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Abstract: The conditional tail expectation in risk analysis describes the expected amount of risk that can be experienced given that a potential risk exceeds a threshold value, and provides an important measure for right-tail risk. In this paper, we study the convolution and extreme values of dependent risks that follow a multivariate phase type distribution, and derive explicit formulas of several conditional tail expectations of the convolution and extreme values for such dependent risks. Utilizing the underly-ing Markovian property of these distributions, our method not only reveals structural insight, but also yields some new distributional properties for multivariate phase type distributions