Chen–Hermite balancing conjecture for Gaussian single increments

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Let (Xt)t∈[0,T](X_t)_{t\in[0,T]} be a 11-dimensional continuous stochastic process whose single increments are Gaussian. Assume that, for every 0≤s≤u≤t≤T0\leq s\leq u\leq t\leq T and every n≥2n\geq2,

∑i=1n−1E[Hi(Xs,u,−12(u−s))⋅Hn−i(Xu,t,−12(t−u))]=0.\sum_{i=1}^{n-1}\mathbb{E}[H_i(X_{s,u},-\tfrac{1}{2}(u-s))\cdot H_{n-i}(X_{u,t},-\tfrac{1}{2}(t-u))]=0.

Chen–Hermite Gaussianity conjecture. Then (Xt)t∈[0,T](X_t)_{t\in[0,T]} must be a Gaussian process. This would establish joint Gaussianity from Gaussian single-increment laws and the Chen–Hermite balancing identities; the corresponding implication is the missing step in the paper's characterization, while it is immediate under an additional joint-Gaussianity assumption.

References

Primary source

Tomoyuki Ichiba and Qijin Shi, “Unbiased Rough Integrators and No Free Lunch in Rough-Path-Based Market Models”, arXiv:2509.14529 (2026).

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Current status (as of August 2026): it appears open, with no recorded activity.

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