The Quantitative Risk Management track in the MSc Actuarial Science and Mathematical Finance provides an excellent grounding for your career as a risk manager. What makes this 1-year programme unique is that it incorporates all the latest international developments, such as bank regulation and advanced quantitative risk modelling. Also, you will gain insight in all up-to-date techniques and practices to kick-start your career. The Quantitative Risk Management track is one of 2 tracks you can choose from within this Master’s programme.
How does a financial institution deal with financial uncertainty, liabilities, IT security threats and data-related risks? Risk management is all about identifying, assessing and controlling such threats to an organisation's capital and earnings. Since especially digital developments are moving increasingly fast, the field of risk management keeps bringing you interesting challenges to sink your teeth into.
Hi, I'm Ha Ahn! I'm a Master’s student in Actuarial Science and Mathematical Finance from Vietnam. Got questions about this Master's or the Quantitative Risk Management track? Get in touch via our chat tool.Ask Ha Ahn your questions
Your lecturers will be industry experts and leading researchers who conduct world-class research in the field. They are enthusiastic about introducing you to the latest developments and their theories on how to deal with new challenges in the risk management field.
In the 1st part of this course you will learn the basic principles and requirements governing banking regulation and supervision, aimed at safeguarding stable banks. We will analyse the risks as well as the remedies that were drawn up for bank risk management. You will also explore the changing structure of the broader financial system. The 2nd part of this course treats quantitative models for (portfolio) credit and liquidity risk management at an advanced level.
This course covers the following topics: linear time series analysis, volatility models, value at risk, VAR models and cointegration, multivariate volatility and correlation models and high-frequency data and realised variance. These topics are applied to empirical data using Python and R.
Apart from the general and track-specific courses, we offer you a selection of electives to choose from:
Examples of real-life business cases and company projects you will discuss:
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