Reference number : GC-2017-Q1-01704, amendment number : , agreement type : G, recipient legal name : Schied, Alexander (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Traditional risk management relies to a large extend on probabilistic models. Due to the complexity of economic processes and a number of generic statistical facts, such as the nonstationarity of financial time series, these probabilistic models are often subject to significant model uncertainty. The resulting misspecification of a probabilistic model can lead to substantial model risk, and it is therefore desirable to develop risk management methods that are robust with respect to model uncertainty. Such methods and approaches can also lead to interesting mathematical problems, and it is the goal of this proposal to analyze some of these mathematical aspects of model uncertainty and robustness and thereby to contribute to the long-term goal of developing an array of robust responses to model risk. Specifically, we will investigate issues arising in the statistical estimation of risk measures, probabilistic solutions of robust optimization problems in portfolio liquidation, and the construction of robust trading strategies in continuous time. Mathematically, this will involve robust statistics, nonlinear expectation operators, branching diffusions, and pathwise It&ocirc; calculus., program purpose french : Traditional risk management relies to a large extend on probabilistic models. Due to the complexity of economic processes and a number of generic statistical facts, such as the nonstationarity of financial time series, these probabilistic models are often subject to significant model uncertainty. The resulting misspecification of a probabilistic model can lead to substantial model risk, and it is therefore desirable to develop risk management methods that are robust with respect to model uncertainty. Such methods and approaches can also lead to interesting mathematical problems, and it is the goal of this proposal to analyze some of these mathematical aspects of model uncertainty and robustness and thereby to contribute to the long-term goal of developing an array of robust responses to model risk. Specifically, we will investigate issues arising in the statistical estimation of risk measures, probabilistic solutions of robust optimization problems in portfolio liquidation, and the construction of robust trading strategies in continuous time. Mathematically, this will involve robust statistics, nonlinear expectation operators, branching diffusions, and pathwise It&ocirc; calculus., agreement title english : Mathematical aspects of some robust techniques in risk management, agreement title french : Mathematical aspects of some robust techniques in risk management, agreement number : RGPIN, agreement value : 215000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada