This answer follows up on another threadmy answer whichin Bias and variance in leave-one-out vs K-fold cross validation that discusses why LOOCV does not always lead to higher variance. Following a similar approach, I will attempt to highlight a case where LOOCV does lead to higher variance in the presence of outliers and an "unstable model".
Repeating the experiment from the previous thread (see here), we now introduce a certain ratio of outliers in the data set. In particular:
Performing the simulation as previously and plotting the resulting average MSE and variance of the MSE gives results very similar to experimentExperiment 2 of the Bengio and GrandvaletBengio & Grandvalet 2004 paper.
(see the linked paper for explanation of the last figure)
Quoting Yves Grandvalet's answerYves Grandvalet's answer on the other thread: