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DTSTART;TZID=Europe/Paris:20190307T130000
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DTSTAMP:20260418T125416
CREATED:20181105T082321Z
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SUMMARY:Ines Wilms (Maastricht University\, Netherlands)
DESCRIPTION:Title: Multi-class Vector AutoRegressive Models for Multi-Market Commodity Data \nAbstract: Vector AutoRegressive (VAR) models form a special case of multivariate regression models in that the response variables are observed over time and modeled as a function of their own past values. We use VAR models to study dynamics among agricultural\, metal and energy commodity returns. As the increasing integration of the world economy suggests commodity dynamics to be comparable for different markets\, we aim to jointly analyze these dynamics across markets. To this end\, we introduce a sparse estimator of the Multi-Class Vector AutoRegressive model. We jointly estimate several VAR models\, one for each market (“class”)\, to borrow strength across markets. Our methodology encourages effects to be similar across markets\, while still allowing for small differences between them. Moreover\, we focus on multi-class estimation of high-dimensional VAR models\, i.e. models with a large number of time series relative to the time series length. Therefore\, our estimate is sparse: unimportant effects are estimated as exactly zero\, which facilitates the interpretation of the results. \nThis is joint work with Luca Barbaglia and Christophe Croux
URL:https://www.naxys.be/event/ines-wilms/
LOCATION:E25
CATEGORIES:NAXYS Seminar
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DTSTART;TZID=Europe/Paris:20190312T130000
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CREATED:20190117T152123Z
LAST-MODIFIED:20190304T222141Z
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SUMMARY:Anna Kiriliouk (UNamur)
DESCRIPTION:Title: Climate event attribution using multivariate peaks-over-thresholds modelling \nAbstract: Quantifying the human influence on climate change and identifying potential causes is a highly relevant research area which is often referred to as detection and attribution. A common approach is to compare the probability of an extreme event in the factual world to the probability of an extreme event in a counterfactual world\, i.e.\, a world that might have been if no humans would have existed. The event probabilities can be calculated using large scale climate model runs that simulate the evolution of the climate with and without anthropogenic forcings. The Fraction of Attributable Risk (FAR) is defined as the relative ratio of event probabilities in the factual and in the counterfactual world. Estimating the FAR will allow us to quantify the extent to which human activities have increased the risk of occurrence of an extreme event. We propose a model for the FAR based on the multivariate generalized Pareto distribution\, i.e.\, the asymptotic distribution of suitably normalized exceedances over a high threshold. The model is used to quantify the increased risk of an extreme rainfall event in central Europe. \n 
URL:https://www.naxys.be/event/anna-kiriliouk-unamur/
LOCATION:E25
CATEGORIES:NAXYS Seminar
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