  XVAs denote various counterparty risk related valuation adjustments that are
applied to financial derivatives since the 2007--09 crisis. We root a
cost-of-capital XVA strategy in a balance sheet perspective which is key in
identifying the economic meaning of the XVA terms. Our approach is first
detailed in a static setup that is solved explicitly. It is then plugged in the
dynamic and trade incremental context of a real derivative banking portfolio.
The corresponding cost-of-capital XVA strategy ensures to bank shareholders a
submartingale equity process corresponding to a target hurdle rate on their
capital at risk, consistently between and throughout deals. Set on a
forward/backward SDE formulation, this strategy can be solved efficiently using
GPU computing combined with deep learning regression methods in a whole bank
balance sheet context. A numerical case study emphasizes the workability and
added value of the ensuing pathwise XVA computations.
