Speaker: Prof. Damien Challet, Mathematics & Computer Science, University of Paris-Saclay Abstract: We systematically investigate the links between price returns and ESG features. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and […]
Speaker: Prof. Dorje C. Brody, Mathematics, University of Surrey Abstract: The behaviour of a person is dominated by their ability to process uncertain information available to them. When there is a range of alternatives to choose from, the likelihoods assigned by the person to these different alternatives determine the state of their mind in relation […]
Speaker: Prof. Andrea Macrina, Mathematics, University College London Abstract: We develop a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: the first, which we largely focus on, features a dynamic random quantile level and allows for direct interpretation of […]
Speaker: Dr. Carmine de Franco, Head of Research & ESG, Ossiam Abstract: We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess […]
Speaker: Prof. Matthew Dixon, Applied Mathematics, Illinois Institute of Technology Abstract: Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset […]