Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall

Chao Wang; Richard Gerlach

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arXiv · 1906.09961

Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall

Chao Wang·Richard Gerlach

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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.

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Chao Wang, Richard Gerlach. 2019-06-21. Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall. https://arxiv.org/abs/1906.09961

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