arXiv research

Sharp privacy bounds for sequential analysis of sensitive data.

By adapting methods of \cite{AC} we prove a sharp estimate on the expansion modulus of the gradient of the log of the parabolic kernel to the Schördinger operator with convex potential, which improves an earlier work of Brascamp-Lieb. We also include alternate proofs to the improved log-concavity estimate, and to the f…

Sharp Sobolev inequality derived for Riemannian manifolds with bounded Ricci curvature.

Non-negative curvature affects Markov chains' mixing and expansion properties.

Gradient descent at edge of stability stabilizes implicitly, following projected gradient descent.

Study asymptotics of Poisson kernel and Green's functions for fractional conformal Laplacian.

In an incomplete market, including liquidly-traded European options in an investment portfolio could potentially improve the expected terminal utility for a risk-averse investor. However, unlike the Sharpe ratio, which provides a concise measure of the relative investment attractiveness of different underlying risky as…

The main goal of the paper is to address the issue of the existence of Kempf's distortion function and the Tian-Yau-Zelditch (TYZ) asymptotic expansion for the Kepler manifold - an important example of non compact manfold. Motivated by the recent results for compact manifolds we construct Kempf's distortion function an…

Improved mass-capacity bounds for specific 3D manifolds.

Study on shape optimization for specific eigenvalue problems on domains.

We prove several relations between spectrum and dynamics including wave trace expansion, sharp/improved Weyl laws, propagation of singularities and quantum ergodicity for the sub-Riemannian (sR) Laplacian in the four dimensional quasi-contact case. A key role in all results is played by the presence of abnormal geodesi…

Develops a dynamical method to prove the sharp Berezin-Li-Yau inequality.

We consider a stochastic volatility asset price model in which the volatility is the absolute value of a continuous Gaussian process with arbitrary prescribed mean and covariance. By exhibiting a Karhunen-Loève expansion for the integrated variance, and using sharp estimates of the density of a general second-chaos var…

In this paper we prove mixed norm estimates for Riesz transforms related to Laplace--Beltrami operators on compact Riemannian symmetric spaces of rank one. These operators are closely related to the Riesz transforms for Jacobi polynomials expansions. The key point is to obtain sharp estimates for the kernel of the Jaco…

We prove here a general closed-form expansion formula for forward-start options and the forward implied volatility smile in a large class of models, including the Heston stochastic volatility and time-changed exponential Lévy models. This expansion applies to both small and large maturities and is based solely on the p…

Roy's `Safety First' criterion for selecting one risky asset from many is adapted to the case of non-normal returns, via Cornish Fisher expansion. The resulting investment objective is consistent with first order stochastic dominance, and is equal to the Sharpe ratio for the case of normal returns. An investor selectin…

Proposes a new framework to optimize portfolios with reduced estimation errors.

Sharp estimates on 2-step nilpotent Lie groups' metrics and cones.

Paper provides Edgeworth expansions for network moments, improving accuracy of sampling distributions.

We consider the first non-zero eigenvalue $λ_1$ of the Laplacian on hyperbolic surfaces for which one disconnecting collar degenerates and prove that $8π\nabla\log(λ_1)$ essentially agrees with the dual of the differential of the degenerating Fenchel-Nielsen length coordinate. As a consequence, we can improve previous …

This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.

We consider a stochastic volatility model where the moment generating function of the logarithmic price is finite only on part of the real line. Using a new Tauberian result obtained in [1] and [2], we show that the knowledge of the moment generating function near its critical moment gives a sharp asymptotic expansion …

A new method embeds data using Gaussian processes based on the heat kernel.

A new model for pricing ultra-short-term options with complex volatility patterns.

We study the asymptotic behavior of distribution densities arising in stock price models with stochastic volatility. The main objects of our interest in the present paper are the density of time averages of the squared volatility process and the density of the stock price process in the Stein-Stein and the Heston model…

We study the finite horizon Merton portfolio optimization problem in a general local-stochastic volatility setting. Using model coefficient expansion techniques, we derive approximations for the both the value function and the optimal investment strategy. We also analyze the `implied Sharpe ratio' and derive a series a…

We provide a full characterisation of the large-maturity forward implied volatility smile in the Heston model. Although the leading decay is provided by a fairly classical large deviations behaviour, the algebraic expansion providing the higher-order terms highly depends on the parameters, and different powers of the m…

New spatiotemporal Besov process improves CT image reconstruction and other inverse problems.

Sharp Sobolev inequality on circle proven with Lorentz invariance.

Paper quantifies uncertainty in pairwise comparison models.

We compute a sharp small-time estimate for implied volatility under a general uncorrelated local-stochastic volatility model. For this we use the Bellaiche \cite{Bel81} heat kernel expansion combined with Laplace's method to integrate over the volatility variable on a compact set, and (after a gauge transformation) we …

Improved stochastic approximation method reduces residual error.

Injectivity of ReLU networks is characterized for generative models and inverse problems.

Formula for heat coefficients of curved conic singularities derived from Riemannian metrics.

Rescaling expansiveness proven for k*-expansive vector fields.

We compute a sharp small-time estimate for the price of a basket call under a bi-variate SABR model with both $β$ parameters equal to $1$ and three correlation parameters, which extends the work of Bayer,Friz&Laurence [BFL14] for the multivariate Black-Scholes flat vol model. The result follows from the heat kernel on …

Study characterizes non-collapsed RCD(K, N) spaces using heat kernel metrics.

We consider an investor who seeks to maximize her expected utility derived from her terminal wealth relative to the maximum performance achieved over a fixed time horizon, and under a portfolio drawdown constraint, in a market with local stochastic volatility (LSV). In the absence of closed-form formulas for the value …

The paper derives expansions for Green's operators and resolvents using Hadamard methods.

We construct embedded Willmore tori with small area constraint in Riemannian three-manifolds under some curvature condition used to prevent Möbius degeneration. The construction relies on a Lyapunov-Schmidt reduction; to this aim we establish new geometric expansions of exponentiated small symmetric Clifford tori and a…

In this paper we investigate the asymptotics of forward-start options and the forward implied volatility smile in the Heston model as the maturity approaches zero. We prove that the forward smile for out-of-the-money options explodes and compute a closed-form high-order expansion detailing the rate of the explosion. Fu…

The paper solves the problem of fitting an ellipsoid to random points efficiently.

SIMPLE-RC method tests group membership profiles in large networks with weak signals.

Develops a martingale expansion for stochastic volatility models.

This is the second of a series of two papers where we construct embedded Willmore tori with small area constraint in Riemannian three-manifolds. In both papers the construction relies on a Lyapunov-Schmidt reduction, the difficulty being the Möbius degeneration of the tori. In the first paper the construction was perfo…

Taylor expansions improve reinforcement learning policies.

Analytic torsion expansions for symmetric and complex homogeneous spaces.

Proved cyclotomic expansion for double twist knots' HOMFLY-PT invariants.