Machine Learning for Financial Risk Management with Python

Chapter 1. Fundamentals of Risk Management

In 2007, no one would have thought that risk functions could have changed as much as they have in the last eight years. It is a natural temptation to expect that the next decade has to contain less change. However, we believe that the opposite will likely be true.

Harle, Havas, and Samandari (2016)

Risk management is a constantly evolving process. Constant evolution is inevitable because long-standing risk management practice cannot keep pace with recent developments or be a precursor to unfolding crises. Therefore, it is important to monitor and adopt the changes brought by structural breaks in a risk management process. Adopting these changes implies redefining the components and tools of risk management, and that is what this book is all about.

Traditionally, empirical research in finance has had a strong focus on statistical inference. Econometrics has been built on the rationale of statistical inference. These types of models concentrate on the structure of underlying data, generating process and relationships among variables. Machine learning (ML) models, however, are not assumed to define the underlying data-generating processes but are considered as a means to an end for the purpose of prediction (Lommers, El Harzli, and Kim 2021). Thus, ML models tend to be more data centric and prediction accuracy oriented.

Moreover, data scarcity and unavailability have always been an issue in finance, and it is not hard to guess that the ...