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Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall

Chao Wang, Richard Gerlach

arXiv 21 Jun 2019 · Finance — Risk Management · 2 citations (OpenAlex)

arXiv:1906.09961 · PDF · DOI · OpenAlex · Extracted main text

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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