Paper extends option spanning results for non-integrable assets.
problem Spanning power of options in non-integrable assets.
method Unified results for Lp-models and applied to pricing problem. result Option prices can be extended to all contingent claims.
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
New method uses neural networks for better financial hedging.
problem Spanning multi-asset payoffs with vanilla options.
method One-hidden-layer feedforward neural networks for numerical solution.
result Better hedging results with neural networks compared to single-asset approaches.
The paper finds the smallest order closed sublattice containing a given sublattice in vector lattices.
problem Identifying the smallest order closed sublattice containing a given sublattice in vector lattices.
method Analyzing the order closure and second order closure of sublattices.
result The smallest order closed sublattice containing a sublattice Y is often the second order closure of Y. This paper extends static hedging for European options over multiple maturities.
problem Hedging European options over multiple time periods.
method Developed a spanning relation for multiple shorter-term options using a Markovian framework.
result Demonstrated a practical implementation using Gaussian Quadrature for finite sets of shorter-term options.
Risk-averse reinforcement learning optimizes option hedging.
problem Optimizing option hedging under risk aversion and realistic market conditions.
method Applied Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment.
result The derived hedging strategy outperforms Black & Scholes and is robust to market variations.
The paper develops a neural network model for SPX option pricing.
problem Developing an empirical model for SPX option pricing.
method Formulated and rigorously evaluated several statistical models including neural network, random forest, and linear regression.
result The neural network model outperforms other models and Black-Scholes-Merton model for SPX option pricing.
Study evaluates hedging strategies for S&P500 index options.
problem Improving returns and risk management in index option portfolios.
method Compared Black-Scholes-Merton and Variance-Gamma models for hedging strategies.
result Systematic option-writing strategies can yield superior returns compared to buy-and-hold benchmarks.
We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have exponential utility functions and the individual endowments are spanned by the securities…
This paper compares linear regression and neural networks for pricing swing options.
problem Pricing swing options using approximation methods.
method Linear regression and neural networks for approximating the continuation value and swing price.
result The approximation methods converge to the actual swing price as the number of functions or Monte Carlo samples increases.
Deep learning outperforms Black-Scholes in Brazilian Petrobras option pricing.
problem Improving option pricing accuracy for Petrobras stocks.
method Trained deep residual networks using a custom loss function with historical data.
result Deep learning achieved a 64.3% reduction in mean absolute error compared to Black-Scholes.
We introduce a new approach for the numerical pricing of American options. The main idea is to choose a finite number of suitable excessive functions (randomly) and to find the smallest majorant of the gain function in the span of these functions. The resulting problem is a linear semi-infinite programming problem, tha…
Breaks circular dependency in synthetic option pricing with a novel model.
problem Circular dependency in implied volatility limits synthetic data for machine learning and risk analysis.
method Uses a Jump-Hidden Markov Model to generate price paths and a modified Heston process to convert paths into implied volatility.
result Framework generates realistic synthetic American option prices without external calibration.
This paper improves financial derivative pricing by incorporating multiple hedging instruments.
problem Valuation of financial derivatives with multiple hedging instruments.
method Deep hedging algorithm and reinforcement learning to solve global hedging problems.
result Including options as hedging instruments can significantly decrease equal risk prices and market incompleteness.
The paper develops a new framework for pricing and hedging liquidity in crypto markets.
problem Arbitrage and risk management in crypto market making.
method Developed a new mathematical framework using a coordinate system defined by price and intrinsic liquidity.
result Established a linear dependence of asset reserves and value functions on intrinsic liquidity, facilitating arbitrage-free pricing and delta hedging.
Paper develops data-driven compact models for diodes.
problem Manual and time-consuming compact model development.
method Machine Learning techniques for automation.
result Data-driven models accurately predict diode behavior.
This work extends SVM error bounds to weighted SVM and introduces hyperparameter selection methods.
problem Improving SVM performance through effective hyperparameter selection.
method Extending span error bound theory to weighted SVM and introducing hyperparameter selection methods.
result The span rule is the most effective method for weighted SVM hyperparameter selection and provides the best predictor of test error.
