Skip to main content

Quantitative Risk Management in R

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Start Course for Free
5 Hours18 Videos55 Exercises11,250 Learners4350 XPApplied Finance TrackQuantitative Analyst Track

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

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.

    Play Chapter Now
    Welcome to the course!
    50 xp
    Exploring risk-factor time series: equity indexes
    100 xp
    Exploring risk-factor time series: individual equities
    100 xp
    Exploring risk-factor data: exchange rates
    100 xp
    Risk-factor returns
    50 xp
    Exploring return series
    100 xp
    Different ways of plotting risk-factor and return series
    100 xp
    Aggregating log-returns
    50 xp
    Aggregating log-return series
    100 xp
    A test on aggregation of log-returns
    50 xp
    Exploring other kinds of risk factors
    50 xp
    Commodities data
    100 xp
    Interest-rate data
    100 xp

In the following tracks

Applied FinanceQuantitative Analyst


loreLore Dirick
Alexander J. McNeil Headshot

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

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA