Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall
Abstract
A joint conditional autoregressive expectile and Expected Shortfall framework is proposed. The framework is extended through incorporating a measurement equation which models the contemporaneous dependence between the realized measures and the latent conditional expectile. Nonlinear threshold specification is further incorporated into the proposed framework. A Bayesian Markov Chain Monte Carlo method is adapted for estimation, whose properties are assessed and compared with maximum likelihood via a simulation study. One-day-ahead VaR and ES forecasting studies, with seven market indices, provide empirical support to the proposed models.
Cite
@article{arxiv.1906.09961,
title = {Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall},
author = {Chao Wang and Richard Gerlach},
journal= {arXiv preprint arXiv:1906.09961},
year = {2019}
}
Comments
41 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1805.08653, arXiv:1807.02422, arXiv:1612.08488