Quantile forecasts using the Realized GARCH-EVT approach Samit Paul Department of Finance and Control, Indian Institute of Management Calcutta, Calcutta, India, and Prateek Sharma Department of Finance and Accounting, Indian Institute of Management Udaipur, Udaipur, India Abstract Purpose – This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models. Design/methodology/approach – One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model. Findings – In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model. Originality/value – It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market. Keywords Value-at-Risk, Expected shortfall, Extreme value theory, Realized GARCH, Realized kernel, Skewed student-t Paper type Research paper 1. Introduction Quantile measures of financial returns distributions, such as Value-at-Risk (VaR) and expected shortfall (ES), are useful for the computation of regulatory capital requirements for the financial institutions and for risk management applications. The generalized autoregressive conditional heteroskedasticity (GARCH) models are frequently used for estimating conditional moments of the returns distribution, which are subsequently used to forecast conditional quantiles. The standard GARCH model uses squared daily returns as the estimate of the daily conditional variance. But a single daily return contains little information about the current level of volatility, and hence it provides a very noisy estimate of the daily conditional variance (Andersen and Bollerslev, 1998). A better approach is to use the realized variance (RV) Quantile forecasts 481 Received 28 September 2016 Revised 21 November 2016 27 January 2017 2 February 2017 Accepted 6 February 2017 Studies in Economics and Finance Vol. 35 No. 4, 2018 pp. 481-504 © Emerald Publishing Limited 1086-7376 DOI 10.1108/SEF-09-2016-0236 The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1086-7376.htm