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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,932 papers · 148 categories

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12.5%25.0%37.5%50.0% · May 199419922001200920172026
48 results for Dirac delta regression

DDR estimates personalized treatment effects from clinical trials.

problem Estimating personalized treatment effects from clinical trials data.
method Transforms outcome into Dirac delta distributions and estimates density using non-linear regression.
result Identifies significant patient-specific outcomes even when no population-level effect exists.

We introduce Dirac processes, using Dirac delta functions, for short-rate-type pricing of financial derivatives. Dirac processes add spikes to the existing building blocks of diffusions and jumps. Dirac processes are Generalized Processes, which have not been used directly before because the dollar value of non-Real nu…

2015-04-17abs ↗pdf ↗

We consider the set of solutions to the rho-vortex equations over a Kahler surface and prove a Uhlenbeck compactness result, namely that a sequence of solutions with the same energy converge to the sum of a solution of smaller energy and deltas of Dirac.

2004-06-29abs ↗pdf ↗

This study examines deep hedging for S&P 500 options, revealing systematic delta corrections and fragility.

problem Understanding and validating deep hedging strategies for financial options.
method Compared TD3 agents with a Black-Scholes delta hedge, using walk-forward tests and symbolic regression.
result Deep hedging agents learn systematic delta corrections, which can improve performance but are regime-fragile.

In high frequency financial data not only returns but also waiting times between trades are random variables. In this work, we analyze the spectra of the waiting-time processes for tick-by-tick trades. The numerical problem, strictly related with the real inversion of Laplace transforms, is analyzed by using Tikhonov's…

2008-01-19abs ↗pdf ↗

In this paper we analytically study the problem of pricing an arithmetically averaged Asian option in the path integral formalism. By a trick about the Dirac delta function, the measure of the path integral is defined by an effective action functional whose potential term is an exponential function. This path integral …

2010-08-28abs ↗pdf ↗

Deep BSDE method for pricing and hedging complex financial portfolios.

problem Simultaneous pricing and delta-gamma hedging of large portfolios of multi-asset Bermudan options.
method Discretely reflected BSDEs, One Step Malliavin scheme, neural network regression Monte Carlo method.
result Efficient and accurate pricing and hedging strategies for high-dimensional portfolios.

Paper presents an efficient algorithm for estimating Lipschitz functions from noisy data.

problem Estimating unknown Lipschitz functions from noisy observations.
method Extends max-affine methods to Lipschitz setting using nonlinear feature expansion and adaptive partitioning.
result Achieves minimax convergence rate with respect to intrinsic dimension, up to logarithmic factors.

The Dirac operator d+delta on the Hodge complex of a Riemannian manifold is regarded as an annihilation operator A. On a weighted space L_mu^2 Omega, [A,A*] acts as multiplication by a positive constant on excited states if and only if the logarithm of the measure density of mu satisfies a pair of equations. The equati…

2001-04-17abs ↗pdf ↗

New methods for delta-moves on algebraically split links identified.

problem Understanding delta-moves on algebraically split links.
method Introducing self and mixed delta-moves, proving equivalence, and calculating delta-splitting numbers.
result Two links are mixed delta-equivalent if they have the same pairwise linking number and components.

The paper compares machine learning methods with traditional techniques for pricing and sensitivities of financial products with path-dependent structures.

problem Evaluating financial products with early-termination clauses, especially those with path-dependent structures.
method The paper compares regression methods including randomized recurrent and feed-forward neural networks, and a novel approach using signatures of the underlying price process, with traditional polynomial basis functions for pricing and sensitivities.
result Machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates.

Delta-unlinking number measures how to unlink algebraically split links.

problem Measuring unlinking complexity of algebraically split links.
method Defining delta-unlinking number as minimum delta-moves to unlink, proving bounds and calculating specific values.
result Precise delta-unlinking numbers for algebraically split prime links up to 9 crossings, and 4-genus values for most.

