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Overview
Context
Volatility is an essential concept in finance, which is why GARCH models in Python are a popular choice for forecasting changes in variance, specifically when working with time-series data that are time-dependant.
This GARCH Models in Python course offered by Data Camp will show you how and when to implement GARCH models, how to specify model assumptions, and how to make volatility forecasts and evaluate model performance. Using real-world data, including historical Tesla stock prices, you’ll gain hands-on experience of how to better quantify portfolio risks, through calculations of Value-at-Risk, covariance, and stock Beta.
You’ll also apply what you’ve learned to a wide range of assets, including stocks, indices, cryptocurrencies, and foreign exchange, preparing you to go forth and use GARCH models.
Programme Structure
Chapters
- GARCH Model
- GARCH Model Configuration
- Model Performance Evaluation
- GARCH in Action
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Financial Technology View 64 other Short Courses in Financial Technology in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
PREREQUISITES
- Time Series Analysis in Python
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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