Skip to main content
HomeR

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 exercises14,454 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

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.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Applied Finance in R

Go To Track

Quantitative Analyst in R

Go To Track
  1. 1

    Exploring Market Risk-Factor Data

    Free

    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
  2. 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.

    Play Chapter Now
    The normal distribution
    50 xp
    Graphical methods for assessing normality
    100 xp
    Testing for normality
    50 xp
    Q-Q plots for assessing normality
    100 xp
    Skewness, kurtosis and the Jarque-Bera test
    50 xp
    Numerical tests of normality
    100 xp
    Testing normality for longer time horizons
    100 xp
    Overlapping returns
    100 xp
    Reviewing knowledge of normal distributions and returns
    50 xp
    The Student t distribution
    50 xp
    Fitting t distribution to data
    100 xp
    Testing FX returns for normality
    100 xp
    Testing interest-rate returns for normality
    100 xp
    Testing gold price returns for normality
    50 xp
  3. 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.

    Play Chapter Now
    Characteristics of volatile return series
    50 xp
    Spotting a volatile time series
    100 xp
    Estimating serial correlations
    50 xp
    Using acf plots to reveal volatility
    100 xp
    The Ljung-Box test
    50 xp
    Applying Ljung-Box tests to return data
    100 xp
    Applying Ljung-Box tests to longer-interval returns
    100 xp
    Looking at the extremes in volatile return series
    50 xp
    Extreme values in volatile time series
    100 xp
    Cross correlations between risk-factor return series
    100 xp
    The stylized facts of return series
    50 xp
    Volatility and correlation of FX returns
    100 xp
    Volatility and correlation of interest-rate data
    100 xp
    Reviewing knowledge of volatility and correlation
    50 xp
  4. 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.

    Play Chapter Now
    Value-at-risk and expected shortfall
    50 xp
    Computing VaR and ES for normal distribution
    100 xp
    International equity portfolio
    50 xp
    Examining risk factors for international equity portfolio
    100 xp
    Historical simulation
    100 xp
    Estimating VaR and ES
    100 xp
    Option portfolio and Black Scholes
    50 xp
    Compute Black-Scholes price of an option
    100 xp
    Equity and implied volatility risk factors
    100 xp
    Historical simulation for the option example
    50 xp
    Historical simulation of losses for option portfolio
    100 xp
    Estimating VaR and ES for option portfolio
    100 xp
    Computing VaR for weekly losses
    50 xp
    Wrap-up
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Applied Finance in R

Go To Track

Quantitative Analyst in R

Go To Track

collaborators

Collaborator's avatar
Lore Dirick

prerequisites

Manipulating Time Series Data in R
Alexander J. McNeil HeadshotAlexander J. McNeil

Professor of Actuarial Science at the University of York.

See More

What do other learners have to say?

    Join over 15 million learners and start Quantitative Risk Management in R today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.