Martina Fraschini is the PI. The project “Fintech And Artificial Intelligence For Inflation Targeting” (FAIT) will benefit from an FNR commitment of 737,000 EUR and was accepted within the Industrial and Service Transformation priority. This project aims to investigate the potential of Central Bank Digital Currencies (CBDCs) and Artificial Intelligence (AI) to improve central banks’ efficacy in inflation targeting and help them achieve their policy objectives. The project comprises three working packages, the results of which will help academics and policymakers understand inflation dynamics better. Prof. Fraschini’s study seeks to develop strategies for effectively integrating these technologies into central banks’ frameworks and facilitate more robust policy implementation and responsiveness.

Julien Penasse is the PI. The project “Macroeconomic Risk Forecasting” (MACROFOR), with an FNR commitment of 654,000 EUR, was accepted within the Industrial and Service Transformation priority. The MACROFOR project introduces an innovative framework for monitoring and forecasting macroeconomic conditions, alongside the risks of rare yet significant events such as financial crises, pandemics, and wars. The project will gather diverse data, including macro-financial indicators, economic uncertainty measures, and data on infrequent occurrences like geopolitical conflicts and natural disasters. The project will reexamine several critical questions in macro-finance: the impact of significant uncertainty shocks on macroeconomic outcomes and the role of financial information in forecasting macroeconomic tail risk. The analysis will facilitate a deeper understanding of risk pricing and perception in equity and options markets over an extended period and across a broad range of economies.

Christos Koulovatianos is the PI. The project “Understanding And Quantifying Driving Forces And Effects Of Populism”, or POPULISM, has received an FNR commitment of 868,000 EUR, was accepted within the Sustainable and Responsible Development priority. The main goal of the project POPULISM is to quantify the role of populism in shaping market and political outcomes. The main applications of the project will focus on modelling the interplay between socioeconomic disparity measures, societal polarisation, political attitudes, voting patterns, austerity policies, financial instability, and the evolution of populism through social media platforms. By providing a systematic quantification of the phenomenon, policymakers can design resilient political institutions and evaluate policy choices to deal more effectively with the destabilizing forces of populism.

Diane Pierret with Christophe Ley (DMATH) and Gautam Tripathi (DEM) received the grant to enhance the understanding of time series data. Time-dependent data, also known as temporal data, is a vital and ubiquitous aspect of various domains, including finance, economics, healthcare, weather forecasting, and social media analysis. As the availability and complexity of temporal data continue to increase, there is a growing need for adapted robust methodologies and techniques to model and analyze such data in order to extract valuable insights.

Professor Taniguchi is a world-renowned expert in the analysis of such data, specialized in time series data analysis techniques and inference for stochastic processes (modelling, estimation, hypothesis testing). Since this combined skillset is not present at the University of Luxembourg, his visit would be a unique opportunity for researchers from all Faculties and Interdisciplinary Centers to learn about these approaches through discussions with him, a short course that he will teach and a workshop, and to collaborate with him on their burning research questions. Very concretely, he will work with Prof. Ley and his team on the creation of time series models for directional data on the sphere (with applications in many domains including space science and climatology), with Prof. Tripathi and colleagues from the Department of Economics and Management on estimating and forecasting econometric models to answer policy related questions, and with Prof. Pierret and colleagues from the Department of Finance on statistical inference in financial engineering, risk analysis and portfolio management. Outside the academic world, he shall give a public talk, organized within the realm of the Luxembourg Statistical Society, on modern challenges, risks and chances related to time-dependent data, have two meetings with representatives from banks and insurance companies in Luxembourg, and solidify links with the professional Japanese community (e.g., the Embassy of Japan) in Luxembourg.

Roberto Steri is the PI. The project, entitled Applied Contracting And Quantitative Incentive Theory, or ACQUIT. Prof. Steri’s project, with an FNR commitment of 776,000 EUR was accepted within the industrial and service transformation priority. ACQUIT, which kicks off in September 2023 aims to take a step towards closing the gap between theory and practice in the field of financial contracting. While the theory of financial contracting offers powerful tools to implement efficient decision making in private and public organisations, the applicability of the theory is still limited due to technical and conceptual challenges. The project thus takes advantage of modern computational and econometric tools to offer applications to four broad areas in financial economics, including corporate finance, auction theory, public policy and financial markets.

