Aims:

This module aims to provide fundamental knowledge on how to model, real, complex, financial systems. Students will learn about the design of effective models. They will be taught the methodology that starts from the analytics of the data and ends in the construction of a model which is able to both describe the existing observations and also predict new outcomes.Learning outcomes:

On successful completion of the module, a student will be able to:

Acquire fundamental data-driven modelling knowledge and skills to design, construct, test and validate models starting from the analysis of real data.
	Learn methodologies and development tools specifically designed for the application of data-driven modelling to financial markets.
Content:

Students will first learn about probability and probabilistic modelling. They will be thought how to identify and characterize the likelihood of occurrence of an event, or a set of events from complex datasets. Focus will be given to non-normal statistics: ‘fat-tails’ distributions. Students will also learn about the multivariate nature of these systems and they will be provided tools for the identification of the structure of dependency and causality between the system’s variables. The specificity of machine learning approaches to time series and non-stationary stochastic processes will be emphasized. Concepts concerning, scaling laws and memory effects in real stochastic processes will be introduced and discussed.Requisites:

To be eligible to select this module as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have fundamental knowledge of mathematics, including calculus, limits, differential and integral equations, linear algebra, study of functions, the concept of probability; and (3) have some knowledge of coding.

Students can complement some of these basic skills in the first few weeks of Term 1 by attending the ‘Introduction to Mathematics and Programming for Finance’ course.