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Introducing common shocks is a popular dependence modelling approach, withsome recent applications in loss reserving. The main advantage of this approachis the ability to capture structural dependence coming from knownrelationships. In addition, it helps with the parsimonious construction ofcorrelation matrices of large dimensions. However, complications arise in thepresence of "unbalanced data", that is, when (expected) magnitude ofobservations over a single triangle, or between triangles, can varysubstantially. Specifically, if a single common shock is applied to all ofthese cells, it can contribute insignificantly to the larger values and/orswamp the smaller ones, unless careful adjustments are made. This problem isfurther complicated in applications involving negative claim amounts. In thispaper, we address this problem in the loss reserving context using a commonshock Tweedie approach for unbalanced data. We show that the solution not onlyprovides a much better balance of the common shock proportions relative to theunbalanced data, but it is also parsimonious. Finally, the common shock Tweediemodel also provides distributional tractability.

On unbalanced data and common shock models in stochastic loss reserving