Develops a hedging method for multi-asset derivatives with correlation risk.
problem Hedging multi-asset derivatives exposed to correlation and covariance risk.
method Combines dynamic trading with static hedging instruments using Galtchouk--Kunita--Watanabe decomposition.
result Explicit semi-static replication formulas for covariance swaps and geometric dispersion trades.
New spanning 3-disks found for unlink in 4-sphere.
problem 2-component unlink in 4-sphere.
method Found infinitely many isotopy classes of Brunnian spanning 3-disks.
result Infinitely many Brunnian spanning 3-disks for 2-unlink in 4-sphere.
Develops tests for Markowitz stochastic dominance spanning using saddle points.
problem Determining if adding securities or relaxing investment constraints improves investment opportunity sets.
method Derives properties of cdfs, defines Markowitz stochastic dominance spanning, constructs non-parametric tests based on subsampling.
result Rejects market portfolio Markowitz efficiency and finds evidence of outperformance.
Sharp bounds for spanning tree entropy in planar lattices.
problem Estimating spanning tree entropy in planar lattice graphs.
method Using hyperbolic geometry and polyhedra volumes.
result Proved bounds are easy to compute and provide excellent estimates.
Totally geodesic surfaces found in knots and links.
problem Finding totally geodesic surfaces in knots and links.
method Constructing infinite families of knots and links with totally geodesic spanning surfaces in various 3-manifolds.
result Infinite families of knots and links with totally geodesic spanning surfaces in multiple 3-manifolds.
The Jones polynomial can be expressed in terms of spanning trees of the graph obtained by checkerboard coloring a knot diagram. We show there exists a complex generated by these spanning trees whose homology is the reduced Khovanov homology. The spanning trees provide a filtration on the reduced Khovanov complex and a …
A new classification method based on Minimum Spanning Trees
problem Improving classification in supervised learning
method Proposing a classification algorithm based on Minimum Spanning Trees
result The proposed method is effective and computationally efficient
Spanning attack improves black-box attacks with unlabeled data.
problem Query inefficiency in black-box attacks due to high input space dimensionality.
method Proposes spanning attack by constraining adversarial perturbations in a low-dimensional subspace via an auxiliary unlabeled dataset.
result Significantly improves query efficiency of black-box attacks.
Refines knot defect measurement in 3D and 4D.
problem Measuring how far knots are from being alternating.
method Extends spanning surface defect to 4-ball, making comparisons and proving formulas.
result Connected sum formula proven.
Proves bounds on spanning two-forests and random cut sizes.
problem Counting spanning two-forests and estimating random cut sizes.
method Uses pairwise effective resistances and potential theory.
result Establishes bounds on the number of spanning two-forests and average cut size.
Ancient curves span halfplanes via flow.
problem Ancient solutions to Curve Shortening Flow.
method Constructing infinite family of solutions.
result Spanning halfplane with ancient curves.
Alexander polynomial equals spanning tree count at t=1.
problem Alexander polynomial for spatial graphs.
method Combinatorial constructions generalized to weighted graphs.
result Value of Alexander polynomial at t=1 equals weighted spanning tree count.
We introduce the warping polynomial of an oriented knot diagram. In this paper, we characterize the warping polynomial, and define the span of a knot to be the minimal span of the warping polynomial for all diagrams of the knot. We show that the span of a knot is one if and only if it is non-trivial and alternating, an…
Study explores robust Orlicz spaces in finance, showing separability implications.
problem Understanding robustness in financial and economic contexts.
method Distinguished two constructions of robust Orlicz spaces: top-down and bottom-up.
result Separability of robust Orlicz spaces has strong implications for dominatedness and order completeness.
This paper improves speech recognition by using raw waveform signals in multi-span CNN acoustic models.
problem Improving speech recognition accuracy using raw waveform signals.
method Proposes a novel multi-span structure for acoustic modelling based on raw waveform signals with multiple CNN input layers.
result Multi-span acoustic models yield a lower word error rate (WER) than traditional FBANK feature-based models.
New spanning tree model connects knot homology, s-invariant, and exotic discs.
problem Understanding exotic discs in the 4-ball for knots.
method Explicitly defined differential in spanning tree complex, described Rasmussen's s-invariant.
result Identified new infinite family of knots bounding exotic discs.
