arXiv subjects

Junchi Shen

Publications and source records attributed to Junchi Shen.

3 recordsLinked to original sources

arXiv2026

Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage

Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main evaluation -- a point-in-time universe and 170 walk-forward windows (2012-2025) -- no quantum advantage exists: the fidelity kernel is indistinguishable from its RBF control ($\Delta$IC $=+0.005$, $p=0.42$), and a $2\times2$ design crossing kernel type with training budget (a Nystrom extension to the full ~38,000-observation windows) shows quantum kernels matching, but never beating, equal-budget linear models; after family-wise correction no pairwise difference among eleven models is significant, with point estimates favoring penalized linear regressions throughout. We then document how the opposite conclusion arises: a 60-window evaluation on a universe screened with full-sample information makes the same quantum kernel appear dominant on stability criteria and significantly better than neural baselines. Interaction characteristics from the anomalies literature help nothing, quantum or classical; a widened bandwidth grid reveals an interior optimum rather than the near-classical endpoint a coarse grid suggests; and the geometric difference, while large throughout ($g \gg 1$), does not predict out-of-sample gains ($\rho=-0.20$). We propose protocol standards -- kernel-swap controls, budget-equalized comparisons, point-in-time universes, and multiplicity-robust inference -- for empirical claims of quantum advantage in finance.

q-fin.PR↗

arXiv2026

Denoising Subordinated Probabilistic Models: Diffusion with a Tempered-Stable Volatility Clock, and What the Noise Mechanism Actually Controls

Heavy-tailed diffusion models replace Gaussian noise by a Gaussian variance mixture: denoising Levy probabilistic models (DLPM) take the mixing variables i.i.d. across coordinates, while Student-t EDM shares one mixing variable per sample. Neither has dynamics, yet temporal dependence of the noise amplitude - volatility clustering - is the defining stylized fact of financial returns. We introduce the Denoising Subordinated Probabilistic Model (DSPM), whose mixing vector is a stationary AR(1) chain driven by tempered-stable subordinator increments (the discrete Barndorff-Nielsen-Shephard volatility process) along the data axis. Conditionally on the chain the DDPM machinery survives verbatim; kurtosis and squared-noise autocorrelation are closed-form in the chain parameters, giving an exactly identified, analytically invertible calibration; DDPM, DLPM and Student-t noise are boundary cases of one memory parameter. We then prove a delimiting result: when the denoiser is conditioned on the mixing variables, their law is a nuisance - in the exact-denoiser limit the generated distribution is invariant to it and interventions on the chain do nothing. Experiments confirm both halves: conditioned models match the data's clustering whatever the mixing law, a designed x8 volatility shock moves the envelope by under 13%, while blind models transmit the mechanism exactly as calibrated. Finally, coupling the chain to the data by a variational volatility encoder - trained with the stochastic-volatility likelihood whose log-determinant the simplified denoising loss provably drops - restores control (shock response 3.07 vs. naive 2.83), recovers latent volatility (correlation 0.76), and learns the prior memory toward the true persistence.

q-fin.MF↗

arXiv2025

Conditional Deep Levy Models for Exotic Derivatives: History-Aware Path Generation and P-Q Payoff Diagnostics

We develop and audit a history-aware financial path generator based on Denoising Levy Probabilistic Models (DLPMs) for conditional equity-index path generation. The model combines symmetric alpha-stable diffusion noise with a conditional U-Net observing the contract state, 60- and 252-day return histories, and pre-start trend, drawdown, and volatility state. A chronological protocol evaluates one frozen model on 6,824 untouched windows from eight Chinese and U.S. equity indices. The generator attains terminal CRPS 0.0485, path energy score 0.3368, and a generated-to-realized volatility ratio of 0.882, improving on contract-only DLPM and Gaussian diffusion controls. An unconditional historical block bootstrap remains competitive on pooled marginal metrics; against a stronger state-matched empirical control using the same index, tenor, volatility, trend, and drawdown information, DLPM lowers terminal CRPS from 0.0633 to 0.0485 (23.3%), with joint calendar-block confidence intervals below zero from 20 to 150 days. We then embed the learned physical-measure distribution in a transparent P-Q payoff diagnostic against an independently fitted, martingale-corrected Student-t GARCH(1,1) benchmark. At a pre-specified 0.05 dealer spread and a 1%-of-spot materiality threshold, the aligned unconditional diagnostic is 0.91% of initial spot for vanilla calls and 0.36% for Asian calls. Drift-neutral and one-time-delta counterfactuals decompose this gap: physical direction is a large component, leaving a 0.33% vanilla residual and a near-zero Asian residual. The P-Q framework is a belief-based payoff diagnostic, distinct from an option-surface-calibrated pricing engine.

q-fin.PR↗