GARCH Models in Python

Starting at USD 39.00
4 hours duration

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Volatility is a fundamental concept in finance, and GARCH models in Python have emerged as a popular tool for predicting changes in variance, particularly in time-series data that exhibit time-dependency. This comprehensive course aims to equip you with the knowledge and skills necessary to effectively implement GARCH models, determine appropriate model assumptions, and generate accurate volatility forecasts. By utilizing real-world data, such as historical Tesla stock prices, you will gain practical experience in quantifying portfolio risks through calculations of Value-at-Risk, covariance, and stock Beta. Furthermore, you will have the opportunity to apply these techniques to a diverse range of assets, including stocks, indices, cryptocurrencies, and foreign exchange, enabling you to confidently utilize GARCH models in your future endeavors.

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DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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