The XVA perturbative expansion of BUETGOLFOUSE2026 recovers the classical XVA stack as the leading-order term in an asymptotic expansion of the nonlinear HJB equation. Setting (small risk-aversion) and (small frictions), the XVA adjustment expands as .
Leading order: — Classical XVA stack under P
As : the diffusive quadratic, -gradient cross-terms, and friction terms all vanish. The exponential default term linearises: , contributing to the discount (combined rate , eq. 11).
The BSDE (eq. 12) is linear, and Feynman-Kac with discount factor gives (eq. 13):
plus a carry term from the real-world drift.
First order: — HVA and variance penalties
The first-order source term (eq. 14) contains:
- Diffusion variance: — the continuous-time mean-variance deep-hedging penalty
- Default convexity:
- HVA: — the friction cost of hedging XVA
Key Details
- The three P-world XVA components use (not ), discount at (not ), and take expectations
- For -: CVA and FVA are materially smaller under P — the Q-world over-provisions by the credit-risk premium factor
- The carry term has no Q-world counterpart; it represents incremental alpha from the real-world drift
Critical Notes
Convergence not established
The paper does not discuss whether the perturbative series converges or is merely asymptotic. For BSDE-based expansions with nonlinear drivers (especially the exponential default term), convergence is non-trivial and should not be assumed. The result should be understood as a formal asymptotic expansion.
HVA ordering is scaling-dependent
The claim “HVA is , not ” is a consequence of the specific scaling , . Under alternative parametrisations where friction costs are held fixed while risk-aversion vanishes (or vice versa), HVA could enter at leading order. The paper’s statement (Conclusion: “including HVA alongside CVA and FVA conflates orders”) should be read as “conflates orders under our scaling,” not as a universal statement.
Linearisation of the default term
The passage as is the key step that produces the combined discount . This linearisation loses the exponential tail behaviour that makes the exact BSDE driver superlinear. For portfolios with large positive exposures, the leading-order approximation may significantly underestimate the default contribution.