A1363
Title: Regime-specific return predictability in quantiles
Authors: Xinling Xie - Southwestern University of Finance of Economics (China) [presenting]
Abstract: Regime switching mechanisms in stock return prediction are captured via predictive quantile regression with multiple thresholds. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and varies across quantiles, while predictors may possess heterogeneous degrees of persistence. Statistical learning techniques, including sequential estimation, adaptive group Lasso refinement, and backward elimination algorithms, are employed to estimate the unknown multiple thresholds. The adaptive Lasso is then applied to identify important predictors in each regime to improve prediction accuracy at each quantile. Asymptotic properties of the proposed estimators are established and corroborated with their finite sample performance revealed in simulations. Empirical analysis of U.S. stock returns demonstrates that the return predictability of several predictors changes with economic states across quantiles, revealing regime-specific return predictability in quantiles.
