Tail risk forecasting of realized volatility CAViaR models
Finance Research Letters
; : 103326, 2022.
Article
in English
| ScienceDirect | ID: covidwho-2031285
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Type of study:
Prognostic study
Language:
English
Journal:
Finance Research Letters
Year:
2022
Document Type:
Article
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