New methods for multicategory classification with reject and refine options reduce misclassification costs.
problem Reducing misclassification costs in multicategory classification problems.
method Margin-based multicategory classification methods with reject and refine options.
result The refine option provides more constructive information by ruling out implausible classes.
A new framework forecasts implied volatility surfaces by separating learning and refinement stages.
problem Forecasting implied volatility surfaces is challenging due to stochastic future surfaces and static no-arbitrage constraints.
method Decoupled generative refinement framework using a conditional diffusion model and SAAM for surface refinement.
result The framework improves forecasting accuracy and reduces static no-arbitrage violations.
Improved hedging strategy for SABR model options.
problem Inaccurate conventional SABR delta hedging.
method Theoretical justification of Bartlett's delta.
result Bartlett's delta provides more accurate hedging.
Barrieu, Rouault, and Yor [J. Appl. Probab. 41 (2004)] determined asymptotics for the logarithm of the distribution function of the Hartman-Watson distribution. We determine the asymptotics of the density. This refinement can be applied to the pricing of Asian options in the Black-Scholes model.
NNLCI improves options pricing efficiency with minimal training data.
problem Efficiently pricing multi-asset options with high-dimensional models.
method Neural Networks with Local Converging Inputs (NNLCI) for numerical corrections.
result Reduces RMSE by 4-12 times with minimal training data.
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
problem Lack of effective trading strategies in volatile Chinese equity markets.
method Volatility forecasting using GARCH models to dynamically adjust positions and exposures.
result Dynamic adjustment of positions and exposures enhances returns in volatile markets.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
The model outperforms other models in option pricing, especially for short-term implied volatility.
problem Improper calibration and pricing of exotic options in financial models.
method Stochastic volatility model with double-exponential jumps, Fourier pricing techniques.
result The model outperforms other models in fitting the short-term implied volatility smile and pricing exotic options.
New method for precise option pricing in stochastic volatility models.
problem Analyzing large classes of stochastic volatility models for robust option pricing.
method Theory of regularity structures and Laplace method on the space of models.
result Precise asymptotics for European options in rough volatility models.
The paper models Gasoil options using Brent benchmarks, improving volatility estimation.
problem Inability to directly model illiquid Gasoil options market.
method Jointly models Brent and Gasoil futures prices with a correlated Bachelier model, estimating volatility spread.
result The proposed framework accurately maps Brent implied volatilities to Gasoil implied volatilities.
Refining a discrete model of Cheuk and Vorst we obtain a closed formula for the price of a European lookback option at any time between emission and maturity. We derive an asymptotic expansion of the price as the number of periods tends to infinity, thereby solving a problem posed by Lin and Palmer. We prove, in partic…
Model predicts option movements using residual transactions for better market timing.
problem Predicting option movements using standard metrics like open interest and trading volume.
method Analyzes residual transactions, integrates machine learning and regression techniques.
result Identifies early indicators of market trends for better option price forecasting.
Improved pricing method for American options in various models.
problem Efficient pricing of American options in jump-diffusion models and barrier options.
method Hybrid method combining perturbative arguments and quadratic approximation.
result Higher order approximations provide significantly more pricing accuracy.
We refine option pricing near-the-money skew in rough fractional volatility models.
problem Approximating near-the-money skew in rough fractional volatility models.
method Proved higher order moderate deviation estimates for rough fractional volatility models.
result Allowed application of skew approximation formulae to wider moderate deviations regime.
Improved fourth-order compact scheme for option valuation with Robin boundary condition.
problem Lower convergence rates in numerical methods for American options.
method High-order compact scheme, Robin boundary condition, coupled nonlinear PDEs.
result Fourth-order convergence rate achieved without mesh refinement.
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…
Enhances CEV model pricing with high-order scheme and adaptive time stepping.
problem Improving accuracy in pricing American CEV models with irregularities.
method High-order time adapted scheme, local mesh refinement, adaptive time stepping, fifth-order 5(4) Dormand-Prince method.
result Highly accurate solution with reduced computational runtime.