In corporate finance, Prof. Steri will assess the quantitative importance of managerial beliefs and optimally-designed compensation contracts in shaping corporate policies. In auction theory, he will design optimal procurement auctions for a financially-constrained buyer with implications for large-scale applications such as governmental purchases of pharmaceuticals. In public policy, he will assess whether interventions are effective to address credit market failures and stimulate recovery during the Covid19 crisis. In financial markets, he will show that the study of financial contracts between firms and external lenders provide helpful tools to track price fluctuations of financial securities.

Denitsa Stefanova is the PI. The project “Sustainable Finance and the Efficient Allocation of Capital” under the acronym GREEN, with a budget of 763,000 EUR, was accepted within the Sustainable and Responsible Development priority. The project kicked off in September 2022. Its overarching objective is to contribute to the understanding of the implications of sustainability considerations in the capital allocation decisions of households, corporations, and institutional investors, both public and private. The project aims at providing a framework for integrating sustainability metrics in the valuation and risk assessment of listed and private assets.

To achieve it, the project departs from the reliance on standard ESG metrics that are largely based on self-reported data and reflect the extent of company’s selective disclosure of ESG policies. Instead, it relies on machine learning techniques to infer the alignment of a company to the full array of UN’s sustainable development goals (SDGs). Companies’ production output as categories of products and services is linked to granular sustainability concepts by a natural language processing model that establishes relationships based on a vast cross-section of academic articles to find evidence of a product’s impact on a sustainability concept.

The project’s objectives are four-fold. First, it aims at testing the hypothesis whether higher ESG ratings of companies lead to more total capital issued by firms in equilibrium by establishing the role of ESG ratings on firms’ capital structure decisions. Second, it examines whether, in their allocation decisions, investors incorporate information about the sustainability impact of the assets in their portfolio through the lens of mutual fund investors. Third, it aims at assessing the implications of the divergence between published ESG ratings and the underlying fundamental sustainabiltiy footprint of the issuer for corporate bond valuation and the pricing of credit risk. Fourth, it targets to provide a framweork for the assessment of the sustainability alignment of infrastructure assets and th role of sustainabiltiy considerations in the flow of institutional investor capital to the asset class.

Diane Pierret and Roberto Steri are co PI with Artashes Karapetyan (ESSEC Business School). In this project, they study the effect of “green” as an attribute of collateral assets on household financial constraints and housing transactions. Households’ financing needs are primarily for durable assets (houses, cars, etc.) that they pledge as collateral in exchange for bank loans. Households have the possibility to choose between green vs. brown durable assets. The goal is to understand how the green attribute plays a role in the investment decision of the buying household, and in bank lending and risk-taking decisions.

They build on the literature on dynamic collateralized financing (Rampini, 2019; Lanteri and Rampini, 2021) and draw a parallel between green and durable. Both attributes increase asset prices. Durable assets can serve as collateral for a longer horizon and require less maintenance costs. Green assets also serve as collateral for a longer horizon, allow to save on energy expenses in the short run, and provide insurance against energy price risk in the long run. Households face collateral constraints such that they can only borrow up to a fraction of the house value and need to pay the rest with their own funds upfront. As the price of the green asset increases compared to brown assets, so does the upfront payment, and the financing needs of households investing in green assets increase. Financing needs of households may override risk management concerns, especially for poor households, amplifying inequalities as uninsured households are more susceptible to shocks.

The project has important policy implications as governments often use subsidies to stimulate investments into green assets. Banking regulation can also foster bank loans backed by green collateral. Through analysing the effect of green collateral on households’ financial constraints, we plan to learn more about the intended and unintended consequences of these policies on inequalities, financial stability, as well as on their environmental impact.

