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Quantitative Risk Management (MSc Actuarial Science and Mathematical Finance)
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The study programme

In the study programme of Quantitative Risk Management you will learn about the latest techniques, practices and big data developments in the field of financial risk management.

Profit from an up-to-date curriculum anticipating big data developments 

The study programme of this track 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 programme

Quantitative Risk Management is one of the tracks of the MSc Actuarial Science and Mathematical Finance. During your Master's you will follow 6 general courses and 3 track-specific courses including 1 elective. You will finish with a thesis. If you want to meet the requirements for admission to the post-Master's Actuarial Practice Cycle, you will also need to take the Honours programme.

  • Compulsory courses

    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.

  • Track-specific courses

    Banking Risk Management

    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.

    Financial Econometrics

    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.


    Choose 1 elective from the following options:

    • Actuarial Science of Pensions and Ageing
    • Machine Learning in Finance
    • Retirement Savings and Investment Decisions
    • Advanced Mathematics and Economics of Risk
    • Experimental Economics
    • Financial Institutions and Banking
  • Thesis

    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. Upon graduation, you will be awarded the title Master of Science (MSc).

  • Honours programme

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

    More about the Honours programme
Real-life case: how to regulate and manage large banks

Brexit, the trade war between the US and China, climate and energy transition risks, household debts, cyber threats, and low interest rates put a constant pressure on the performance of banks, insurance companies and pension funds. Add the ever growing, complex and interconnected financial system, and the need for adequate regulatory policies and risk management practices is larger than ever. These topics are important for regulatory bodies such as the Dutch Central Bank (DNB) and the European Central Bank (ECB), as well as risk departments within banks, pension funds, and insurance companies.

Contemporary issues

Examples of real-life business cases and company projects you will discuss:

  • Financial data. What is an accurate way of modelling financial times series data?
  • Capital Buffers. How should capital buffers of banks be determined such that the probability of another financial crisis is minimised?
  • Regulation. What issues do banks face when implementing the new bank regulation?
Copyright: FEB
Students from the ASE are open-minded and friendly. We are encouraged to develop critical thinking and a cooperative mindset. Jingde Guo - track Quantitative Risk Management Read about Jingde's experiences with this Master's
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