Quantitative Risk Management in Python

Colleagues, the “Quantitative Risk Management in Python” program will introduce 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. Skill-based training modules address: 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, 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 pyplot. You’ll also learn how to mitigate risk exposure using the Black-Scholes model to hedge an options portfolio, 3) Estimating and identifying risk - 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 - 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. Discover how neural networks can be implemented to approximate loss distributions and conduct real-time portfolio optimization.

Enroll today at (teams & execs welcome): https://datacamp.pxf.io/kOo2aV   

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