Irene Monasterolo - On the dependence of investor's probability of default on climate transition scenarios
Climate risk brings about a new type of financial risk that standard approaches to risk management are not adequate to handle. Amidst the growing concern about climate change, financial supervisors and risk managers are concerned with the risk of a disorderly low-carbon transition. We develop a model to compute i) the valuation adjustment of corporate bonds, depending both on climate transition risk scenarios and on companies’ shares of revenues across low/high-carbon activities, and ii) the corresponding adjustments of an investor’s Expected Shortfall and probability of default. Implications for central banks' climate financial risk management include that climate stress test exercises should allow for a wide enough set of scenarios in order to limit the underestimation of losses.
Eva Flonner - Bayesian Neural SDEs for Exotic Option Pricing
In financial institutions, the task of choosing one model from a pool of asset pricing models, given current market and historical data, is called calibration. Machine learning techniques started to offer new perspectives on this crucial task since they are computationally efficient and manage to capture empirically observed market characteristics. Recently so-called neural stochastic differential equations (NSDE) were introduced for solving the calibration problem in a setting where one is looking for a model which generates given market prices. The two drawbacks of this approach are that only point estimates of neural network weights are provided without uncertainty quantification on the resulting option prices. Price bounds within the asset pricing model at hand are however important in view of robustness and model risk. It is attempted to tackle these aspects using Bayesian NSDEs.
Lydia Novoszel - Implication of COVID-19 pandemic on supply chains, a meta-analysis of supply chain disruption research – work in progress
The current COVID-19 global pandemic has immediate implications on supply chains across industries. The crisis impacts supply, distribution and demand. Disruptions triggered through a number of different risks are well researched. The review shows the current academic discourse on supply chain disruptions amid the COVID-19 pandemic, applied approaches and highlights future research opportunities and research methods to investigate supply chain disruptions and its implications on performance.
Giacomo Bressan - Climate physical risk and the financial sector
Climate risk raises significant concerns for financial institutions and regulators, both from an individual company and systemic perspective. In this presentation, after a quick discussion on the main features of climate risk, we provide an overview of the research proposal, centred on climate physical risk. We take a closer look at its assessment, focusing on exposure, pricing and impact. To do so, we leverage the current work on the CASCADES project, aiming at conducting the stress test of the European financial system for extra-European climate physical risks. We also illustrate the model that we plan to use for the assessment, to show how network modelling can be leveraged for stress tests of interconnected financial systems. Finally, we discuss existing data challenges and further developments for the research project.
Régis Gourdel - Bi-layer stress contagion across investment funds: a climate application
We provide a framework for short-term modelling of market risk and shock propagation in the investment funds sector, with an extensive analysis of its different internal contagion channels, including the influence of funds' cross-holdings. This tackles the increasing importance of the sector within the financial system, and in particular with regard to climate risk exposure and contribution to financing the green transition. Indeed, while fund managers or investors communicate more aggressively on their awareness of climate risk, it is still not fully understood how that risk threatens the sector. Our analysis suggests that the particular topology of the fund network interacts with the heterogeneity of the market with regard to the exposure to certain shocks, such as climate-driven ones. The interplay of these different layers appears as a key determinant of contagion when applying realistic data-based shocks.