This course is part of these tracks:

Alexander J. McNeil
Alexander J. McNeil

Professor of Actuarial Science at the University of York.

Alexander McNeil has been Professor of Actuarial Science at the University of York since September 2016. He is joint author, together with Rüdiger Frey and Paul Embrechts, of the book "Quantitative Risk Management: Concepts, Techniques and Tools", published by Princeton University Press (2015). He is also an Honorary Fellow of the Institute and Faculty of Actuaries and a Corresponding Member of the Swiss Association of Actuaries.

See More
  • Lore Dirick

    Lore Dirick

Course Description

In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called "stylized facts" of these data - including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

  1. 1

    Exploring market risk-factor data


    In this chapter, you will learn how to form return series, aggregate them over longer periods and plot them in different ways. You will look at examples using the qrmdata package.

  2. Real world returns are riskier than normal

    In this chapter, you will learn about graphical and numerical tests of normality, apply them to different datasets, and consider the alternative Student t model.

  3. Real world returns are volatile and correlated

    In this chapter, you will learn about volatility and how to detect it using act plots. You will learn how to apply Ljung-Box tests for serial correlation and estimate cross correlations.

  4. Estimating portfolio value-at-risk (VaR)

    In this chapter, the concept of value-at-risk and simple methods of estimating VaR based on historical simulation are introduced.