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Financial Econometrics (Msc Econometrics)
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The study programme

The study programme of the Financial Econometrics track has focus on mathematical and statistical techniques and their application to financial models and time series.

Econometric techniques from a financial perspective

In the Financial Econometrics track, you learn how to apply econometric techniques to support portfolio management or for example in the valuation of securities. You will familiarise yourself with the application of econometric techniques on financial data, interpret the results from a financial perspective and become an expert in the complex mathematics of the financial economy.

Your questions answered

Our student and programme director answer 3 questions you might have: what makes this programme unique, what will you bring to your future employer and what is the biggest pitfall in this programme?

The programme

Financial Econometrics is one of the tracks of the MSc Econometrics. During your Master's you will follow 4 general courses and 5 track-specific courses and electives. You will finish with a thesis. If you have excellent analytical and leadership abilities and it is your goal to use applied research to tackle complex real-life problems, you can participate in our Honours programme. There is also an opportunity to pursue a Master’s degree in both Econometrics and Mathematics, if you opt for a Double Degree Master’s programmes.

  • Compulsory courses

    Advanced Econometrics I

    In this course you will gain a deep understanding of econometric theory, practice and inference. You will learn how to apply advanced econometric techniques in practice, extend available methods for particular applications and how to implement them in a matrix programming environment. Also you will learn to understand and derive their statistical properties.

    Theory of Markets

    In this course you will study the microeconomic theory of perfect and imperfect competition. Learn under what conditions markets perform well as a means to organise economic activity (and under what conditions they do not).

    Data Science Methods

    In this course you will cover the basic theory of multivariate data analysis and of statistical methods in data science. You will focus on the most relevant multivariate techniques, as well as their application to econometric data in computer lab sessions. We will introduce you to Python, NumPy and pandas, data scraping, cleaning and wrangling.

    Advanced Econometrics II

    In this course you will build upon the general knowledge you acquired in Advanced Econometrics 1. You will gain a deep understanding of econometric theory, acquire the technical skills to conduct inference and be able to implement these techniques using software like MATLAB, R or Python.

  • Track-specific courses

    Financial Mathematics for Insurance

    In this course you learn the basic principles of asset pricing and risk mitigation on a market consistent basis. The underlying principle for this course is the notion that the market consistent value of an insurance or pension contract is based on the market value of the best possible replicating portfolio plus a possible add-on for the remaining (unhedgeable) residual risk. Therefore we provide you with an introduction to mathematical techniques which can be used in complete markets, such as those for equity and interest derivatives.

    Mandatory electives: semester 1

    Choose 1 out of 2 electives:

    • Complex Economic Dynamics
    • Machine Learning for Econometrics

    Mandatory electives: semester 2

    Choose 1 out of 8 electives:

    • Behavioural Finance
    • Economic and Financial Network Analysis
    • Machine Learning in Finance
    • Microeconometrics
    • Quantitative Finance and Algorithmic Trading
    • Real Estate and Alternative Investments
    • Real Estate Finance
    • Behavioural Macro and Finance

    Stochastic Calculus

    In this course you learn the elements of probability theory, stochastic processes and stochastic calculus relevant in the analysis of financial derivatives. You focus on the mathematical concepts and techniques and to a lesser extent on their application in pricing and hedging derivatives.

    Financial Econometrics

    In this course you cover: linear time series analysis, volatility models, value at risk, VAR models and co-integration, multivariate volatility and correlation models, high-frequency data and realized variance. You will apply your knowledge to empirical data using Python and R.

  • Thesis

    The academic programme culminates in a thesis, which allows you to engage with state-of-the-art data analysis and statistical techniques. The Master’s thesis is the final requirement for your graduation. It is your chance to dive deep into a topic in your field of choice (track) that you are enthusiastic about, and allows you to do an independent research project. A professor of your track will supervise and support you in writing your thesis.

  • Honours programme

    If you are a student of the Econometrics MSc and you have a record of academic excellence, a critical mind and an enthusiasm for applied research, then our Econometrics Honours programme is a great opportunity for you.

    More about the Honours programme
  • Double Degree Master's programme

    If you want to pursue a Master’s degree in Econometrics as well as in Mathematics, you can opt for one of our Double Degree Master’s programmes:

    • Master's in Double Degree Programme in Mathematics and Econometrics/Econometrics. In combination with all specialisations of the MSc in Econometrics.
    • Double Degree Master's programme Econometrics and Stochastics and Financial Mathematics. In combination with the specialisation Financial Econometrics of the MSc in Econometrics.
    More about the Double Degree Master's programmes
Check more detailed information about the specific courses in the course catalogue
Real-life case: high-frequency algorithmic Bitcoin trading using both financial and social features

Study and compare the performance of high-frequency trading (HFT) algorithms for trading Bitcoins on cryptocurrency exchanges. It is possible to develop profitable trading strategies on the Bitcoin market? Does the inclusion of social indicators, retrieved from sentiment analysis, lead to significantly better results?

Contemporary issues

Examples of relevant issues that could be discussed in your classroom.

  • Value at risk
  • Activa pricing
  • High frequency data from stock markets
  • What is the neural circuitry involved in financial and social decision-making?
Copyright: Onbekend
I wanted to be an architect when I was a kid. Now I operate as a Machine Learning Engineer. The academic way of tackling a problem, something I learned during my studies, is something I still use a lot. Dolf Noordman - alumnus MSc Econometrics Read about Dolf's experiences with this Master's
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