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Quantitative Risk Management in Python

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

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4 Hours15 Videos54 Exercises
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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.
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In the following Tracks

Applied Finance in Python

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  1. 1

    Risk and return recap

    Free

    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.

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    Welcome!
    50 xp
    Portfolio returns during the crisis
    100 xp
    Asset covariance and portfolio volatility
    100 xp
    Risk factors and the financial crisis
    50 xp
    Frequency resampling primer
    100 xp
    Visualizing risk factor correlation
    100 xp
    Least-squares factor model
    100 xp
    Modern portfolio theory
    50 xp
    Practice with PyPortfolioOpt: returns
    100 xp
    Practice with PyPortfolioOpt: covariance
    100 xp
    Breaking down the financial crisis
    100 xp
    The efficient frontier and the financial crisis
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following Tracks

Applied Finance in Python

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Datasets

IBM stock priceGE stock priceCrisis PortfolioMortgage Delinquency

Collaborators

Collaborator's avatar
Adel Nehme
Jamsheed Shorish HeadshotJamsheed Shorish

Computational Economist

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