Machine Learning and Econometrics for Risk Measurement in Finance – MLEforRisk

The growing use of artificial intelligence and Machine Learning (ML) by banks and Fintech companies is one of the most significant technological changes in the financial industry over past decades. These new technologies hold great promise for the future of financial services, but also raise new challenges. In this context, the MLEforRisk project aims to provide better understanding of the usefulness of combining econometrics and ML for financial risk measurement. This project aims to provide a rigorous study of the benefits and limitations of these two approaches in the field of risk management, which is the core business of the financial industry. MLEforRisk is a multidisciplinary project in the fields of finance and financial econometrics which brings together junior and senior researchers in management, economics, applied mathematics, and data science.
The project has five methodological objectives related to credit, market, and liquidity risks. In the context of credit risk, ML methods are known to provide good classification performances. However, these methods often black boxes, which is particularly problematic for both clients and regulators. Thus, our objective is to develop hybrid approaches to credit risk modeling by combining econometrics and ML to overcome the trade-off between interpretability and predictive performance. At the same time, the use of ML in the field of credit risk has led to a debate on the potential discrimination biases which could be generated by these algorithms. Here, our objective is to develop statistical methods to test the algorithmic fairness of credit risk models and to mitigate these biases.
In the area of market risk, the project aims to combine ML techniques and advanced econometric modeling to improve the estimation of conditional risk measures associated to portfolio returns. Our objective is to propose new hybrid approaches for modeling the conditional variance matrix of returns or its inverse, called the precision matrix. Since these risk measures are the key input of trading strategies, the accuracy of their estimation is essential for the asset management industry. These estimation methods will be designed in the perspective of large portfolios for which the number of assets can exceed by far the number of time observations available to estimate the moments. A second objective is to take into account the asymmetry of the conditional distribution of returns when modeling the conditional risk by using ML methods.
Concerning liquidity risk, we observe that the development of alternative market indices and factorial investment significantly modify the dynamics of traded volumes on the markets by increasing dependencies and network effects. Our objective is to take these effects into account when measuring liquidity risk, while reducing the dimension of the parameter set used in the network with ML methods.
The MLEforRisk project aims at creating a doctoral training network for young researchers specialized in financial econometrics. It also aims to promote a reproducible research. All codes and data produced within the project will be archived on RunMyCode and the reproducibility of the numerical results will be certified by cascad, the first certification agency for scientific code and data.