Curated items for this scope are still being collected. Below are the latest related updates; items meeting the curation criteria will be marked separately.
The paper proposes a branching signature model that generalizes the classical geometric rough path based on the branching rough path framework, proving its universal approximation theorem, which can approximate high-level signatures through iterative combinations of low-level signature mappings. The model can explicitly extend the original path to a high-dimensional space via the mapping Ψ, implementing it as the classical geometric signature of the extended path, and its effectiveness is verified in two applications: rough volatility calibration and optimal execution.
The study proposes a conditional latent diffusion framework that jointly generates 30-step trajectories of the implied volatility surface and underlying returns. It learns surface representations through an arbitrage-aware autoencoder, with the diffusion process capturing the evolution dynamics. The framework is evaluated on the SPX surface dataset and can generate realistic probabilistic multi-step scenarios, with its forecast mean outperforming the persistence benchmark for horizons beyond the shortest maturity.
This paper proposes an arbitrage-aware latent flow matching framework for unconditional generation of implied volatility surfaces. The framework first compresses high-dimensional surfaces into a low-dimensional latent space using a variational autoencoder with differentiable penalty regularization for calendar spreads, call spreads, and butterfly arbitrage. A flow matching model then learns to map a Gaussian prior to the empirical latent distribution, which is finally decoded back to a volatility surface. This method can generate 90.8% of surfaces that satisfy all tested static no-arbitrage conditions, performs best in the extreme Q99 range, and balances distributional similarity, tail preservation, and financial consistency without post-sampling reweighting.
This study formulates and solves a stochastic control problem for Ethena protocol's core yield strategy, which generates returns through a delta-neutral combination of long stETH positions and short ETH perpetual contracts of equal size. The model takes the rate of simultaneously buying stETH and shorting perpetual contracts as the control variable, incorporating both permanent price impact and temporary execution slippage. The study solves both the infinite-horizon discounted problem and the finite-horizon problem with terminal liquidation costs, obtaining explicit optimal control solutions in both cases.
This paper proposes AddMart, a few-shot implied volatility surface reconstruction framework that embeds no-arbitrage structure into the architecture to achieve global static no-arbitrage, enabling continuous and valid surface generation from a small number of option quotes. Experiments on 10 real-world option datasets show that the framework delivers competitive reconstruction accuracy with no significant static arbitrage violations, and can be directly applied to VaR, CVaR and crash probability estimation.
This 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.
This study extends the classical Black-Scholes market framework to infinite-dimensional scenarios, modeling asset prices with stochastic evolution equations in separable Hilbert spaces, deriving an infinite-dimensional Black-Scholes equation, and establishing exact replication results for strong solutions and asymptotic replication results for weak solutions. The study also introduces Hilbert lattice structures to characterize no-arbitrage conditions, provides sufficient conditions for the existence and uniqueness of equivalent risk-neutral measures, and explicit pricing formulas for some tractable cases; the model can be interpreted as a segmented market composed of a series of one-dimensional geometric Brownian motions.
Published at ICAIF 2026, this study finds that accurate option prices do not necessarily mean accurate recovery of the underlying risk-neutral density. By comparing synthetic simulation benchmarks with out-of-sample NIFTY option benchmarks, the research reveals that the two-component lognormal mixture model demonstrates overall pricing performance on synthetic benchmarks.
This paper focuses on the impact of hybrid dynamics (rough volatility and infinite variation Lévy processes) on the implied volatility skew and curvature of at-the-money options. By analyzing the leading terms of their short-time expansions, the origin mechanism of the power-law behavior of skew and curvature can be determined from the hybrid dynamics model. The paper confirms that hybrid models also produce power-law relationships, and the relevant conclusions are crucial for model design and calibration. Numerical evidence for the rough Heston model with CGMY Lévy jumps is also provided.
This study examines the Merton consumption-investment problem under the Itô, Stratonovich, and Hänggi-Klimontovich conventions, corresponding to stochastic integral convention parameter α = 0, 1/2, and 1, respectively. The study finds that if the drift term is transformed synchronously, the optimal investment strategy is unaffected by the choice of convention; if the drift coefficient value is fixed, the investor's opportunity set will change. Under logarithmic utility, consumption decisions are unaffected, but the optimal portfolio weights shift, and the stochastic integral convention cannot be identified using return data alone.
Antonis Ballis and Christos Ioannidis released a working paper on arXiv, using a sample of all 8,970 crypto assets and 690 weekly cross-sections from 2013 to 2026 to directly observe asset entry, exit, and survival, and to test the weak-form efficiency symmetry constraints of the Markov chain for the crypto asset market capitalization distribution.
This paper proposes a generalized variance framework based on the determinant of the instantaneous covariance matrix of asset log returns for pricing multi-asset discrete swaps, which captures the joint discreteness of multivariate asset returns while accounting for their dependence structure. The framework is constructed under Heston and Barndorff-Nielsen-Shephard (BNS) stochastic volatility models, deriving analytical pricing expressions for the three-asset Heston model and tractable approximations for the corresponding BNS model.
