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Course

Quantitative Risk Management in Python

AdvancedSkill Level

4.8+

Updated 04/2023

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

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PythonApplied Finance4 hr15 videos54 Exercises4,500 XP17,368Statement of Accomplishment

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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

Introduction to Portfolio Analysis in Python

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.

Portfolio returns during the crisis

Asset covariance and portfolio volatility

Risk factors and the financial crisis

Frequency resampling primer

Visualizing risk factor correlation

Least-squares factor model

Modern portfolio theory

Practice with PyPortfolioOpt: returns

Practice with PyPortfolioOpt: covariance

Breaking down the financial crisis

The efficient frontier and the financial crisis

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.

Measuring Risk

VaR for the Normal distribution

Comparing CVaR and VaR

Which risk measure is "better"?

Risk exposure and loss

What's your risk appetite?

VaR and risk exposure

CVaR and risk exposure

Risk management using VaR & CVaR

VaR from a fitted distribution

Minimizing CVaR

CVaR risk management and the crisis

Portfolio hedging: offsetting risk

Black-Scholes options pricing

Options pricing and the underlying asset

Using options for hedging

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.

Parametric Estimation

Parameter estimation: Normal

Parameter estimation: Skewed Normal

Historical and Monte Carlo Simulation

Historical Simulation

Monte Carlo Simulation

Structural breaks

Crisis structural break: I

Crisis structural break: II

Crisis structural break: III

Volatility and extreme values

Volatility and structural breaks

Extreme values and backtesting

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.

Extreme value theory

Block maxima

Extreme events during the crisis

GEV risk estimation

Kernel density estimation

KDE of a loss distribution

Which distribution?

CVaR and loss cover selection

Neural network risk management

Single layer neural networks

Asset price prediction

Real-time risk management

Wrap-up and Future Steps

Quantitative Risk Management in Python

Course
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FAQs

What financial crisis context does this course use?

The course examines the 2007 to 2008 financial crisis and its effects on banks like Goldman Sachs and J.P. Morgan, using this context to teach risk measurement and mitigation techniques.

What risk measures will I learn to calculate in Python?

You learn to compute Value at Risk and Conditional Value at Risk using pandas, scipy, and pypfopt. You also learn the Black-Scholes model for hedging an options portfolio.

Does the course cover Monte Carlo simulation?

Yes. Chapter 3 teaches you to use Monte Carlo simulation to predict uncertainty and estimate risk, alongside methods for identifying structural breaks in financial data.

Are neural networks used in this risk management course?

Yes. Chapter 4 introduces neural networks for approximating loss distributions and conducting real-time portfolio optimization as part of advanced risk management techniques.

How many Python prerequisites does this course require?

Seven prerequisites are listed, including Intermediate Python for Finance, Introduction to Portfolio Analysis in Python, and Manipulating Time Series Data in Python. Strong Python and finance foundations are needed.

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