Quantitative Risk Management in Python

Overview

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

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.

Syllabus

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.

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.

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.

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.