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试用DataCamp for Business课程描述
先决条件
Time Series Analysis in Python1
GARCH Model Fundamentals
What are GARCH models, what are they used for, and how can you implement them in Python? After completing this first chapter you’ll be able to confidently answer all these questions.
2
GARCH Model Configuration
A normal GARCH model is not representative of the real financial data, whose distributions frequently exhibit fat tails, skewness, and asymmetric shocks. In this chapter, you’ll learn how to define better GARCH models with more realistic assumptions. You’ll also learn how to make more sophisticated volatility forecasts with rolling window approaches.
3
Model Performance Evaluation
This chapter introduces you to the KISS principle of data science modeling. You’ll learn how to use p-values and t-statistics to simplify model configuration, use ACF plot, Ljung-Box test to verify model assumptions and use likelihood and information criteria for model selection.
4
GARCH in Action
In this final chapter, you’ll learn how to apply the GARCH models you’ve previously learned to practical financial world scenarios. You’ll develop your skills as you become more familiar with VaR in risk management, dynamic covariance in asset allocation, and dynamic Beta in portfolio management.
GARCH Models in Python
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