Project risk management is a crucial activity in project management. Nowadays, projects are facing a growing complexity and are thus exposed to numerous and interdependent risks. However, existing classical methods have limitations for modeling the real complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This Ph.D. thesis aims at analyzing propagation behavior in the project risk network through modelling risks and risk interactions. An integrated framework of decision support system is presented with a series of proposed methods. The construction of the project risk network requires the involvement of the project manager and the team of experts using the Design Structure Matrix (DSM) method. Simulation techniques are used and several network theory-based methods are developed for analyzing and prioritizing project risks, with respect to their role and importance in the risk network in terms of various indicators. The proposed approach serves as a powerful complement to classical project risk analysis. These novel analyses provide project managers with improved insights on risks and risk interactions under complexity and help them to design more effective response actions. Considering resource constraints, a greedy algorithm and a genetic algorithm are developed to optimize the risk response plan and the allocation of budget reserves dedicated to the risk management. Two examples of application, 1) to a real musical staging project in the entertainment industry and 2) to a real urban transportation system implementation project, are presented to illustrate the utility of the proposed decision support system.

La gestion des risques projet est une activité cruciale dans le management de projet. Aujourd'hui, les projets sont confrontés à une complexité croissante et sont ainsi exposés à de nombreux risques interdépendants. Cependant, les méthodes classiques ont des limites pour la modélisation de la complexité réelle des risques du projet. Par exemple, certains phénomènes comme les réactions en chaîne et des boucles ne sont pas correctement pris en compte. Cette thèse de doctorat vise à analyser le comportement du réseau de risques projet grâce à la modélisation des risques et des interactions entre risques. Un système d'aide à la décision est introduit avec une série de méthodes associées. La construction du réseau de risques projet nécessite l'implication du manager de projet et l'équipe d'experts en utilisant la méthode Design Structure Matrix (DSM). Des techniques basées sur la simulation et la théorie des réseaux sont développées pour analyser et hiérarchiser les risques du projet, en regard de leur rôle et leur importance dans le réseau des risques. L'approche proposée constitue un puissant complément à l'analyse classique des risques projet. Ces nouvelles analyses fournissent aux managers de projet une meilleure vision sur les risques et sur leurs interactions complexes et les aident à élaborer des réponses plus efficaces. Prenant en compte les contraintes de ressources, un algorithme glouton et un algorithme génétique sont développés pour optimiser le plan de réponse aux risques et l'allocation des réserves budgétaires. Deux exemples d'application, 1) à un projet réel de mise en scène musicale dans l'industrie du divertissement et 2) à un projet réel de construction d’un système de transport urbain, sont présentés pour illustrer l'utilité du système d'aide à la décision proposé. / Project risk management is a crucial activity in project management. Nowadays, projects are facing a growing complexity and are thus exposed to numerous and interdependent risks. However, existing classical methods have limitations for modeling the real complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This Ph.D. thesis aims at analyzing propagation behavior in the project risk network through modelling risks and risk interactions. An integrated framework of decision support system is presented with a series of proposed methods. The construction of the project risk network requires the involvement of the project manager and the team of experts using the Design Structure Matrix (DSM) method. Simulation techniques are used and several network theory-based methods are developed for analyzing and prioritizing project risks, with respect to their role and importance in the risk network in terms of various indicators. The proposed approach serves as a powerful complement to classical project risk analysis. These novel analyses provide project managers with improved insights on risks and risk interactions under complexity and help them to design more effective response actions. Considering resource constraints, a greedy algorithm and a genetic algorithm are developed to optimize the risk response plan and the allocation of budget reserves dedicated to the risk management. Two examples of application, 1) to a real musical staging project in the entertainment industry and 2) to a real urban transportation system implementation project, are presented to illustrate the utility of the proposed decision support system.

In dieser Dissertation werden neue Methoden zur Erfassung zweier Risikoarten entwickelt. Markrisiko ist definiert als das Risiko, auf Grund von Wertrückgängen in Wertpapierportfolios Geld zu verlieren. Systemisches Risiko bezieht sich auf das Risiko des Zusammenbruchs eines Finanzsystems, das durch die Notlage eines einzelnen Finanzinstituts entsteht. Im Zuge der Finanzkrise 2007–2009 realisierten sich beide Risiken, was weltweit zu hohen Verlusten für Investoren, Unternehmen und Steuerzahler führte. Vor diesem Hintergrund besteht sowohl bei Finanzinstituten als auch bei Regulierungsbehörden Interesse an neuen Ansätzen für das Risikomanagement. Die Gemeinsamkeit der in dieser Dissertation entwickelten Methoden besteht darin, dass unterschiedliche Quantilsregressionsansätze in neuartiger Weise für das Finanzrisikomanagement verwendet werden. Zum einen wird nichtparametrische Quantilsregression mit Extremwertmethoden kombiniert, um extreme Markpreisänderungsrisiken zu prognostizieren. Das resultierende Value at Risk (VaR) Prognose- Modell für extremeWahrscheinlichkeiten wird auf internationale Aktienindizes angewandt. In vielen Fällen schneidet es besser ab als parametrische Vergleichsmodelle. Zum anderen wird ein Maß für systemisches Risiko, das realized systemic risk beta, eingeführt. Anders als bereits existierende Messgrößen erfasst es explizit sowohl Risikoabhängigkeiten zwischen Finanzinstituten als auch deren individuelle Bilanzmerkmale und Finanzsektor-Indikatoren. Um die relevanten Risikotreiber jedes einzelnen Unternehmens zu bestimmen, werden Modellselektionsverfahren für hochdimensionale Quantilsregressionen benutzt. Das realized systemic risk beta entspricht dem totalen Effekt eines Anstiegs des VaR eines Unternehmens auf den VaR des Finanzsystems. Anhand von us-amerikanischen und europäischen Daten wird gezeigt, dass die neue Messzahl sich gut zur Erfassung und Vorhersage systemischen Risikos eignet. / This thesis develops new methods to assess two types of financial risk. Market risk is defined as the risk of losing money due to drops in the values of asset portfolios. Systemic risk refers to the breakdown risk for the financial system induced by the distress of individual companies. During the financial crisis 2007–2009, both types of risk materialized, resulting in huge losses for investors, companies, and tax payers all over the world. Therefore, considering new risk management alternatives is of interest for both financial institutions and regulatory authorities. A common feature of the models used throughout the thesis is that they adapt quantile regression techniques to the context of financial risk management in a novel way. Firstly, to predict extreme market risk, nonparametric quantile regression is combined with extreme value theory. The resulting extreme Value at Risk (VaR) forecast framework is applied to different international stock indices. In many situations, its performance is superior to parametric benchmark models. Secondly, a systemic risk measure, the realized systemic risk beta, is proposed. In contrast to exististing measures it is tailored to account for tail risk interconnections within the financial sector, individual firm characteristics, and financial indicators. To determine each company’s relevant risk drivers, model selection techniques for high-dimensional quantile regression are employed. The realized systemic risk beta corresponds to the total effect of each firm’s VaR on the system’s VaR. Using data on major financial institutions in the U.S. and in Europe, it is shown that the new measure is a valuable tool to both estimate and forecast systemic risk.