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A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures

Author

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  • Chao Wang
  • Richard Gerlach
  • Qian Chen

Abstract

A class of realized semi-parametric conditional autoregressive joint Value-at-Risk (VaR) and Expected Shortfall (ES) models is proposed. This class includes novel specifications that allow separate dynamics for VaR and ES, driven by realized measures of volatility. A measurement equation is included in the model class for risk modeling, meaning it generalizes the parametric Realized-GARCH model into the semi-parametric realm. The proposed models implicitly allow the conditional return distribution to change over time via the relationship between VaR and ES. Employing a quasi-likelihood built on the asymmetric Laplace distribution, a Bayesian Markov Chain Monte Carlo method is used for model estimation, whose finite sample properties are assessed via simulation. In a forecasting study applied to 7 indices and 7 assets, one-day-ahead 1% and 2.5% VaR and ES forecasting results support the proposed model class.

Suggested Citation

  • Chao Wang & Richard Gerlach & Qian Chen, 2023. "A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures," Quantitative Finance, Taylor & Francis Journals, vol. 23(2), pages 309-334, February.
    • Handle: RePEc:taf:quantf:v:23:y:2023:i:2:p:309-334
    DOI: 10.1080/14697688.2022.2157322
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    Cited by:

    1. Cathy W. S. Chen & Takaaki Koike & Wei-Hsuan Shau, 2024. "Tail risk forecasting with semi-parametric regression models by incorporating overnight information," Papers 2402.07134, arXiv.org.
    2. Rangika Peiris & Chao Wang & Richard Gerlach & Minh-Ngoc Tran, 2024. "Semi-parametric financial risk forecasting incorporating multiple realized measures," Papers 2402.09985, arXiv.org, revised Jun 2024.

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