After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Managing risk using Quantitative Risk Management is a vital task across the banking, insurance, and asset management industries. It’s essential that financial risk analysts, regulators, and actuaries can quantitatively balance rewards against their exposure to risk.This course introduces you to financial portfolio risk management through an examination of the 2007—2008 financial crisis and its effect on investment banks such as Goldman Sachs and J.P. Morgan. You’ll learn how to use Python to calculate and mitigate risk exposure using the Value at Risk and Conditional Value at Risk measures, estimate risk with techniques like Monte Carlo simulation, and use cutting-edge technologies such as neural networks to conduct real time portfolio rebalancing.
Prerequisites
Curriculum
Course outline
1
Risk and return recap
Risk management begins with an understanding of risk and return. We’ll recap how risk and return are related to each other, identify risk factors, and use them to re-acquaint ourselves with Modern Portfolio Theory applied to the global financial crisis of 2007-2008.
- Welcome!50 XP
- Portfolio returns during the crisis100 XP
- Asset covariance and portfolio volatility100 XP
- Risk factors and the financial crisis50 XP
- Frequency resampling primer100 XP
- Visualizing risk factor correlation100 XP
- Least-squares factor model100 XP
- Modern portfolio theory50 XP
- Practice with PyPortfolioOpt: returns100 XP
- Practice with PyPortfolioOpt: covariance100 XP
- Breaking down the financial crisis100 XP
- The efficient frontier and the financial crisis100 XP
2
Goal-oriented risk management
Now it’s time to expand your portfolio optimization toolkit with risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). To do this you will use specialized Python libraries including pandas, scipy, and pypfopt. You’ll also learn how to mitigate risk exposure using the Black-Scholes model to hedge an options portfolio.
3
Estimating and identifying risk
In this chapter, you’ll estimate risk measures using parametric estimation and historical real-world data. You'll then discover how Monte Carlo simulation can help you predict uncertainty. Lastly, you’ll learn how the global financial crisis signaled that randomness itself was changing, by understanding structural breaks and how to identify them.
4
Advanced risk management
It's time to explore more general risk management tools. These advanced techniques are pivotal when attempting to understand extreme events, such as losses incurred during the financial crisis, and complicated loss distributions which may defy traditional estimation techniques. You’ll also discover how neural networks can be implemented to approximate loss distributions and conduct real-time portfolio optimization.
Quantitative Risk Management in Python
Course
Complete
