Realized Tail-Risk Conditional Autoregressive Expectile Models: Accurate and Stable Forecasting Performance
Abstract
A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES). Realized tail measures such as the realized quantile and the realized ES, constructed from intraday high-frequency returns, are incorporated into the dynamics and measurement equations of the conditional expectile, allowing information about lower-tail risk to enter the forecasts. The parameters are estimated by Markov chain Monte Carlo, and we evaluate the resulting VaR-ES forecasts in numerical examples and in an empirical application using U.S. stock-return data. Across the considered tail levels, the RTCARE models deliver competitive and often superior forecasting performance relative to existing alternatives, with stable results across Monte Carlo replications and rolling forecast evaluations.
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BibTeX
@misc{sasaki2026realized,
title = {{Realized Tail-Risk Conditional Autoregressive Expectile Models: Accurate and Stable Forecasting Performance}},
author = {Keisuke Sasaki and Yasuhiro Omori},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13446},
eprint = {2610.13446},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13446},
}
RIS
TY - UNPB
TI - Realized Tail-Risk Conditional Autoregressive Expectile Models: Accurate and Stable Forecasting Performance
AU - Keisuke Sasaki
AU - Yasuhiro Omori
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13446
UR - https://arxiv.org/abs/2610.13446
AB - A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES). Realized tail measures such as the realized quantile and the realized ES, constructed from intraday high-frequency returns, are incorporated into the dynamics and measurement equations of the conditional expectile, allowing information about lower-tail risk to enter the forecasts. The parameters are estimated by Markov chain Monte Carlo, and we evaluate the resulting VaR-ES forecasts in numerical examples and in an empirical application using U.S. stock-return data. Across the considered tail levels, the RTCARE models deliver competitive and often superior forecasting performance relative to existing alternatives, with stable results across Monte Carlo replications and rolling forecast evaluations.
ER -