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.


concept