and investigate the predictability of different series of financial returns.