Course Summary
This course teaches financial risk management concepts and techniques using the R programming language. It covers topics such as value-at-risk, stress testing, Monte Carlo simulation, and portfolio optimization.Key Learning Points
- Learn how to use R programming language to implement financial risk management strategies
- Understand the concepts of value-at-risk, stress testing, and Monte Carlo simulation
- Gain knowledge on portfolio optimization techniques
Related Topics for further study
Learning Outcomes
- Develop proficiency in R programming language
- Analyze and manage financial risk using various techniques
- Optimize portfolios for maximum returns
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of finance
- Basic programming knowledge
Course Difficulty Level
IntermediateCourse Format
- Online Self-paced
- Video Lectures
- Quizzes
- Assignments
Similar Courses
- Financial Engineering and Risk Management Part I
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Related Education Paths
Related Books
Description
This course teaches you how to calculate the return of a portfolio of securities as well as quantify the market risk of that portfolio, an important skill for financial market analysts in banks, hedge funds, insurance companies, and other financial services and investment firms. Using the R programming language with Microsoft Open R and RStudio, you will use the two main tools for calculating the market risk of stock portfolios: Value-at-Risk (VaR) and Expected Shortfall (ES). You will need a beginner-level understanding of R programming to complete the assignments of this course.
Outline
- Introduction to R, Data Retrieval, and Return Calculation
- Introduction
- Retrieving Data from FRED
- Calculating Daily Returns
- Calculating Longer Returns
- A Simple Example
- Exercise 1 - Introduction to Microsoft Open R and R Studio
- Week 1 Quiz Instructions
- Exercise 2 - Retrieving data from FRED
- Exercise 3 - Calculating Returns on Gold
- Exercise 4 - Longer Horizon Returns of Gold
- Week 1 Quiz (1 of 4)
- Week 1 Quiz (2 of 4)
- Week 1 Quiz (3 of 4)
- Week 1 Quiz (4 of 4)
- Risk Management under Normal Distributions
- Distribution of Returns
- Value-at-Risk (VaR)
- Expected Shortfall (ES)
- Using Simulation to Estimate VaR and ES
- Week 2 Quiz Instructions
- Exercise 5 - Estimating Parameters of the Normal Distribution
- Exercise 6 - Estimating VaR of the Normal Distribution
- Exercise 7 - Estimating ES of the Normal Distribution
- Exercise 8 - Estimating VaR and ES via Simulation
- Week 2 Quiz (1 of 4)
- Week 2 Quiz (2 of 4)
- Week 2 Quiz (3 of 4)
- Week 2 Quiz (4 of 4)
- Risk Management under Non-normal Distributions
- Non-normal Distributions
- Student-t Distribution
- Rescaled t Distribution Model
- VaR and ES for Multi-day Horizon
- Week 3 Quiz Instructions
- Exercise 9 - Skewness, Kurtosis, Jarque-Bera Test for Normality
- Exercise 10 - Estimate Parameters of the Scaled Student-t Distribution
- Exercise 11 - Estimate VaR and ES at 10-day Horizon
- Week 3 Quiz (1 of 4)
- Week 3 Quiz (2 of 4)
- Week 3 Quiz (3 of 4)
- Week 3 Quiz (4 of 4)
- Risk Management under Volatility Clustering
- Future vs Historical Distribution
- Volatility Clustering
- GARCH
- Estimation: rugarch Package
- GARCH(1,1) - t
- Diagnostic Tests
- Using the ugarchboot Function
- Using the ugarchroll Function
- Course Summary
- Week 4 Quiz Instructions
- Exercise 12 - Serial Correlation, Volatility Clustering, GARCH
- Exercise 13 - VaR and ES for GARCH bootstrap
- Week 4 Quiz (1 of 4)
- Week 4 Quiz (2 of 4)
- Week 4 Quiz (3 of 4)
- Week 4 Quiz (4 of 4)
Summary of User Reviews
Financial Risk Management with R is a popular course on Coursera that covers various topics related to financial risk management. Users have given it positive reviews overall, with many praising its practical approach and comprehensive coverage of the subject.Key Aspect Users Liked About This Course
The course is praised for its practical approach to financial risk management.Pros from User Reviews
- Comprehensive coverage of financial risk management topics
- Real-world examples and exercises for practical application
- Easy to follow lectures and clear explanations
- Great for beginners and experienced professionals alike
Cons from User Reviews
- Some users may find the course too basic
- Not enough emphasis on advanced statistical techniques
- Limited interaction with instructors and peers
- Some technical issues with the platform
David Hsieh
4.5 Raiting
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