Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling

Probabilistic Graphical Models gives an overview of PGMs (a framework encompassing techniques like bayesian networks, markov random fields and chain graphs), which incorporate forward-looking information for making financial decisions, and applies them to stress testing, asset allocation, hedging, and credit risk.

This approach describes a new way to contend with stress testing (a big component of regulations like CCAR, the AIFMD, and Solvency II), teaches the reader how to strengthen their portfolios, presents a forward-looking way of conducting tail hedging, and gives a clear picture of the credit risk of the institution in question (such as a bank or a hedge fund).

Probabilistic Graphical Models teaches this relatively new technique to the reader, explaining how it can be applied to a variety of everyday challenges. Previous to their use in finance, PGMs have been used in disciplines such as computer science, engineering and medicine. Author Alexander Denev expands on this pre-existing material to examine other types of PGMs, demonstrating a novel range of applications