# 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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## 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
### 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.

Welcome to the course!50 xpExploring risk-factor time series: equity indexes100 xpExploring risk-factor time series: individual equities100 xpExploring risk-factor data: exchange rates100 xpRisk-factor returns50 xpExploring return series100 xpDifferent ways of plotting risk-factor and return series100 xpAggregating log-returns50 xpAggregating log-return series100 xpA test on aggregation of log-returns50 xpExploring other kinds of risk factors50 xpCommodities data100 xpInterest-rate data100 xp - 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.

The normal distribution50 xpGraphical methods for assessing normality100 xpTesting for normality50 xpQ-Q plots for assessing normality100 xpSkewness, kurtosis and the Jarque-Bera test50 xpNumerical tests of normality100 xpTesting normality for longer time horizons100 xpOverlapping returns100 xpReviewing knowledge of normal distributions and returns50 xpThe Student t distribution50 xpFitting t distribution to data100 xpTesting FX returns for normality100 xpTesting interest-rate returns for normality100 xpTesting gold price returns for normality50 xp - 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.

Characteristics of volatile return series50 xpSpotting a volatile time series100 xpEstimating serial correlations50 xpUsing acf plots to reveal volatility100 xpThe Ljung-Box test50 xpApplying Ljung-Box tests to return data100 xpApplying Ljung-Box tests to longer-interval returns100 xpLooking at the extremes in volatile return series50 xpExtreme values in volatile time series100 xpCross correlations between risk-factor return series100 xpThe stylized facts of return series50 xpVolatility and correlation of FX returns100 xpVolatility and correlation of interest-rate data100 xpReviewing knowledge of volatility and correlation50 xp - 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.

Value-at-risk and expected shortfall50 xpComputing VaR and ES for normal distribution100 xpInternational equity portfolio50 xpExamining risk factors for international equity portfolio100 xpHistorical simulation100 xpEstimating VaR and ES100 xpOption portfolio and Black Scholes50 xpCompute Black-Scholes price of an option100 xpEquity and implied volatility risk factors100 xpHistorical simulation for the option example50 xpHistorical simulation of losses for option portfolio100 xpEstimating VaR and ES for option portfolio100 xpComputing VaR for weekly losses50 xpWrap-up50 xp

Collaborators

Prerequisites

Manipulating Time Series Data with xts and zoo in RAlexander J. McNeil

See MoreProfessor of Actuarial Science at the University of York.

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