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Bayesian Expected Shortfall Forecasting Incorporating the Intraday Range

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  • Richard Gerlach
  • Cathy W. S. Chen

Abstract

Intraday sources of data have proved to be effective for dynamic volatility and tail risk estimation. Expected shortfall (ES) is a tail risk measure, which is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to implicitly model ES, is generalized to incorporate information on the intraday range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to direct estimation and one-step-ahead forecasts of expectiles and, subsequently, of ES. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favorably with a large range of competitors in an empirical study forecasting seven financial return series over a 10-year period.

Suggested Citation

  • Richard Gerlach & Cathy W. S. Chen, 2016. "Bayesian Expected Shortfall Forecasting Incorporating the Intraday Range," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 128-158.
    • Handle: RePEc:oup:jfinec:v:14:y:2016:i:1:p:128-158.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbu022
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    Citations

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    Cited by:

    1. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    2. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    3. Kim, Dongwhan & Kang, Kyu Ho, 2021. "Conditional value-at-risk forecasts of an optimal foreign currency portfolio," International Journal of Forecasting, Elsevier, vol. 37(2), pages 838-861.
    4. Cathy W. S. Chen & Edward M. H. Lin & Tara F. J. Huang, 2022. "Bayesian quantile forecasting via the realized hysteretic GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1317-1337, November.
    5. Cathy W.S. Chen & Toshiaki Watanabe, 2019. "Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 747-765, May.
    6. Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org, revised Jan 2021.
    7. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    8. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    9. Richard Gerlach & Chao Wang, 2018. "Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures," Papers 1805.08653, arXiv.org.

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