Congratulations
1. Congratulations! You have learned three languages
Congratulations! You have made it to the end of the course on GARCH models in R. In this course you have learned two languages.2. Language of GARCH models
First, the language of GARCH models with the central concept of time-varying volatility sigma_t and its behavior to cluster over time. You learned about the information set available to make predictions using assumptions on the mean, variance and distribution. A notable example is the GJR GARCH model and the skewed student t distribution. You also learned about model validation using the MSE, significance testing, standardized returns and the Ljung-Box test. Finally, you have seen several practical applications such as the estimation of value at risk, dynamic beta calculation and optimization of financial portfolios.3. Language of rugarch
The rugarch package lets you apply the garch languange in the R language using its own syntax. The key functions are ugarchspec, ugarchfit, ugarchroll, ugarchforecast, ugarchfilter and ugarchpath. The output of these functions can be analyzed through specific methods such as the methods sigma, fitted, coef, infocriteria, likelihood, setfixed, setbounds and quantile, among others.4. @OptimizeRisk
The advantage of speaking both the GARCH model language and the rugarch language is that you can now measure the risks of your financial decisions. This should help you in optimizing them by taking only the risks that are worthwhile to take.Create Your Free Account
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