Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks.
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arXiv:2607. 27188v1 Announce Type: new Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density.
arXiv:2609.31570v1 Announce Type: new Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captur...
The paper introduces a doubly robust framework for distributionally robust offline contextual pricing, addressing challenges of distributional shifts and continuous actions. It proposes a localized DR estimator that is computationally efficient and semiparametrically optimal, and a learning algorithm that exploits demand noise smoothness to achieve finite‑sample regret bounds of τ^{-σ/(2σ+1)} for smoothness orders s=1,2. Experiments across varying distribution shifts demonstrate the effectiveness of the proposed methods.
OrderFlowDiT is a conditional Diffusion Transformer designed to forecast limit‑order volume over the next five minutes, jointly modeling price placement, total volume, and predictive uncertainty. The model uses unnormalized bid and ask volume‑at‑price fields and incorporates an unbalanced optimal‑transport (UOT) auxiliary loss to better capture price displacement and volume information. In a validation study across 30 U.S. equities, adding UOT reduced transport cost by 45.8% and transport energy score by 26.1% compared to mean‑squared‑error training, improving accuracy for every stock.
arXiv:2608. 01217v1 Announce Type: cross Abstract: Local-stochastic volatility (LSV) combines vanilla marginals with richer smile dynamics, but calibration requires a slow, noisy and sequential McKean--Vlasov fixed point.
arXiv:2410.14839v5 Announce Type: replace-cross Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpor...