After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
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 XP
- Exploring risk-factor time series: equity indexes100 XP
- Exploring risk-factor time series: individual equities100 XP
- Exploring risk-factor data: exchange rates100 XP
- Risk-factor returns50 XP
- Exploring return series100 XP
- Different ways of plotting risk-factor and return series100 XP
- Aggregating log-returns50 XP
- Aggregating log-return series100 XP
- A test on aggregation of log-returns50 XP
- Exploring other kinds of risk factors50 XP
- Commodities data100 XP
- Interest-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.
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.
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.
R
Quantitative Risk Management in R
Course
Complete
