Quantitative Risk Management in R

Offered byDataCamp

Quantitative Risk Management in R
at
DataCamp
Overview

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

Duration	5 hours
Total fee	Free
Mode of learning	Online
Schedule type	Self paced
Official Website	Explore Free Course
Credential	Certificate

Quantitative Risk Management in R
at
DataCamp
Highlights

Community and Support
Provides video lectures, reading materials, code examples, and data sets
Highly Experienced Instructors

Quantitative Risk Management in R
at
DataCamp
Course details

Skills you will learn

Risk Management R Simulation Software Testing

More about this course

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

Quantitative Risk Management in R
at
DataCamp
Curriculum

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

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

Real world returns are volatile and correlated

In this chapter, you will learn about volatility and how to detect it using act plots.

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.