A0197
Title: CAViaR models for value at risk and expected shortfall with long range dependency features
Authors: Gelly Mitrodima - LSE (United Kingdom) [presenting]
Jaideep Oberoi - SOAS University of London (United Kingdom)
Abstract: Alternative specifications of conditional autoregressive quantile models are considered to estimate value-at-risk (VaR) and expected shortfall (ES). The proposed specifications include a slow-moving component in the quantile process and aggregate returns from heterogeneous horizons as regressors. Using data for ten stock indices over a period that incorporated the COVID-19 pandemic, the performance of the models is evaluated, and the proposed valuable features are found to capture tail dynamics better.