Benjamin Holcblat is a Secondary Proposer and a member of the management committee. He is also the representative for Luxembourg. COST – European Cooperation in Science and Technology– is Europe’s longest-running intergovernmental framework for science and technology cooperation. The project, with a COST commitment of more than 650 000 EUR, is called “Text, functional and other high-dimensional data in econometrics: New models, methods, applications” or HiTeC (2022-2026).

It will integrate cutting-edge analytic developments involving innovative sources of information, such as text, functions, perceptions or imprecise data, in econometrics. High-dimensional, complex and unstructured economic datasets cannot be fully exploited hitherto by the existing methodologies. An international network of experts, spanning the disciplines of econometrics, mathematics, statistics and computer science, will be created, with the aim of establishing and implementing new efficient inferential procedures for using such information in econometric modelling and forecasting. User-friendly and freely available software will be produced. These results will enable applied econometricians to mine textual information gathered from newspapers, articles, opinions and sentiments recorded by poles, in combination with other complex and traditional data. New techniques for analyzing the evolution of economic indicators will help to improve forecasting. Valuable insights into economic issues will provide ample prospects for further research, as vast sources of data are still noticeably under-exploited.

The potential to enhance economic data analysis will be fostered by a training programme for Early Career Investigators, and by intensifying connections among academics, stakeholders, and policy-makers. The impact will not be limited to economics and finance. The interaction with experts in other areas, such as environmental sciences or health, will facilitate the transfer of knowledge and technology. Emphasis will be given to sensor data and indicators that will alert to the vulnerability of commercial enterprises and social groups to extreme events associated with environmental hazards. Such indicators will include those relating to mortality risks.

Benjamin Holcblat and Mark Podolskij are co-PI with professors and researchers from the Department of Mathematics and the Department of Engineering. Since the turn of the millennium, technology has allowed the collection and storage of increasingly complex and large data sets, which has spurred significant advances in medicine, meteorology, engineering, finance, social sciences, and many more areas.

Most of these advances rely on strong mathematical foundations — see e.g. the epidemiological models or statistical theories required to tackle the COVID-19 pandemic and its potential remedies. The COVID pandemic, the associated financial crisis, or even the recent floods in Luxembourg and neighboring countries, have demonstrated that modern societies keep facing new challenges involving many intertwined factors, and that researchers and practitioners need flexible mathematical tools for analysing the resulting complex data structures. Modern data are typically composed of a huge number of (possibly high-dimensional) observations (e.g. internet traffic or social networks), have complicated topological structures such as directional data, and display strong (time-) dependence structures — to cite but a few crucial features. Therefore, it is of the utmost importance to train a new generation of scientists to take up these challenges and build novel, high-performance tools for modeling and statistically analyzing complex data.

The MATHCODA DTU intends precisely this: it is a doctoral training program that covers a coherent set of themes around the ideation and study of novel mathematical tools for dealing with high-dimensional and complex data structures, with applications ranging from life sciences to engineering and finance. The seven co-PIs are: Yannick Baraud (DMATH), Jack Hale (Department of Engineering), Benjamin Holcblat (Department of Finance), Christophe Ley (DMATH), Ivan Nourdin (DMATH), Giovanni Peccati (DMATH), and Mark Podolskij (DMATH and Department of Finance). According to our vision, the interaction between the distinct research groups participating in MATHCODA will provide to the doctoral candidates an invaluably rich learning environment, as well as an ideal springboard for their subsequent careers.

Mark Podolskij, who has a joint appointment with the Department of Mathematics, is the PI. The ERC commitment is 1.6 million euros. The project “Statistical Methods For High Dimensional Diffusions (STAMFORD) aims to provide a concise statistical theory for estimation of high dimensional diffusions. Such high dimensional processes naturally appear in modelling particle interactions in physics, neural networks in biology or large portfolios in economics, just to name a few.

The methodological part of the project will require development of novel advanced techniques in mathematical statistics and probability theory. In particular, new results will be needed in parametric and non-parametric statistics, and high dimensional probability, that are reaching far beyond the state-of-the-art. Hence, a successful outcome of STAMFORD will not only have a tremendous impact on statistical inference for continuous-time models in natural and applied sciences, but also strongly influence the field of high dimensional statistics and probability.