Premium
Time‐varying dynamics of expected shortfall in commodity futures markets
Author(s) -
Mehlitz Julia S.,
Auer Benjamin R.
Publication year - 2021
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22196
Subject(s) - futures contract , estimator , econometrics , economics , commodity , margin (machine learning) , financial economics , computer science , finance , statistics , mathematics , machine learning
Motivated by the growing interest of investors in commodities and by advances in risk measurement, we present a full‐scale analysis of expected shortfall (ES) in commodity futures markets. Besides illustrating the dynamics of historic ES, we evaluate whether popular estimators are suitable for forecasting future ES. By implementing a new backtest, we find that the performance of estimators hinges on market stability. Estimators tend to fail when markets are in turmoil and accurate forecasts are urgently needed. Even though a kernel method performs best on average, our results advise against the use of established estimators for risk (and margin) prediction.