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.