Programme overview

Quantitative Finance
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The programme consists of seven core courses, a seminar (Financial Case Studies) and the master’s thesis.

Each core course focuses on either particular econometric methods (including time series models, Bayesian techniques, and Machine Learning methods) or a specific area in finance (such as Asset Pricing, Derivatives, Risk Management, and Portfolio Management) where quantitative support is indispensable. All core courses include empirical assignments, enabling you to gain experience with applying the econometric techniques in practice.

During the seminar Financial Case Studies you intensely work in a small team for a period of two months, in order to conduct an applied research project from start to finish. The topics for these projects are provided by firms in the financial industry. Representatives of these firms are actively involved in the supervision of the research projects, together with faculty members.

The master’s thesis can be theoretically oriented or practically oriented. Practically oriented theses are usually combined with an internship or traineeship.


The curriculum consists of:

  • 35% Asset and derivative pricing
  • 15% Risk management
  • 15% Portfolio Management
  • 35% Econometrics

In class

Some example of projects that featured in the seminar Financial Case Studies in the recent past:

  • Can we exploit the information in limit order book data for high-frequency algorithmic trading?
  • How to hedge interest rate risk for corporate bonds?
  • Is it possible to improve the investment strategy of pension funds, in an environment with very low interest rates and large changes in regulations?
  • What are attractive active investment strategies for commodities?

Study schedule

The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.

The aim of this course is to provide a profound and state-of-the-art insight into asset pricing, both from a theoretical and empirical perspective. The field of asset pricing aims to explain the prices of financial assets such as stocks, fixed income instruments and derivative securities. The field is highly relevant for financial management, because asset pricing models form the basis for many practical management applications such as capital budgeting, risk management, portfolio selection and performance evaluation.

During this course, you will be introduced tot the key insights from 50 years of theoretical, empirical and methodological research.
The course consists of two parts. One part focuses on the theory of asset pricing in the unifying framework of stochastic discount factors. The other part concentrates on testing asset pricing models and covers both empirical and methodological issues.

This course provides the mathematical foundations for the analysis of financial derivatives, in particular, futures, forwards and options. It explains pricing and hedging under the no-arbitrage assumption. Basic discrete and continuous probabilistic models are used to illustrate the binomial tree approach to option valuation and the Black-Scholes formulas for option pricing using Brownian motion and Ito calculus. Further topics include European option Greeks and hedging strategies, American and Exotic options, implied volatilityorder books and market makers.

Throughout the course, numerical examples and applications of empirical financial data are used to illustrate the analytical and Monte Carlo approaches.

By its very nature, the course involves a considerable amount of mathematics and statistics. Of all subjects in finance, the area of derivatives has used these tools most intensively.

The goals of the course are:

  • To become proficient at maneuvering through the fundamental derivative pricing concepts and modeling approaches.
  • To open the black box of the underlying modeling assumptions and understand the pros and cons of the most widely used models in practice.

Bayesian Econometrics

This course deals with the theory of Bayesian econometric techniques and relevant applications in a financial decision-making context.
Bayesian Econometrics plays an important role in quantitative economics, marketing research and finance. This course discusses the basic tools which are needed to perform Bayesian analyses. It starts with a discussion on the difference between Bayesian and frequentist statistical approach. Next, Bayesian parameter estimation, forecasting and Bayesian testing is considered. To perform a Bayesian analysis, knowledge of advanced simulation methods is necessary. Part of the course is devoted to Markov Chain Monte Carlo sampling methods including Gibbs sampling, data augmentation and Monte Carlo integration. The topics are illustrated using simple computer examples which are demonstrated during the lectures.
Financial applications in risk management, return predictability, and asset allocation are used to demonstrate the practical relevance of these issues.

Machine Learning in Finance

This course provides an introduction to machine learning techniques, focusing on those methods that are useful and popular in economic and financial applications. Topics that will be covered include:

  • Regularization
  • Classification and Regression Trees, Random Forests and other Ensemble Methods
  • Support Vector Machines
  • Clustering
  • Neural Networks

This course surveys modern finance theory as well as econometric and time series techniques that are used for modeling and forecasting (the term structure of) interest rates.
Topics include:

  • Bond market concepts: bond prices, interest rates, yields, forward rates and the term structure of interest rates
  • Empirical features of the yield curve
  • Theories of the term structure (expectations hypothesis, asset pricing theory)
  • Models of the term structure (Nelson-Siegel, Vasicek, CIR, Affine) in discrete and continuous time
  • Yield curve forecasting
  • Interest rate derivatives

This course covers advanced topics in time series econometrics and forecasting, including:

  • state space methods
  • regime-switching models
  • forecasting with many predictors (factor models)
  • forecast combinations
  • time-varying volatility and correlation

The focus is on techniques that are found useful in applications to macroeconomic variables (such as output and inflation), and financial time series variables (asset returns and volatility).

This course surveys the portfolio choice literature with a focus on the (econometric) methodology. We use both classical as well as Bayesian techniques to model future asset returns and to come up with optimal portfolios.

The content of the course can be split in 2 parts. The first part deals with short-term asset allocation (how to divide money over many individual securities). We start with efficient set mathematics and mean-variance analysis. Then we proceed by assessing the performance of the allocations out-of-sample. Finally, we analyze more sophisticated (econometric) techniques to improve performance.

The second part deals with long-term strategic asset allocation (i.e. how to divide money over different asset classes and how to change the allocations over time). We first analyze the standard methodology to understand the difference between short-term and long-term investors. Next, we critically assess the plausibility and performance of the outcomes. Finally, we consider more plausible specifications that incorporate realistic aspects such as parameter uncertainty and model instability.

The course focuses on modern, quantitative methods for measuring risks faced by financial institutions. It covers:

  • Various risk measures and risk measure theory (Value-at-Risk, expected shortfall, coherency)
  • General method used for quantifying risks (analytical method, historical simulation, Monte Carlo)
  • Market risk and risk aggregation (dynamic volatility model, normal mixture distributions, Extreme Value Theory, copula)
  • Model validation and backtesting
  • Credit risk (Merton model, threshold model, Bernoulli mixture model)
  • Systemic risk

Students work in teams of 4 on specific (theoretical or empirical) financial econometric research projects, which are supplied by firms in the financial industry (including asset managers, hedge funds, insurance companies, and pension funds). Examples include problems in asset pricing, portfolio management, risk management, and derivatives pricing.
Each group has its own supervisor(s). Groups meet with their supervisor(s) on a weekly basis, to discuss the progress of the research and relevant issues and questions. Each group writes a report about its research. A plenary session is scheduled at the end of the course term, in which the research project is presented by a member of the group and discussed by a member of another group, followed by a plenary discussion.

Proposal for the Master thesis Econometrics and Management Science. This proposal can be used as a part of the Master thesis. There is no grade for this proposal.

The thesis is an individual assignment about a subject from your Master's specialisation. More information about thesis subjects, thesis supervisors and the writing process can be found on the Master thesis website.

The overview above provides an impression of the curriculum for this programme for the academic year 2023-2024. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR. Please note that minor changes to this schedule are possible in future academic years.

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