Paper title: Stochastic simulations for climate risk assessment
Presenter: Denis Allard
Abstract : Stochastic simulations of spatio-temporal processes are powerful tools for generating new data scenarios, for example in view of assessing climate risks. Stochastic simulations are usually divided into two groups: parametric models and nonparametric models. Parametric approaches face difficulties when it comes to accurately simulating multiple meteorological variables in space and time because they necessitate models that can capture the complex inter-variable and space-time dependencies. Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datasets while using only minimal assumptions. However, such methods cannot generate extreme events beyond the observed range of data value. Extreme value theory can be used to extrapolate observed data towards yet unobserved high quantiles.
Paper title: What data are we missing to build a sustainable future? Focus on 2 R&D projects to fill in the environmental data gap
Presenter: Imène Ben Rejeb-Mzah
Abstract : AI, satellite images… To what extent new technologies could be harnessed to fill in the environmental data gap? During this presentation, Imène Ben Rejeb-Mzah, will present 2 examples of R&D projects carried on within her team to model climate data. The first one uses machine learning among other techniques to predict missing Energy Performance Certificates, while the second one explores the use of satellite images to measure methane emissions from Oil and Gas infrastructures.
Paper title: Clustering for Environmental Extreme Events
Presenter: Elena Di Bernadino
Abstract : In a wide range of applications in climate science, extreme events with a nonnegligible probability can occur, leading to disastrous consequences. Extremes in climatic events such as wind, temperature, and precipitation can profoundly impact humans and ecosystems, resulting in events like floods, landslides, or heatwaves. When the focus is on studying variables measured over time at numerous specific locations, such as the previously mentioned variables, partitioning these variables becomes essential to summarize and visualize spatial trends, which is crucial in the study of extreme events. This talk explores several models and methods for partitioning the variables of a multivariate stationary process, focusing on extreme dependencies.
Paper title: Risk associated with Nature and Biodiversity degradation : First overview of the main problems and difficulties encountered in an attempt to identify a science-based approach/framework to model these risks and their impact on financial risks.
Presenter: Alexandre Marette
Abstract : After focusing on climate risk a few years ago, regulators, currently in dispersed order, seem to be placing increasing emphasis on assessing the risk associated with the degradation of nature and biodiversity, as well as its impact on the economy and the stability of the banking system. For example, the ECB has proposed a first approach to modelling these risks and their impact on the euro area in a recently published article (2023). During the presentation, Alexandre will present the first conclusions/findings of the work carried out by the transversal team in charge of the project "Risk modelling associated with biodiversity and nature", within the ESG Squad RISK QUANT.
Paper title: Physical climate risk for companies
Presenter: Joe Moorhouse
Abstract : Assessing physical climate risk is difficult and uncertain; assessing a company’s physical climate risk is trickier still. We discuss how the problem can be tackled, the limitations – what these assessments can and cannot achieve – and the next steps.
Paper title: Asset pricing under transition scenario uncertainty and model ambiguity
Presenter: Peter Tankov
Abstract : We study asset pricing and optimal investment decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. We assume that the probabilities of individual prospective scenarios are ambiguous and place ourselves into the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal investment decision as an optimal stopping problem with learning under ambiguity. We then prove a minimax result allowing to reduce this problem to a series of standard optimal stopping problems. The theory is illustrated with the example of optimal divestment from a coal-fired power plant under transition scenario ambiguity.
