Assessing performance evaluation and credit risk of energy companies with Multicriteria decision models

During the last decades, the energy sector has gone through several deregulation phases, allowing for the new entrance of competitors. This renewed competitive structure has led stakeholders to face with unprecedented complex problems, such as more alternatives to evaluate, multiple and conflictual criteria to manage and a higher level of uncertainty to deal with, that were no longer solvable with traditional models. Multi criteria decision aid (MCDA) models, thanks to their multi-dimensional nature, easiness of application and ability to include different Decision Maker’s preferences, appear as the most suitable models to help multiple decision makers in solving two of the most crucial issues of the energy sector: the performance evaluation and the credit risk assessment of energy companies. Thus, in this thesis three different Multi Criteria Decision Aid (MCDA) models have been developed to address the two aforementioned research issues of this sector. With regard to the first issue, Chapter 2 proposes the development of the Stochastic Multi-Attribute Acceptability Analysis (SMAA) model to assess the performances of a set of twenty listed energy companies under different criteria and uncertainty scenarios. With regard to the second issue, Chapter 3 presents the implementation of a non-parametric multiple criteria decision aiding (MCDA) model, the Multi-group Hierarchy Discrimination (M.H.DIS) model, with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), on a dataset of 114 European unlisted companies operating in the energy sector.