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Quantitative Risk Management in Python
4.8(221)17k studentsADVANCEDEnglish
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Course Overview
The Quantitative Risk Management in Python course delivers advanced instruction on measuring and managing financial portfolio risk. Drawing insights from the 2007–2008 financial crisis and top investment firms like Goldman Sachs and J.P. Morgan, you will implement core quantitative metrics and predictive models using Python.
Who Is This Course For?
This course is ideal for advanced financial risk analysts, regulators, and actuaries working across banking, insurance, and asset management who need to balance returns against risk exposures.
Key Takeaways
- Calculate Value at Risk (VaR) and Conditional Value at Risk (CVaR) in Python.
- Estimate portfolio risk using Monte Carlo simulation techniques.
- Apply neural networks for real-time portfolio rebalancing.
- Analyze historical risk lessons from the 2007–2008 financial crisis.
About this course
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.
Price
Free
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