Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures

Richard Gerlach and Chao Wang

International Journal of Forecasting, 2020, vol. 36, issue 2, 489-506

Abstract: This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.

Keywords: Realized variance; Realized range; Semi-parametric; Markov chain Monte Carlo; Value-at-Risk; Expected shortfall (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:489-506

DOI: 10.1016/j.ijforecast.2019.07.003

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