Hybrid model uses LLM to build transparent Bayesian networks for trading decisions.
problem Rigorous and transparent reasoning required in financial trading, especially for options strategies.
method Combines LLM strengths with Bayesian Networks, using LLM to construct context-specific networks and select relevant data.
result Empirically, the hybrid system outperforms market benchmarks with superior risk-adjusted performance.
Gonogo offers tools for sensitivity experiments in R.
problem Conducting, analyzing, and simulating sensitivity experiments.
method Suite of R functions for various adaptive procedures.
result Achieving overlapping data and refining testing in distribution tails.
Volatility models must be rough to match market skew.
problem Inconsistent non-rough volatility models with power law volatility skew.
method Asymptotic expansion and continuous price dynamics analysis.
result Volatility must be rough to align with market skew.
RIDGE validates LLM-generated financial models using diverse tests.
problem Validating LLM-generated financial models requires more than traditional testing.
method RIDGE uses structured tests, stress tests, and consistency checks.
result RIDGE identifies and removes implementation defects, leading to new methodologies.
FlashIV solves Black-Scholes implied volatility efficiently and accurately.
problem Efficiently calculating implied volatility for financial models.
method Normalizes inputs, uses asymptotic seed, and combines Householder refinement.
result Runs faster than existing methods while maintaining accuracy.
We describe a high performance parallel implementation of a derivative pricing model, within which we introduce a new parallel method for the calibration of the industry standard SABR (stochastic-αβρ) stochastic volatility model using three strike inputs. SABR calibration involves a non-linear three dimensional minimis…
We propose a new approach to solve optimal stopping problems via simulation. Working within the backward dynamic programming/Snell envelope framework, we augment the methodology of Longstaff-Schwartz that focuses on approximating the stopping strategy. Namely, we introduce adaptive generation of the stochastic grids an…
New inequality for refined knot invariants in a specific space.
problem General adjunction inequality for refined s-invariants does not hold. method Introduced an adjunction inequality for a specific spatial refinement in kCP2. result An adjunction inequality holds for the s-version of the Sq1-refinement in kCP2. Proposes GLWB-LTC for enhanced life care annuities with dynamic withdrawal strategies and stochastic interest rates.
problem Improving life care annuity features and pricing methods.
method Introduces GLWB-LTC with dynamic withdrawal strategies and stochastic interest rates. Solves the stochastic control problem using a robust tree method.
result Optimal withdrawal strategies vary over time with policyholder's health status, highlighting the advantage of flexibility.
Refines Khovanov homology using stable homotopy theory.
problem Khovanov homology needs refinement.
method Stable homotopy refinement approach.
result Stable homotopy refinement of Khovanov homology constructed.
We study refined topological string theory in the presence of orientifolds by counting second-quantized BPS states in M-theory. This leads us to propose a new integrality condition for both refined and unrefined topological strings when orientifolds are present. We define the SO(2N) refined Chern-Simons theory which co…
Refines neural network predictions using background knowledge for improved accuracy.
problem Compensate for lack of labeled data in neural networks.
method Introduces differentiable refinement functions and Iterative Local Refinement (ILR) algorithm to refine predictions efficiently and accurately.
result ILR finds competitive results in MNIST addition task and refines predictions on complex SAT formulas.
New research shows label refinement and weak training have limitations for aligning LLMs.
problem Limitations of refinement methods for aligning large language models.
method Analyzed probabilistic assumptions and alternative approaches to label refinement and weak training.
result Label refinement and weak training suffer from irreducible error, leaving a performance gap.
Refines Khovanov homology for knots with involution.
problem Detecting mutations in knots with an involution.
method Introduces a triply-graded theory with two filtrations.
result Shows the refinement can detect mutation.
This paper provides fast estimates for complex option types.
problem Estimating prices for constrained multiple exercise American options.
method Lookahead search for lower estimates and nearest-neighbor martingale for upper estimates.
result Probabilistic convergence guarantees for the algorithms.
