1. Wrap-up and Future Steps
Congratulations! You've successfully completed this introduction to quantitative risk management, using tools from the Python programming environment and applications drawn from the global financial crisis of 2007 - 2009.
2. Congratulations!
In the first part of the course, we reviewed the concepts of risk and return. We examined risk factors, volatility and applications of Modern Portfolio Theory such as the Efficient Frontier.
3. Congratulations!
In the second chapter, we looked at risk measures such as Value at Risk and Conditional Value at Risk, and touched upon portfolio hedging.
4. Congratulations!
Chapter 3 introduced estimation techniques, such as parametric estimation, historical simulation and Monte Carlo simulation, while emphasizing how structural breaks and extreme events can challenge prior assumptions.
5. Congratulations!
Finally, Chapter 4 provided an overview of advanced technologies, such as extreme value theory, kernel density estimation, and analysis using neural networks.
6. Tools in your toolkit
Throughout the course, the concepts introduced were supported by existing tools in Python's data science ecosystem. We covered a great deal, as you can see! But even so, we've only scratched the surface of what Python has to offer.
7. Future steps and reference
To extend what you've learned, keep an eye out for existing and upcoming courses from DataCamp.
You can also find these and other topics covered in a reference devoted to quantitative risk management.
8. Best of luck on your data science journey!
It was my sincere pleasure to act as your guide throughout this course, and all the best to you on your data science journey!