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Quantitative Risk Management in R

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

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5 Hours18 Videos55 Exercises
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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

    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.

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    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 Finance in RQuantitative Analyst with R

Collaborators

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Lore Dirick
Alexander J. McNeil HeadshotAlexander J. McNeil

Professor of Actuarial Science at the University of York.

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