A Delta-groupoid is an algebraic structure which axiomitizes the combinatorics of a truncated tetrahedron. It is shown that there are relations of Delta-groupoids to rings, group pairs, and (ideal) triangulations of three-manifolds. In particular, one can associate a Delta-groupoid to ideal triangulations of knot compl…

2009-08-10abs ↗pdf ↗

We refine the analysis of hedging strategies for options under the SABR model carried out in [2]. In particular, we provide a theoretical justification of the empirical observation made in [2] that the modified delta ("Bartlett's delta") introduced there provides a more accurate and robust hedging strategy than the con…

2017-04-11abs ↗pdf ↗

A Delta-groupoid is an algebraic structure which axiomatizes the combinatorics of a truncated tetrahedron. By considering two simplest examples coming from knot theory, we illustrate how can one associate a Delta-groupoid to an ideal triangulation of a three-manifold. We also describe in detail the rings associated wit…

2010-01-18abs ↗pdf ↗

Delta method vs Bootstrap for deep learning classification shows strong linear relationship and faster computation.

problem Validating the Delta method for deep learning classification.
method Comparison of Delta method and Bootstrap on LeNet-based neural networks using MNIST and CIFAR-10 datasets.
result The Delta method provides a five times faster computation with strong linear predictive uncertainty relationship.

Delta finite-type invariants are defined analogously to finite-type invariants, using delta moves instead of crossing changes. We show that they are closely related to the lower central series of the commutator subgroup of the pure braid group.

1999-07-12abs ↗pdf ↗

We call a Delta Diagram any diagram of a knot or link whose regions (including the unbounded one) have 3, 4, or 5 sides. We prove that any knot or link admits a delta diagram. We define and estimate combinatorial link invariants stemming from this definition.

2015-12-20abs ↗pdf ↗

The paper classifies pretzel links with 2 components and gives conditions for those with 3 or more.

problem Classifying pretzel links based on their self delta-equivalence.
method Using Conway polynomials to determine self delta-equivalence for links with 2 or more components.
result Necessary and sufficient conditions for self delta-equivalence of pretzel links with 3 or more components.

We study generalizations of finite-type knot invariants obtained by replacing the crossing change in the Vassiliev skein relation by some other local move, analyzing in detail the band-pass and doubled-delta moves. Using braid-theoretic techniques, we show that, for a large class of local moves, generalized Goussarov's…

2005-11-08abs ↗pdf ↗

Study calculates liquidity costs for delta hedging of European options.

problem Determining expected liquidity costs in delta hedging.
method Derives an integration formula for liquidity costs, including option prices and delta process.
result Expected liquidity costs can be calculated faster than Monte Carlo simulations.

This paper uses Malliavin calculus to price and compute delta of financial derivatives in jump-diffusion models.

problem Pricing and delta computation of financial derivatives in jump-diffusion models with stochastic intensity.
method Utilizes Malliavin calculus to price and compute delta, applying the Euler scheme for convergence analysis.
result Established the convergence of approximated solution, financial derivative, and its delta Greeks.

Paper introduces a new multi-kernel algorithm for better gradient approximation.

problem Improving gradient approximation in high-dimensional problems.
method Develops a multi-kernel passive stochastic gradient algorithm with variance reduction.
result The multi-kernel algorithm performs better in high-dimensional problems.

Modelling stock prices via jump processes is common in financial markets. In practice, to hedge a contingent claim one typically uses the so-called delta-hedging strategy. This strategy stems from the Black--Merton--Scholes model where it perfectly replicates contingent claims. From the theoretical viewpoint, there is …

2011-03-25abs ↗pdf ↗

We generalize the Manolescu-Owens smooth concordance invariant delta(K) of knots K in the 3-sphere to invariants delta_{p^n}(K) obtained by considering covers of order p^n, with p prime. Our main result shows that for any odd prime p, the direct sum of delta_{p^n} as n ranges through the natural numbers, yields a homom…

2008-09-05abs ↗pdf ↗

We consider a strictly pathwise setting for Delta hedging exotic options, based on Föllmer's pathwise Itō calculus. Price trajectories are dd-dimensional continuous functions whose pathwise quadratic variations and covariations are determined by a given local volatility matrix. The existence of Delta hedging strategie…

2015-10-30abs ↗pdf ↗

Reflective Hamiltonian Monte Carlo struggles with high-dimensional sampling.

problem Slow mixing in reflective Hamiltonian Monte Carlo with inexact reflections.
method Quantifying instantaneous non-uniformity with Sinkhorn divergence; analyzing particle motion in spheres and cubes; constructing low-dimensional toy models.
result Particles spontaneously unmix, leading to resonances in particle density.