Asset Liability Management - Cases and Skills

In this hands-on seminar you will learn about the practical implementation of an Asset Liability Management (ALM) study focusing on the match between investment policies and liabilities. The cases involve theoretical aspects such as asset dynamics and liability modelling, numerical aspects like Monte Carlo simulation as well as practical communication and team working skills.

Financial Mathematics for Insurance

In this course you learn the basic principles of asset pricing and risk mitigation on a market-consistent basis. The course provides an introduction to mathematical techniques which can be used in complete markets such as those for equity and interest derivatives, but it also considers incomplete markets.

Non-life Insurance - Statistical Techniques and Data Analysis

In this course you study statistical techniques that can be applied in non-life insurance. We explore Generalized Linear Models for determining insurance prices. Also, we take a look at IBNR models for predicting future payments on claims regarding events that have occurred in the past but are not yet (fully) known to the insurer. Another topic is credibility theory to predict future claims. Apart from the theory, we study and practice the implementation of the techniques using the programming language R.

Principles of the Mathematics and Economics of Risk

The course will cover the basics of information theory, including information asymmetry, moral hazard and adverse selection and the basics of behavioural insurance and finance.

Risk Management for Insurance and Pensions

This course provides an in-depth treatment of the principles of (quantitative) risk management for insurers and pensions. The course focuses first on the joint measurement, modelling and allocation of financial and insurance risks. Next, the course treats the design of risk mitigation strategies and of asset allocation strategies from a long-term perspective.

Stochastic Calculus

In this course elements of probability theory, stochastic processes and stochastic calculus are discussed to the extent that it is relevant in the analysis of financial derivatives. The emphasis is on the mathematical concepts and techniques and to a lesser extent on their application in pricing and hedging derivatives. Topics that are covered are discrete time methods: binomial trees and the Cox-Ross-Rubinstein model; continuous time stochastic processes: Brownian motion and martingales; stochastic calculus: the Ito integral, Ito's lemma and stochastic differential equations; Girsanov's theorem, equivalent martingale measures and risk-neutral valuation; the Black-Scholes-Merton model; implementation of various numerical methods in computer programmes.

Choose 3 electives from the following options:

• Actuarial Science of Pensions and Ageing
• Advanced Mathematics and Economics of Risk
• Banking Risk Management
• Climate Change Economics
• Experimental Economics
• Financial Institutions and Banking
• Machine Learning for Econometrics
• Retirement Savings and Investment Decisions
• Machine Learning in Finance

The Master’s thesis is the final requirement for your graduation. It is your chance to dive deep into a topic that you are enthusiastic about. A professor in your field of choice (track) will supervise and support you in writing your thesis. After a successful defence of your thesis and completion of the courses, you will be awarded the title Master of Science (MSc). 

Highly motivated students can participate in the Master's Actuarial Science and Mathematical Finance Honours programme. This challenging programme is a great chance to stand out for future employers.