Evaluating VaR and ES for commodities - both conventionally and with neural networks

Sammanfattning: As commodities are becoming more popular and accessible assets for speculative and hedging purposes, the limited research regarding risk management for said asset-class justifies further contribution to the deficient output. Many previous studies have highlighted the extraordinary high volatility, with non-linear and clustering characteristics associated with commodities. Hence, incorporating volatility forecasts in risk anagement seems warranted. As the standard risk measurements for market risk in the last decades have been Value at Risk (VaR) and Expected Shortfall (ES), these metrics are evaluated based on a Volatility Weighted Historical Simulation with volatility forecasts provided by a GARCH(1,1) approach and a Recurrent Neural Network (LSTM) approach for oil, gold and soybean. The data period spans from 1990 to 2019 and the results indicate that both approaches work remarkably well in estimating both VaR and ES. In general, the GARCH(1,1) approach displays somewhat more accurate VaR estimates according to Kupiecs test. However, The LSTM approach does spread the violations more adequate according to Christoffersen’s test. Both approaches display very well specified ES estimates, with somewhat better test statistics for the GARCH(1,1) approach according to the ”Testing ES directly”-test proposed by Acerbi & Szekely.