I know that there are many related threads, packages and papers. Currently I`m reading through many of them. However, I don't plan to dig too deeply into this topic. I need a sound method that works with strongly dependent univariate time series (in large samples). My champion so far is the Kokoszka & Leipus test as evaluated and discussed in this paper
ANDREOU (2002) DETECTING MULTIPLE BREAKS IN FINANCIAL MARKET VOLATILITY DYNAMICS
but there seems to be no implementation of this test in R? Maybe someone knows better?
I've spotted the Inclán and Tiao (1994) test (which is also discussed in this paper) in the
changepoint package. But this method is too sensitive to outliers. It is originally developed for independent data, so additional transformations might be required first.
The following paper also develops an interesting method and offers R code:
Fryzlewicz (2014) Multiple-change-point detection for auto-regressive conditional heteroscedastic processes
Currently I'm also checking the
bcppackage and the
strucchange package (while the latter seems to be made for roughly independent data).
I`m a bit lost between all these alternatives. And due to deadline constraints I have not the time to evaluate all these different possibilities (and many more that I have not listed here).
Is there an R user who can recommend a certain method based on his own experience? The application will be to financial returns for a project at uni.