by Patrick Burns.
Abstract: The estimation of value at risk using univariate GARCH models is examined. A long history of the S&P 500 is used to compare these estimators with several other common approaches to value at risk estimation. The test results indicate that GARCH estimates are superior to the other methods in terms of the accuracy and consistency of the probability level. Although all of the GARCH models tested performed relatively well, the quality of the value at risk estimate does depend on which particular GARCH model is used. Weighting recent observations more heavily when fitting the GARCH model seems to be beneficial.
jun 25, 26
The email address patrick@burns-stat.com was out of action for a few hours today. It is back now.
jun 25, 26
Customization in R. Basics Several features benefit from being customizable — either because of personal taste or specifics of the environment. The way R implements this flexibility is through the [...]
jun 25, 26
How to control the limits of data values in R plots. R has multiple graphics engines. Here we will talk about the base graphics and the ggplot2 package. We’ll create [...]