Dropout is shown to be a simplified version of SDR, which improves deep learning performance.
problem Overfitting and misspecification in deep learning models.
method SDR redefines weights as random variables, updating them based on prediction error and local history.
result SDR outperforms Dropout on standard benchmarks, achieving similar accuracy in fewer epochs.
New approach solves utility maximization problems using Delta family.
problem Utility maximization in stochastic control problems.
method Directly solving DP equation with Delta function representation.
result Explicit series representation of value function.
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.
Optimal hedging strategies identified for markets with fast-varying volatility.
problem No perfect hedge in markets with fast-varying stochastic volatility.
method Analyzes various delta-type hedging strategies and their performance in a specific asymptotic regime of rapid mean reversion.
result Identifies the `practitioners' delta hedging scheme as optimal in the considered regime of rapid mean reversion.
Investors mispricing volatility and jump sensitivity in Delta hedging models still super-replicate the true claim.
problem Investors misestimate volatility and jump sensitivity in Delta hedging models.
method Analyzes the robustness of Delta hedging in jump-diffusion models, proving stochastic flow properties and convexity of value functions.
result An erroneously computed Delta strategy super-replicates the true claim in expectation under a wide class of models.
Optimal hedging strategies for exotic options using vanilla options.
problem Hedging exotic options with illiquid vanilla options.
method Simple approximations and variational techniques in a market model and stochastic volatility model framework.
result Optimal Delta and Vega hedging strategies can be computed easily.
Study the hedging of cryptocurrency options in a volatile market.
problem Hedging options in a volatile, non-stationary cryptocurrency market.
method Calibrated to SVI-implied volatility surfaces, Monte Carlo price paths generated using SVCJ, GARCH, and historical data. Delta, Delta-Gamma, Delta-Vega, and Minimum Variance strategies applied. Wide range of market models tested.
result Calibration results indicate stochastic volatility, low jump frequency, and infinite activity. Short-dated options less sensitive to volatility or Gamma hedges; longer-dated options benefit from multiple-instrument hedges.
Asymptotic error distribution for approximation of a stochastic integral with respect to continuous semimartingale by Riemann sum with general stochastic partition is studied. Effective discretization schemes of which asymptotic conditional mean-squared error attains a lower bound are constructed. Two applications are …
A new method calculates accurate SABR model option prices and deltas.
problem Inaccurate and arbitrageable SABR model option prices and deltas.
method Gaussian quadrature integration scheme for the normal SABR model.
result Accurate and arbitrage-free SABR model option prices and deltas calculated with 49 points.
RL and DTSOC for final quadratic hedging performance studied.
problem Optimal hedging of European call options with and without transaction costs.
method Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control.
result RL and DTSOC perform similarly to variance-optimal hedging in various market models.
Bias correction improves language model training performance.
problem Stochastic update bias in preconditioned optimizers.
method Cross-fitted preconditioning and variance-corrected inversion.
result Reduces held-out pretraining loss by 0.15 nats.
We present a new approach to the optimal portfolio problem for an insider with logarithmic utility. Our method is based on white noise theory, stochastic forward integrals, Hida-Malliavin calculus and the Donsker delta function.
This study uses DRL to hedge American put options, outperforming traditional methods.
problem Hedging American put options with high accuracy and low transaction costs.
method Deep Deterministic Policy Gradient (DDPG) method, trained on stochastic volatility models.
result DRL agents outperform traditional methods in both simulated and real-world scenarios.
The paper bridges stochastic control and deep hedging for European call options with transaction costs.
problem Hedging and pricing European call options with proportional transaction costs.
method Complementary perspectives: stochastic control and deep hedging. Two architectures proposed: NTBN-Delta and WW-NTBN.
result WW-NTBN converges faster, matches no-transaction bands more closely, and generalizes well across transaction cost regimes.
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 study models mortgage prepayment risk using stochastic housing market activity.
problem Modeling prepayment risk in mortgages under varying housing market conditions.
method Developed a stochastic model for prepayment option value, using swaption pricing formulas and non-standard actuarial hedging.
result Housing market covariance significantly impacts prepayment option prices.
A general market model with memory is considered in terms of stochastic functional differential equations. We aim at representation formulae for the sensitivity analysis of the dependence of option prices on the memory. This implies a generalization of the concept of delta.
Study of tropical moduli spaces using symmetric Delta-complexes.
problem Understanding the fundamental groups and homology of tropical moduli spaces.
method Develop techniques for symmetric Delta-complexes, apply to moduli spaces of tropical curves.
result Delta_g and Delta_{g,n} are simply connected for positive g.
Connections between nodes of fully connected neural networks are usually represented by weight matrices. In this article, functional transfer matrices are introduced as alternatives to the weight matrices: Instead of using real weights, a functional transfer matrix uses real functions with trainable parameters to repre…
The Local Volatility model is a well-known extension of the Black-Scholes constant volatility model whereby the volatility is dependent on both time and the underlying asset. This model can be calibrated to provide a perfect fit to a wide range of implied volatility surfaces. The model is easy to calibrate and still ve…
Proposes a neural network for high-dimensional American option pricing.
problem High-dimensional American option pricing and hedging.
method Deep neural network framework based on backward stochastic differential equations.
result The framework yields prices and deltas on the entire spacetime.
