Tail risk forecasting of realized volatility CAViaR models

Cathy W. S. Chen (), Hsiao-Yun Hsu and Toshiaki Watanabe

Finance Research Letters, 2023, vol. 51, issue C

Abstract: This research proposes a new class of RES-CAViaR (conditional autoregressive value-at-risk) models, that incorporate daily realized volatility and expected shortfall (ES) to forecast VaR and ES simultaneously. We further consider weekly and monthly realized volatilities in the proposed model to approximate a long-memory process. We employ the Bayesian adaptive Markov chain Monte Carlo approach to estimate all unknown parameters and to jointly predict daily VaR and ES over a 4-year out-of-sample period including the COVID-19 pandemic. Our results show that the realized CAViaR-type models outperform in terms of three backtests, four loss-function criteria, and ES measurement at the 1% level.

Keywords: Bayesian MCMC methods; CAViaR model; Expected shortfall; Generalized autoregressive score (GAS) model; Heterogeneous autoregressive (HAR) model; Realized volatility; Value-at-risk (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005050

DOI: 10.1016/j.frl.2022.103326

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