# Python을 활용한 정량적 리스크 관리
This is a DataCamp course: 2008년 금융위기에 적용된 리스크 관리와 VaR 등을 Python으로 학습합니다.
## Course Details
- **Duration:** ~4h
- **Level:** Advanced
- **Instructor:** Jamsheed Shorish
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Applied Finance, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Introduction to Portfolio Analysis in Python
## Learning Outcomes
- Python
- Applied Finance
- Data Science and Analytics
- Python을 활용한 정량적 리스크 관리
## Traditional Course Outline
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 pypfopt. You’ll also learn how to mitigate risk exposure using the Black-Scholes model to hedge an options portfolio.
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.
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.
## Resources and Related Learning
**Resources:** IBM stock price (dataset), GE stock price (dataset), Crisis Portfolio (dataset), Mortgage Delinquency (dataset)
**Related tracks:** 응용 금융 파이썬에서
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Introduction to Portfolio Analysis in Python1
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 pypfopt. You’ll also learn how to mitigate risk exposure using the Black-Scholes model to hedge an options portfolio.
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.
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.
Python을 활용한 정량적 리스크 관리
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