Markov Switching Quantile Regression with Unknown Quantile Using a Generalized Class of Skewed Distributions: Evidence from the U.S. Technology Stock Market

Abstract

© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. The traditional Markov Switching quantile regression with unknown quantile (MS–QRU) relies on the Asymmetric Laplace Distribution (ALD). However, the old fashion ALD displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis. This study compares ALD with two alternative skewed likelihood types for the estimation of MS–QRU, including the skew-normal distribution (SKN) and skew-student-t distribution (SKT). For all the three skewed distribution-based models, we estimated parameter by the Bayesian approach. Finally, we apply our models to investigate the beta risk of individual FAANG technology stock under the CAPM framework. The model selection results show that our alternative skewed distribution performs better than the ALD. Only for two out of five stocks suggest that ALD is more appropriate for MS–QRU model.