The success of quantitatively trained graduates in the financial sector relies on their ability to communicate their work. The challenges in this context include the traditional ones, leading to verbal and non-verbal communication, but require special attention to the way to communicate technical elements, which bring about additional difficulties. There are also behavioural aspects underlying good communication, dealing with the ways the human brain works, presented well in the book "Thinking Fast and Slow," by Daniel Kahneman. This course raises awareness amongst students on the elements of good communication, brings observations on how to approach technical content, and aims to blend this with the subject matter of the program.

Risk management departments across the financial landscape are heavy users of financial mathematics. This course is designed as a survey course with a number of lectures representing various areas of risk. The scope is vast and many topics could by themselves be entire courses. We approach this challenge by introducing students to a wide array of relevant concepts. The main areas of consideration are market risk, counterparty credit risk, retail credit risk, anti money laundering and anti terrorist financing, and risk management of treasury operations. We also cover capital adequacy and stress testing, as well as a regulatory-driven introduction to climate change management and how it impinges on the risks facing financial firms.

This course begins with a review of the business elements that drive financial products and the mathematical tools needed to model them. The list of mathematical models includes a variety of time series frameworks, allowing for asymmetry, fat-tails and jumps, and will include forecasting techniques. The financial products included will include fixed income, volatility, options and risk premia and their use within different business environments, notably treasury and portfolio management.

Quantitative finaance is applied in the industry mostly through software implementatins of computational algorithms. Often, the mathematical methods employed involve more or less sophisticated mehtods; the accuracy, speed and resource consumption of such algorithms often mark the difference between a business workflow which is productive, useful, and economic and others which are slow, inefficient, resource intensive or simply inaccurate and therefore potentially useless. With the introduction of data science, machine learning and other methods from artificial intelligence into the wold of mathematical finance, computational challenges are increasing due to the larger computational ambition of new algorithms and the expansion of traditional business lines. Aligning innovation and discovery of efficient numerical methods with the development of new business lines, this course will develop best practices in the design of numerical methods for the efficient design of computational algorithms across a wide scope of mathematical implementations, ranging from the traditional areas of computational algebra and differential equations to the newer ones of colmputational graph theory and optimization.

The course exposes students to real examples of risk management in the financial industry through case studies to better understand the risk management concepts covered in MMF2000H. Through the exposure to complex real-world cases, that involve real financial and market data, students learn to synthesize risk management strategies that are the focus of much of the activity at financial institutions.

The course covers Blockchain Technologies from both a theoretical and practical point of view, with a focus on pragmatic applications and real industry examples. Topics will cover supervised and unsupervised learning, as well as high-level workflows from business problem definition down to analysis and integration with business strategy. The course will cover theory, applications, and common usage of key machine learning techniques, as well as case studies from the financial, data science, and professional services industries.

This course is an introduction to concepts and techniques in modern data science and machine learning. The course will be geared towards understanding the theory behind machine learning techniques at a technical level, as well as their practical applications. The course will also serve as an introduction to the activities performed by hands-on machine learning experts in the financial and professional services industries.

This course is designed to provide students with specialized skills in quantitative and data science with the knowledge required to create or identify a novel business idea and create the subsequent plan for a successful start-up. Instruction in the form of lectures, practicals, and seminars with industry practitioners and subject matter experts will provide students with much needed insight and direction. Students will be required to develop and pitch their own novel business plan to a panel of academics and experts in a 'start-up showcase.'

Climate Change is already impacting the lives of millions of people and the operations of thousands of businesses around the world. Along with business, financial institutions are also feeling the first impact of climate change in their portfolios and balance sheets. Climate Risks are the source of financial risks and require special consideration and are receiving increasing attention from regulators around the globe. In this course we start by covering the key facts about Climate Change and its implications on people, business models and the portfolios of financial institutions around the world. We then review the most recent and relevant developments in climate risk, dealing both with the political arena and the business strategies of leading firms. After setting the scene we introduce the key concepts of Climate Risk Management and explore the main aspects of physical transitional climate risk drivers. We will then examine the extensive regulatory activities and guidelines and move into Climate Risk Measurement and Modelling. Here we elaborate on carbon measurement and trading, ECL calculations, and model risk management. As a next step we will be looking at Climate Risk Stress Testing and Scenario Analysis. We conclude the course by focusing on Climate Risk Disclosure and reporting initiatives and discuss their relevance for climate risk measurement and modelling.