I need help answering these questions. I am a doctoral student at a university, and I am advising a bachelor student's thesis on robust optimization of portfolios. Unfortunately, I was unable to help him answer these questions and my superior professor was too busy. Here are the questions:
1.) what are the conditions required to use Shrinkage estimators for example to solve the problem of covariance matrix ?
2- Which models are better for non-normal data and which models are better for normal data ?
3- If we have difference between error term of mean and error term of covariance matrix ,so we have 3 assumptions to solve :
a) minimise error in mean separately b) maximise error in co-variance matrix separately c) minimise mean and maximise co-variance matrix in same time
How can you implement these assumptions precisely
4- what are the conditions to use robust estimation and robust optimization ?
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from Toyama, Japan