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Quantitative Risk Management in R

基礎スキルレベル

更新日 2026/01

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

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RApplied Finance5時間18 ビデオ55 演習4,350 XP15,854達成証明書

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コース説明

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.

前提条件

Manipulating Time Series Data in R

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.

Welcome to the course!

Exploring risk-factor time series: equity indexes

Exploring risk-factor time series: individual equities

Exploring risk-factor data: exchange rates

Risk-factor returns

Exploring return series

Different ways of plotting risk-factor and return series

Aggregating log-returns

Aggregating log-return series

A test on aggregation of log-returns

Exploring other kinds of risk factors

Commodities data

Interest-rate data

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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 distribution

Graphical methods for assessing normality

Testing for normality

Q-Q plots for assessing normality

Skewness, kurtosis and the Jarque-Bera test

Numerical tests of normality

Testing normality for longer time horizons

Overlapping returns

Reviewing knowledge of normal distributions and returns

The Student t distribution

Fitting t distribution to data

Testing FX returns for normality

Testing interest-rate returns for normality

Testing gold price returns for normality

チャプター開始

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 series

Spotting a volatile time series

Estimating serial correlations

Using acf plots to reveal volatility

The Ljung-Box test

Applying Ljung-Box tests to return data

Applying Ljung-Box tests to longer-interval returns

Looking at the extremes in volatile return series

Extreme values in volatile time series

Cross correlations between risk-factor return series

The stylized facts of return series

Volatility and correlation of FX returns

Volatility and correlation of interest-rate data

Reviewing knowledge of volatility and correlation

チャプター開始

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 shortfall

Computing VaR and ES for normal distribution

International equity portfolio

Examining risk factors for international equity portfolio

Historical simulation

Estimating VaR and ES

Option portfolio and Black Scholes

Compute Black-Scholes price of an option

Equity and implied volatility risk factors

Historical simulation for the option example

Historical simulation of losses for option portfolio

Estimating VaR and ES for option portfolio

Computing VaR for weekly losses

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Quantitative Risk Management in R

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