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