Welcome to the course!

Quantitative Risk Management in R

Alexander McNeil

Professor, University of York

About me

  • Professor in mathematical statistics, actuarial science, and quantitative finance
  • Author of Quantitative Risk Management: Concepts, Techniques & Tools with R. Frey and P. Embrechts
  • Creator of qrmtutorial.org with M. Hofert
  • Contributor to R packages including qrmdata and qrmtools

Quantitative Risk Management in R

The objective of QRM

  • In quantitative risk management (QRM), we quantify the risk of a portfolio
  • Measuring risk is first step towards managing risk
  • Managing risk:
    • Selling assets, diversifying portfolios, implementing hedging with derivatives
    • Maintaining sufficient capital to withstand losses
  • Value-at-risk (VaR) is a well-known measure of risk
Quantitative Risk Management in R

Risk factors

  • Value of a portfolio depends on many risk factors
  • Examples: equity indexes/prices, FX rates, interest rates
  • Let's look at the S&P 500 index
Quantitative Risk Management in R

Analyzing risk factors with R

library(qrmdata)

data(SP500)

head(SP500,  n = 3)

           ^GSPC
1950-01-03 16.66
1950-01-04 16.85
1950-01-05 16.93

> tail(SP500, n = 3)

             ^GSPC
2015-12-29 2078.36
2015-12-30 2063.36
2015-12-31 2043.94
Quantitative Risk Management in R

Plotting risk factors

plot(SP500)

Quantitative Risk Management in R

Let's practice!

Quantitative Risk Management in R

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