课程

Quantitative Risk Management in R

基础技能水平

更新时间 2026年1月

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

免费开始课程

RApplied Finance5 小时18 视频55 练习4,350 经验值15,854成就声明

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

深受数千家公司学习者的喜爱

培训2人或更多？

试用DataCamp for Business

课程描述

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.

先决条件

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!

50 经验值

Exploring risk-factor time series: equity indexes

100 经验值

Exploring risk-factor time series: individual equities

100 经验值

Exploring risk-factor data: exchange rates

100 经验值

Risk-factor returns

50 经验值

Exploring return series

100 经验值

Different ways of plotting risk-factor and return series

100 经验值

Aggregating log-returns

50 经验值

Aggregating log-return series

100 经验值

A test on aggregation of log-returns

50 经验值

Exploring other kinds of risk factors

50 经验值

Commodities data

100 经验值

Interest-rate data

100 经验值

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

50 经验值

Graphical methods for assessing normality

100 经验值

Testing for normality

50 经验值

Q-Q plots for assessing normality

100 经验值

Skewness, kurtosis and the Jarque-Bera test

50 经验值

Numerical tests of normality

100 经验值

Testing normality for longer time horizons

100 经验值

Overlapping returns

100 经验值

Reviewing knowledge of normal distributions and returns

50 经验值

The Student t distribution

50 经验值

Fitting t distribution to data

100 经验值

Testing FX returns for normality

100 经验值

Testing interest-rate returns for normality

100 经验值

Testing gold price returns for normality

50 经验值

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

50 经验值

Spotting a volatile time series

100 经验值

Estimating serial correlations

50 经验值

Using acf plots to reveal volatility

100 经验值

The Ljung-Box test

50 经验值

Applying Ljung-Box tests to return data

100 经验值

Applying Ljung-Box tests to longer-interval returns

100 经验值

Looking at the extremes in volatile return series

50 经验值

Extreme values in volatile time series

100 经验值

Cross correlations between risk-factor return series

100 经验值

The stylized facts of return series

50 经验值

Volatility and correlation of FX returns

100 经验值

Volatility and correlation of interest-rate data

100 经验值

Reviewing knowledge of volatility and correlation

50 经验值

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

50 经验值

Computing VaR and ES for normal distribution

100 经验值

International equity portfolio

50 经验值

Examining risk factors for international equity portfolio

100 经验值

Historical simulation

100 经验值

Estimating VaR and ES

100 经验值

Option portfolio and Black Scholes

50 经验值

Compute Black-Scholes price of an option

100 经验值

Equity and implied volatility risk factors

100 经验值

Historical simulation for the option example

50 经验值

Historical simulation of losses for option portfolio

100 经验值

Estimating VaR and ES for option portfolio

100 经验值

Computing VaR for weekly losses

50 经验值

50 经验值

Quantitative Risk Management in R

课程完成

获得成就证明

将此证书添加到你的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Quantitative Risk Management in R！

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。