Modelling of non-bank financial institutions

The development of mathematical models for the analysis of financial stability and systemic risk in the financial system has been the focus of a lot of research efforts in the aftermath of the 2007-2008 financial crisis. Much of this research has been focused on the analysis of the stability of the banking system and the regulation of banks. However, the banking system is only a subset of the financial system, which also includes many types of Onon-bankO financial institutions, most notable asset managers such as mutual funds, pension funds, money market funds, hedge funds, as well as insurance companies. These Onon-banksO hold a sizable fraction of financial assets and account for a large proportion of transactions, so an accurate understanding of their role and their interactions with the banking system is crucial for a realistic modelling of the financial system.

This project aims at improving our understanding of the role of non-bank financial institutions by exploiting regulatory filings and market data to develop a detailed description of the ecology of the financial system. The first step is a statistical analysis of regulatory data and market data in which we will make use of classification methods and modern methods of data analysis in order to arrive at a stable and interpretable partition of financial market participations into groups following similar strategies and to characterize the financial environment at micro and macro levels. Our aim is to characterize the main categories of non-banks in the financial system and identify their main features and interactions.

We take advantage of machine learning techniques to characterize the behavior of non-banks by exploiting all the temporal and spatial information that we can extract from holdings and market data.

A second step will be to model the behavior of these non-banks in a mathematical framework which lends itself to analytical study or simulations which may be useful for stress testing and risk analysis, and for examining the consequences for systemic risk and financial stability.

This project falls within the following EPSRC research areas: artificial intelligence technologies, statistics and applied probability.

Fidelity Investments Inc. is the industrial partner for this project.

Probabilistic modelling permeates the Financial services, healthcare, technology and other Service industries crucial to the UK's continuing social and economic prosperity, which are major users of stochastic algorithms for data analysis, simulation, systems design and optimisation. There is a major and growing skills shortage of experts in this area, and the success of the UK in addressing this shortage in cross-disciplinary research and industry expertise in computing, analytics and finance will directly impact the international competitiveness of UK companies and the quality of services delivered by government institutions.
By training highly skilled experts equipped to build, analyse and deploy probabilistic models, the CDT in Mathematics of Random Systems will contribute to
- sharpening the UK's research lead in this area and
- meeting the needs of industry across the technology, finance, government and healthcare sectors

MATHEMATICS, THEORETICAL PHYSICS and MATHEMATICAL BIOLOGY

The explosion of novel research areas in stochastic analysis requires the training of young researchers capable of facing the new scientific challenges and maintaining the UK's lead in this area. The partners are at the forefront of many recent developments and ideally positioned to successfully train the next generation of UK scientists for tackling these exciting challenges.
The theory of regularity structures, pioneered by Hairer (Imperial), has generated a ground-breaking approach to singular stochastic partial differential equations (SPDEs) and opened the way to solve longstanding problems in physics of random interface growth and quantum field theory, spearheaded by Hairer's group at Imperial. The theory of rough paths, initiated by TJ Lyons (Oxford), is undergoing a renewal spurred by applications in Data Science and systems control, led by the Oxford group in conjunction with Cass (Imperial). Pathwise methods and infinite dimensional methods in stochastic analysis with applications to robust modelling in finance and control have been developed by both groups.
Applications of probabilistic modelling in population genetics, mathematical ecology and precision healthcare, are active areas in which our groups have recognized expertise.

FINANCIAL SERVICES and GOVERNMENT

The large-scale computerisation of financial markets and retail finance and the advent of massive financial data sets are radically changing the landscape of financial services, requiring new profiles of experts with strong analytical and computing skills as well as familiarity with Big Data analysis and data-driven modelling, not matched by current MSc and PhD programs. Financial regulators (Bank of England, FCA, ECB) are investing in analytics and modelling to face this challenge. We will develop a novel training and research agenda adapted to these needs by leveraging the considerable expertise of our teams in quantitative modelling in finance and our extensive experience in partnerships with the financial institutions and regulators.

DATA SCIENCE:

Probabilistic algorithms, such as Stochastic gradient descent and Monte Carlo Tree Search, underlie the impressive achievements of Deep Learning methods. Stochastic control provides the theoretical framework for understanding and designing Reinforcement Learning algorithms. Deeper understanding of these algorithms can pave the way to designing improved algorithms with higher predictability and 'explainable' results, crucial for applications.
We will train experts who can blend a deeper understanding of algorithms with knowledge of the application at hand to go beyond pure data analysis and develop data-driven models and decision aid tools
There is a high demand for such expertise in technology, healthcare and finance sectors and great enthusiasm from our industry partners. Knowledge transfer will be enhanced through internships, co-funded studentships and paths to entrepreneurs