GARCH Models in Python
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
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Course Description
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.
- 1
GARCH Model Fundamentals
FreeWhat 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.
Why do we need GARCH models50 xpUnderstand volatility50 xpObserve volatility clustering100 xpCalculate volatility100 xpWhat are ARCH and GARCH50 xpReview GARCH model basics50 xpSimulate ARCH and GARCH series100 xpObserve the impact of model parameters100 xpHow to implement GARCH models in Python50 xpReview "arch" documentation50 xpImplement a basic GARCH model100 xpMake forecast with GARCH models100 xp - 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.
Distribution assumptions50 xpFat tails and skewness50 xpPlot distribution of standardized residuals100 xpFit a GARCH with skewed t-distribution100 xpMean model specifications50 xpCheck mean model assumptions50 xpEffect of mean model on volatility predictions100 xpVolatility models for asymmetric shocks50 xpModeling of asymmetric responses of volatility50 xpFit GARCH models to cryptocurrency100 xpCompare GJR-GARCH with EGARCH100 xpGARCH rolling window forecast50 xpWhy use rolling window forecast50 xpFixed rolling window forecast100 xpCompare forecast results100 xp - 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.
Significance testing of model parameters50 xpKeep it simple stupid50 xpSimplify the model with p-values100 xpSimplify the model with t-statistics100 xpValidation of GARCH model assumptions50 xpDetect autocorrelations50 xpACF plot100 xpLjung-Box test100 xpGoodness of fit measures50 xpGoodness of fit basics50 xpPick a winner based on log-likelihood100 xpPick a winner based on AIC/BIC100 xpGARCH model backtesting50 xpBacktesting basics50 xpBacktesting with MAE, MSE100 xp - 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.
VaR in financial risk management50 xpVaR concept50 xpCompute parametric VaR100 xpCompute empirical VaR100 xpDynamic covariance in portfolio optimization50 xpCovariance concept50 xpCompute GARCH covariance100 xpCompute dynamic portfolio variance100 xpDynamic Beta in portfolio management50 xpBeta concept50 xpCompute dynamic stock Beta100 xpCongratulations!50 xp
In the following tracks
Applied Finance
Adel Nehme
Amy PetersonPrerequisites
Time Series Analysis in Python
Chelsea Yang
Data Science Instructor
Chelsea is a senior quantitative analyst with over a decade’s experience working for top asset managers and financial institutions. She is a data science enthusiast and passionate about its application in finance. She has expertise in financial modeling, risk management, and machine learning. Chelsea holds a Master's degree in Management Information Systems from Carnegie Mellon University. In her spare time, she enjoys writing Python programs to test her trading ideas.
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Lloyds Banking Group
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Harvard Business School
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Decision Science Analytics, USAA
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