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.
Proposes new rule for ranking investment prospects over long horizons.
problem Ranking investment prospects over long horizons considering bounded risk aversion.
method Introduces asymptotic fractional-order stochastic dominance with bounded relative risk aversion.
result Establishes equivalent conditions for the new rule under lognormal returns without mean non-negativity constraint.
The paper adapts step sizes in TD learning to identify relevant features.
problem Identifying which features are relevant for temporal-difference learning.
method Adapting step sizes in stochastic gradient descent for feature relevance in TD learning.
result TD IDBD effectively distinguishes relevant features in gridworld and robotic tasks.
TWM doesn't reduce delta in PDLPs, proving impossibility.
problem TWM in PDLPs doesn't uniformly reduce portfolio delta.
method Proved TWM's condition is self-contradictory and showed impossibility.
result No TWM can uniformly reduce portfolio delta.
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…
The paper calculates delta invariants for specific geometric structures.
problem Computing delta invariants for projective bundles and cones of Fano type.
method Provides a precise formula for delta invariants.
result A formula to compute delta invariants for projective bundles and cones of Fano type.
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…
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…
Link-homotopy and self Delta-equivalence are equivalence relations on links. It was shown by J. Milnor (resp. the last author) that Milnor invariants determine whether or not a link is link-homotopic (resp. self Delta-equivalent) to a trivial link. We study link-homotopy and self Delta-equivalence on a certain componen…
Continuity of delta invariant leads to uniform Kähler-Einstein metrics.
problem Continuity of delta invariant in Kähler and twisted Kähler-Einstein metrics.
method Analytic delta invariant and uniform Yau-Tian-Donaldson theorem.
result Uniform Yau-Tian-Donaldson theorem for twisted Kähler-Einstein metrics.
The Kelly rule fails to maximize growth in a time-changed return setting.
problem Performance of the Kelly rule in a time-changed return process.
method Investigated the Kelly rule in a semi-martingale setting with a time change process.
result The Kelly rule does not maximize average growth rate in a non-normal log-return setting.
Unified quadrature framework for large-scale kernel machines.
problem Efficiently approximating kernel functions for large-scale machine learning.
method Deterministic and randomized interpolatory rules for numerical integration of kernel functions.
result The proposed method reduces the number of nodes needed for accurate kernel approximation.
A new method simulates implied volatility surfaces for multiple assets.
problem Generating consistent market scenarios for multiple asset implied volatilities.
method Combining functional data analysis and neural SDEs with a penalty for model misspecification.
result Simulated market scenarios are consistent with historical features and lie within the sub-manifold of essentially free static arbitrage.
In this paper we construct some invariants of spatial graphs by disk-summing the constituent knots and show the delta edge-homotopy invariance of them. As an application, we show that there exist infinitely many slice spatial embeddings of a planar graph up to delta edge-homotopy, and there exist infinitely many bounda…
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.
Study delta invariant of curves on rational surfaces using topological methods.
problem Calculate delta invariant for curves embedded in rational singularities.
method Use topological techniques and Poincaré series.
result Develop formulae for delta invariant in terms of embedded data.
Study shows singular set of distance functions is delta-convex.
problem Understanding singular set of distance functions in Finsler manifolds.
method Proved singular set is delta-convex hypersurfaces or Jordan arcs up to exceptional sets.
result Optimal results in Finsler manifolds, even in Euclidean space.
In a stochastic volatility framework, we find a general pricing equation for the class of payoffs depending on the terminal value of a market asset and its final quadratic variation. This allows a pricing tool for European-style claims paying off at maturity a joint function of the underlying and its realised volatilit…
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.
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.
Study virtualized Delta, Sharp, and Pass moves for oriented virtual knots and links.
problem Conditions for unknotting oriented virtual knots and links.
method Local moves (Delta, Sharp, Pass) for oriented virtual links.
result Lower bounds for the unknotting numbers are proven and shown to be best possible.
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.
Study computes option sensitivities using Malliavin calculus for hybrid stochastic models.
problem Computing option sensitivities (Greeks) under hybrid stochastic volatility and interest rate models.
method Integrates Malliavin calculus for Delta, Vega, and Rho computation; extends to non-differentiable payoffs.
result Malliavin calculus enables effective numerical implementations for various option types.
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…
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.
Lower bounds for delta invariant of weighted hypersurfaces proved for K-stability.
problem Proving K-stability of weighted hypersurfaces.
method Abban-Zhuang method and study of linear systems on flags of weighted hypersurfaces.
result Proves K-stability of a large class of quasi-smooth Fano hypersurfaces and all smooth Fano weighted hypersurfaces.
Improved deep hedging with ensemble uncertainty quantification.
problem Uncertainty in deep hedging models hinders their deployment.
method Trained an ensemble of LSTM networks to quantify uncertainty in deep hedging under Heston volatility and proportional transaction costs.
result The ensemble's disagreement provides a strong predictive confidence measure for hedge performance.