Tail risk of coal futures in China's market

Abstract

AbstractThis study evaluates the downside tail risk of coal futures contracts (coke, coking coal and thermal coal) traded in the Chinese market between 2011 and 2021, measured by value at risk (VaR). We examine the one‐day‐ahead VaR forecasting performance with a hybrid econometric and deep learning model (GARCH‐LSTM), GARCH family models, extreme value theory models, quantile regression models and two naïve models (historical simulation and exponentially weighted moving average). We use four backtesting techniques and the model confidence set to identify the optimal models. The results suggest that the models focusing on tail risk or utilising long short‐term memory generate more effective risk management.