unhindered thinking
In their paper on GARCH model comparison, Hansen and Lunde (2005) present evidence that among 330 different models, and using daily data on the DM/$ rate and IBM stock returns, no model does significantly better at predicting volatility (based on a … [Continue reading]
This short demonstration illustrates the use of the DCC model and its methods using the rmgarch package, and in particular an alternative method for 2-stage DCC estimation in the presence of the MVT distribution shape (nuisance) parameter. The … [Continue reading]
The rugarch package contains a rolling volatility forecast function called ugarchroll, but in this example I will show how easy it is to create a quick custom function. Having a rolling forecast of volatility can prove an invaluable indicator for use … [Continue reading]
Despite its shortcomings, Value at Risk (VaR) is still the most widely used measure for measuring the risk of a portfolio, and the preferred measure in the Basel II Accord. In this demonstration, I backtest a group of indices using a GARCH-DCC(MVT) … [Continue reading]
A frequently asked question relates to the minimum size of a dataset, required to obtain 'good' GARCH estimates. In this demonstration, the ugarchdistribution function is used to show how this question can be addressed within the rugarch package and … [Continue reading]