Write and . Their common expected value is . Expanding about the retention gives the requested variance identity
The stop loss variance minimization principle follows from a pointwise comparison. For , the constraint implies
For , the squared distance of from is zero, so the same squared-distance comparison is immediate. Therefore
Subtracting the same proves optimality of the retained stop loss payout:
Since is bounded, its variance is finite; if , the inequality remains valid with infinite variance on the left. When it is finite, equality requires almost surely, because the pointwise squared-distance inequality is strict whenever the two payouts differ.

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