Angelica Gianfreda
Free University of Bozen/Bolzano, Universitätsplatz 1, (39100) Bozen (Italy)
Giacomo Scandolo
University of Florence, Via delle Pandette 32, (50127) Firenze (Italy)

​​2nd International Scientific Conference – EMAN 2018 – Economics and Management: How to Cope With Disrupted Times, Ljubljana – Slovenia, March 22, 2018, CONFERENCE PROCEEDINGS published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; Faculty of Management Koper, Slovenia; Doba Business School – Maribor, Slovenia; Integrated Business Faculty –  Skopje, Macedonia; Faculty of Management – Zajecar, Serbia, ISBN 978-86-80194-11-0


Model risk has an important effect on risk measurements. Indeed, the choice of the underlying probabilistic model can have a significant impact on the risk forecast. The hazard of producing poor risk assessments due to the choice of an unsuited model is known as “model risk”. Its detection and quantification are crucial tasks, particularly with energy commodities which require more complex modelling compared to the ones needed in traditional financial markets. Using a normalized measure of model risk for the forecast of daily Value-at-Risk, we focus on a restricted set of plausible models within the GARCH-type class specified with nine different distributions. In this way, we are able to provide a more reliable assessment of model risk for two energy commodities (natural gas and crude oil) over the years from 2001 to 2015.  

Key words

Relative Measure of Model Risk, VaR, GARCH models, One-step ahead Forecasting, Natural Gas, Crude Oil


  1. Barrieu, P., Scandolo, G. (2015) Assessing financial model risk. European Journal of Operational Research, 242, 546-556.
  2. Burnham, K.P., Anderson, D.R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer-Verlag.
  3. Rigby, R.A., Stasinopoulos, D.M. (2005) Generalised Additive Models for location, scale, and shape. Journal of the Royal Statistical Society: Series C, 54, 507554.

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