Study bounds for prices of European and American options with optional termination.
problem Bounding prices of options with potential termination.
method Duality results linking upper prices of vulnerable options to American options with constrained exercise times.
result Linking upper prices of vulnerable options to American options and game options.
In a previous paper we constructed a spectrum-level refinement of Khovanov homology. This refinement induces stable cohomology operations on Khovanov homology. In this paper we show that these cohomology operations commute with cobordism maps on Khovanov homology. As a consequence we obtain a refinement of Rasmussen's …
New homotopy refinements for tangle invariants defined.
problem Stable homotopy refinements for tangle invariants.
method Defined stable homotopy refinements of Khovanov's arc algebras and tangle invariants.
result Stable homotopy refinements of Khovanov's arc algebras and tangle invariants defined.
New homotopy refinements for tangle invariants.
problem Stable homotopy refinements for tangle invariants.
method Refined Khovanov and Chen-Khovanov spectra.
result Induces refinements of platform algebras and invariants.
Refined 3D index uses surgery and gradings to distinguish 3-manifolds.
problem Distinguishing 3-manifolds and gauge theories phases.
method Dehn surgery presentation, ideal triangulation, and enhanced flavor symmetries.
result Invariance of refined index under various transformations.
New option pricing formulas for American and Bermudan options.
problem Traditional option pricing models assume constant volatility and interest rate.
method Relaxing assumptions, using square root of Brownian motion, providing closed-form formulas.
result Simple, closed-form pricing formulas for American and Bermudan options.
Path integral method calculates barrier option prices.
problem Barrier option pricing in finance.
method Path integral method applied to trapezoid and square potential barriers.
result Analytical expressions for option pricing derived.
New framework identifies hidden risks and optionality in American options.
problem Underestimation of flexibility and convexity in early-exercise features.
method Introducing stochasticity into underlying determinants to quantify hidden risks and optionality.
result Remedies conventional pricing systems that underestimate optionality.
Estimates time-varying network connections using multi-stage smoothing.
problem Estimating edge probabilities of time-varying networks.
method Multi-stage smoothing: temporal local smoothing followed by node-domain smoothing.
result Captures both smooth temporal evolution and structural patterns in connectivity.
American options can be equivalent to European options under certain conditions.
problem Determining when American options can be simplified to European options.
method Using methods from Jourdain and Martini, Chrsitensen, and convex duality.
result A first step towards verifying representability of American options.
There exist several methods how more general options can be priced with call prices. In this article, we extend these results to cover a wider class of options and market models. In particular, we introduce a new pricing formula which can be used to price more general options if prices for call options and digital opti…
Path integral method calculates PDBS option prices with time-dependent parameters.
problem Pricing proportional double-barrier step options with time-dependent interest rates and volatilities.
method Path integral method applied to a quantum mechanical analogy of barrier options.
result Derivation of pricing kernel for PDBS options with time-dependent parameters.
The paper offers methods to price complex options using upper and lower bounds.
problem Pricing complex options like Asian and basket options.
method Develops a general framework using lower and upper bounds.
result Lower bounds simplify the problem and provide reasonable approximations.
The paper refines large N duality for knots using M-theory and string theory.
problem Understanding refined Chern-Simons invariants of knots.
method Formulating large N duality in refined Chern-Simons theory with torus knots, studying refined BPS states in M-theory, and relating to Type IIA string theory.
result The duality predicts graded dimensions of cohomology groups of moduli spaces of M2-M5 bound states associated to torus knots in the resolved conifold.
Algorithm learns mixtures of Markov chains and MDPs from short trajectories.
problem Learning mixtures of Markov chains and MDPs from short unlabeled trajectories.
method Subspace estimation, spectral clustering, EM algorithm, model estimation, classification.
result 96.6% average accuracy on a mixture of two MDPs in gridworld, outperforming EM algorithm with random initialization.
Financial option insurance protects investors from option premiums losses.
problem Risk associated with financial option investments.
method Integrating insurance concepts with financial options, creating a three-entity framework and a mathematical model.
result Protection of option investors and minimization of insurer's risk.