Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures

TL;DR

This paper introduces a semi-parametric model for tail risk forecasting that incorporates realized measures, demonstrating improved accuracy over traditional models through Bayesian estimation and extensive empirical testing on financial data.

Contribution

It extends the joint VaR and ES quantile regression model by integrating realized measures and employs Bayesian methods for estimation, enhancing tail risk forecast accuracy.

Findings

Bayesian approach outperforms maximum likelihood estimation.

Models with realized measures outperform traditional GARCH models.

Incorporating sub-sampled realized variance improves forecast accuracy.

Abstract

The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2017) is extended via incorporating a realized measure, to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte Carlo method are employed for estimation, whose properties are assessed and compared via a simulation study; results favour the Bayesian approach, which is subsequently employed in a forecasting study of seven market indices and two individual assets. The proposed models are compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH and the joint VaR and ES quantile regression models in Taylor (2017). The comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected Shortfall forecasts, over a long forecast sample period that includes the global financial crisis in 2007-2008. The results favor the proposed models incorporating a realized measure, especially when employing the sub-sampled Realized Variance and the sub-sampled Realized Range.