New algorithms find optimal policies without knowing MDP span.
problem Finding optimal policies in MDPs without knowing span.
method Horizon calibration and span penalization techniques.
result First algorithms achieving optimal span-based complexity without prior knowledge.
Study asymptotic expansion of graph Laplacian on discretized surfaces, relating spanning trees and cycle-rooted forests.
problem Asymptotic expansion of graph Laplacian on discretized surfaces.
method Relate spanning trees and cycle-rooted spanning forests to zeta-regularized determinants.
result Explicit formula for limit of cycle-rooted spanning forest probability and topological observables.
New invariants measure how far spanning surfaces are from being compressible.
problem Understanding how essential spanning surfaces are in 3-manifolds.
method Introducing algebraic and geometric essence invariants, proving plumbing respects algebraic essence, and extending results to arbitrary 3-manifolds.
result Plumbing respects the algebraic essence of spanning surfaces, extending Ozawa's theorem.
Study Murasugi sum in 4D for knotted surfaces, defining arborescent surfaces.
problem Defining and understanding Murasugi sum in 4D for knotted surfaces.
method Introduced a 4D Murasugi sum to define arborescent knotted surfaces.
result Defined and studied arborescent knotted surfaces using 4D Murasugi sum.
Nonorientable spanning surfaces of periodic knots can have arbitrarily high first Betti number.
problem Periodic knots do not always have nonorientable spanning surfaces of high genus.
method Examples and calculations of nonorientable spanning surfaces of periodic knots.
result The first Betti number of nonorientable spanning surfaces can be arbitrarily large.
For a spanning tree T of a connected graph G and for a labelling φ: E(T) \rightarrow {+, -}, φis called an alternating sign on a spanning tree T of a graph G if for any cotree edge e \in E(G)-E(T), the unique path in T joining both end vertices of e has alternating signs. In the present note, we prove that any graph ha…
We investigate the time series of the degree of minimum spanning trees obtained by using a correlation based clustering procedure which is starting from (i) asset return and (ii) volatility time series. The minimum spanning tree is obtained at different times by computing correlation among time series over a time windo…
New methods evaluate stock market anomalies for prospect investors.
problem Determining if new securities or investment changes improve prospect investors' opportunities.
method Developed and implemented a new testing procedure for prospect spanning using subsampling and Linear Programming.
result Many well-known anomalies expand prospect investors' opportunity sets, indicating real economic value.
Plateau's problem is to find a surface with minimal area spanning a given boundary. In 1960, Reifenberg and Adams developed a definition for "span" using Čech homology, and variants of this definition have been used ever sense. However, limitations of Čech homology resulted in the lack of a natural definition for a bou…
LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.
problem Manual SLRs are impractical due to the scale and complexity of modern financial research.
method Domain experts define taxonomies and constraints, LLMs execute classification, and human evaluation ensures reliability.
result AI can understand and synthesize literature, revealing trends and core research directions.
We use the methods of Hedden, Juhasz, and Sarkar to exhibit a set of arborescent knots that bound large numbers of non-isotopic minimal genus spanning surfaces. In particular, we describe a sequence of prime knots K_{n} which will bound at least 2^{2n-1} non-isotopic minimal spanning surfaces of genus n.
Differentiable clustering method using perturbed spanning forests.
problem Efficient clustering in trainable pipelines with noisy data.
method Stochastic perturbations of minimum-weight spanning forests.
result Method performs well even in challenging settings.
In this paper we provide the first examples of non-flat soap films proven to span tetrahedra. These are members of a continuous two parameter family of soap films with tetrahedral boundaries. Of particular interest is a two parameter subfamily where each spanning soap film has the property that two minimal surfaces mee…
The paper explores neural networks for improving delta hedging in financial markets.
problem Real-world financial markets do not perfectly match the assumptions of the Black-Scholes model.
method The authors test various neural architectures (RNN, TCN, Attention, MLP) for delta hedging and combine them with traditional models.
result NNHedge framework provides a pipeline for model development and assessment.
We use a spanning tree model to prove a result of E. S. Lee on the support of Khovanov homology of alternating knots.