arbiter
JournalsAccount

Calibration of local volatility models with stochastic interest rates using optimal transport

Benjamin Joseph et al.

Finance and Stochastics2026https://doi.org/10.1007/s00780-026-00588-6article
AJG 3ABDC A
Weight
0.50

Abstract

We develop a nonparametric, semimartingale optimal transport, calibration methodology for local volatility models with stochastic interest rates. The method finds a fully calibrated model which is closest, in a way defined by a general cost function, to a given reference model. We establish a general duality result which allows to solve the problem by optimising over solutions to a second-order fully nonlinear Hamilton–Jacobi–Bellman equation. Our methodology is analogous to Guo et al. (SIAM J. Financ. Math. 13:1–31, 2022; Math. Finance 32:46–77, 2022), but features a novel element of solving for discounted densities, or sub-probability measures. As an example, we apply the method to a sequential calibration problem, where a Vašíček model is already given for the interest rates and we seek to calibrate a stock price’s local volatility model with a volatility coefficient depending on time, the underlying and the short rate process, and the two processes driven by possibly correlated Brownian motions. The equity model is calibrated to any number of European option prices.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1007/s00780-026-00588-6

Or copy a formatted citation

@article{benjamin2026,
  title        = {{Calibration of local volatility models with stochastic interest rates using optimal transport}},
  author       = {Benjamin Joseph et al.},
  journal      = {Finance and Stochastics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s00780-026-00588-6},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Calibration of local volatility models with stochastic interest rates using optimal transport

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.