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Quantitative Asset and Risk Management II

  • Teacher(s):  
  • Course given in: English
  • ECTS Credits:
  • Schedule: Autumn Semester 2021-2022, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Finance, Orientation Asset and Risk Management

    Master of Science (MSc) in Finance, Orientation Corporate Finance

    Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science
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The objective of this course is to have a view on the quantitative techniques that are most frequently used in the industry and adress the most actual debates in quantitative asset management. The material covers a review of smart beta techniques and quantitative portfolio construction techniques, applied across asset classes. We then gradually switch to more active approaches. A good part of the course is dedicated to factor investing, where we review the basic papers of risk premia and their most recent applications. Again, this topic is adressed across asset classes. Finally, we also touch on various ad-hoc topics, extensively debated in the quantitative asset management industry, such as Trend following, Socially responsible investing.


1. Smart beta

  • Fundamental indexing
  • Risk-based indexing
  • Applications across asset classes

2. Active vs passive investing

  • The active vs passive debate
  • The fundamental law of active management
  • Black-Litterman
  • Measures of active investing

3. Factor investing

  • Theory of factor investing
  • Review of basic factors (value, momentum, carry, size, ...)
  • Recent applications of factors
  • Factor investing across asset classes
  • Factor timing

4. ESG investing


  • Roncalli T. , Introduction to Risk Parity and Budgeting, 2014
  • Scherer B., Portfolio Construction and Risk Budgeting, 2010
  • Qian E., R. Hua, E. Sorensen, Quantitative Equity Portfolio Management, 2007
  • Grinold R. and R. Kahn, Active Portfolio Management, 2000
  • Ang A., Asset Management, A Systematic Approach to Factor Investing, 2014
  • Bali T.G., R.F. Engle, S. Murray, Empirical Asset Pricing: The Cross Section of Stock Returns, 2016
  • Coqueret G. and T. Guida, Machine Learning for Factor Investing, 2021


QARM I (useful but not compulsory). Good command of Matlab and/or Python.


First attempt

Written 1h00 hours

The evaluation is based on a project (70%) and a written exam (30%).

For the project, a list of topics related to the course will be available where students will be requested to apply techniques covered in the course. The project will be done in small groups and will last for the whole semester. Grades for the project will take into account the final report, the code to run the strategies and a presentation, done in front of the class.


Written 1h00 hours

The evaluation is based on a combination of a project (70%) and a written exam (30%).

For the project, the retake will include a resubmission of the project, with amended report and code and a new presentation.

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