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 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 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 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.
Quantocracy has compiled the latest quant research-related links as of October 18, 2026, covering topics including trend following, futures continuous contract construction, in-sample to out-of-sample Sharpe ratio decay, the impact of liquidity on stock returns and option pricing, and cryptocurrency cross-sectional trend factors. One study based on 12 commodity futures found that the expected retention ratio of in-sample Sharpe ratios from a 10-year backtest on new data is 30%.
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 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.
This article treats the holding costs, behavioral pressures, and governance burdens of tail risk hedging during calm periods as design constraints rather than post-hoc inconveniences. The authors first break down three types of "bleeding" sources: direct holding costs, opportunity costs, and evaluation pressure, and provide a table of cumulative sleeve losses for different annualized carry rates over 3 to 15 years, as well as a table of portfolio-level annual drag converted by sleeve weight.
Block Scholes has released its Week 40 crypto derivatives market analysis report. The report shows that despite pressure from the macro environment, BTC prices consolidated in the $82,000 to $84,000 range, while spot BTC and ETH ETFs recorded net capital inflows for seven consecutive days. In the options market, the 25-delta skew for BTC and ETH fluctuated between demand for downside protection and preference for upside exposure, with 7-day ETH implied volatility rising by nearly 10 percentage points.
Block Scholes has released its Week 38 crypto derivatives market analysis report, which shows that the market is currently pricing in a 93% probability of a rate hike at the Federal Reserve's meeting the following day, while the probability of the Clarity Act being passed by the end of 2026 on Polymarket has dropped to 19%. The implied volatility across all tenors for BTC options is in the 38%-39% range, and the implied volatility across all tenors for ETH options is in the 52%-54% range. BTC options skew is close to neutral, ETH options skew shows a moderately bullish stance, and the BTC and ETH risk appetite indices are near historical high ranges.
Block Scholes has released its Week 35 crypto derivatives market analysis report, noting that the U.S. Treasury's plan to double the scale of long-term Treasury buybacks, combined with expectations for the advancement of the Clarity Act, drove BTC up 20% during the week to break through $80,000, and ETH up 30% during the week.
