Study optimal dynamic basis trading strategies with stochastic basis model.
problem Optimal dynamic trading of futures and underlying asset under stochastic basis.
method Model basis evolution as stopped scaled Brownian bridge, solve utility maximization problem with HARA risk preferences.
result Derive exact conditions for optimal trading strategies and solve explicitly.
A new kernel improves statistical surrogates for stochastic manifolds with diverse data.
problem Handling statistical surrogates for stochastic manifolds with heterogeneous data.
method A transient anisotropic kernel is introduced to improve statistical surrogates for stochastic manifolds with heterogeneous data.
result The transient anisotropic kernel provides a better representation of statistical dependencies in the learned probability measure.
Two RBF methods solve complex financial derivatives pricing problems.
problem Pricing derivatives in models with multiple stochastic factors.
method Radial Basis Function Partition of Unity and Radial Basis Function generated Finite Differences methods.
result Both methods achieve high accuracy and are efficient for solving multi-dimensional PDEs.
Improved classification of PolSAR data using SVM with stochastic distances and radial basis functions.
problem Improper training samples in PolSAR data classification.
method Combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM).
result SVM with the proposed kernel functions achieves better performance than Minimum Distance classification.
Neural Chaos uses neural networks instead of polynomials for stochastic modeling.
problem Challenges in constructing surrogate models with uncertainty quantification for complex or high-dimensional stochastic processes.
method Adopting spectral expansion formalism with neural network basis functions, identifying them data-drivenly without prior assumptions.
result Demonstrates effectiveness of the proposed scheme through numerical examples of varying complexity.
Scalable Gaussian process models trained with unbiased stochastic ELBO.
problem Training large capacity Gaussian process models on huge datasets.
method Unbiased stochastic variational inference for scalable GPs.
result Accurate inference on large datasets with up to 10 million basis functions.
Study on hedging and valuation of basis risk in incomplete markets with partial information.
problem Hedging and valuation of European and American claims in an incomplete market with correlated assets and partial information.
method Stochastic control and partial information scenario, forward indifference valuation, dual representation, PDE approach.
result Derivation of optimal hedging strategy and forward indifference price representation for claims.
New model solves complex SDEs with high-dimensional spatial and stochastic spaces.
problem Solving SDEs with high-dimensional spatial and stochastic spaces.
method Physics-informed deep generative model (sPI-GeM) combining PI-BasisNet and PI-GeM.
result Scalable solution for high-dimensional SDE problems.
Paper introduces a new method for solving complex stochastic equations.
problem Solving forward-backward stochastic differential equations with jumps.
method Linear basis function regression technique.
result The proposed method is convergent and effective as shown by numerical experiments.
Study optimizes pension scheme risk-sharing for longevity bonds.
problem Managing longevity basis risk in pension schemes with income-drawdown guarantees.
method Stochastic optimal control, dynamic programming, HJB equations.
result Sharing longevity risk increases both manager and member utilities.
Improved reinforcement method for optimal control problems.
problem Optimal control problems with limited computational cost.
method Reinforced least squares Monte Carlo method for stochastic control problems.
result Significant improvement in method's efficiency and accuracy.
The paper analyzes credit valuation adjustments under collateralized interest rate derivatives, introducing a new dynamics for multiple interest rate curves.
problem The impact of multiple interest rate curves on credit valuation adjustments under collateralized models.
method Formulated a consistent dynamics for multiple interest rate curves, including the margin period of risk and stochastic basis for wrong-way risk analysis.
result Numerical results confirm the importance of stochastic basis for proper wrong-way risk analysis of sensitive products like basis swaps.
Proposes a new method to learn entire solution paths without discretization.
problem Optimizing a family of problems indexed by hyperparameters.
method Parameterizes the solution path with basis functions and solves a single stochastic optimization problem.
result Uniform error of learned path converges linearly to a constant related to basis expressiveness.
The sum-product or belief propagation (BP) algorithm is a widely used message-passing technique for computing approximate marginals in graphical models. We introduce a new technique, called stochastic orthogonal series message-passing (SOSMP), for computing the BP fixed point in models with continuous random variables.…
MESSY estimation recovers symbolic density functions from samples using maximum entropy.
problem Estimating probability density functions from limited samples.
method Maximum-Entropy approach with gradient flow and symbolic regression.
result Efficiently finds optimal symbolic expressions for unknown distributions.
The paper analyzes the stochastic frontiers of technological innovation using fractal dimensions.
problem Determining the levels of causality in technological innovation using fractal dimensions.
method The study uses high-frequency data to analyze the stochastic frontiers of production possibilities with level N of partitions in time.
result The main finding is the accuracy and power of indexing the levels of causality in technological innovation.
DBKs enable scalable GPs with tractable inference for large datasets.
problem Scaling Gaussian processes to large and complex datasets while maintaining tractable inference.
method DBKs constructed from neural-network-parameterized basis functions with explicit low-rank structure, enabling linear-complexity inference.
result DBKs provide a unified perspective and improve predictive accuracy, uncertainty quantification, and computational efficiency.
The paper deals with incorporating statistical uncertainty in decision-making.
problem Statistical uncertainty in decision-making.
method Theory of nonlinear expectations.
result Explicit and consistent incorporation of uncertainty in decision valuation.
Algorithm of multicurrency trading at the market of Forex is realized on the basis of nonlinear stochastic wavelets. The distinctive feature of the algorithm is the possibility of weakly- and strongly connected horizontal self-assemblies, as well as use of nested structures. On-line trading with eight currency couples …
Derives portfolio dynamics using retarded action principle.
problem Modeling self-financing portfolio dynamics with causality.
method Retarded action principle for differential representation.
result Consistent representation of portfolio dynamics.
Introduces a neural network-based method for efficient state and parameter estimation in complex systems.
problem Efficiently estimating state paths and parameters from noisy measurements in high-dimensional nonlinear systems.
method Bayesian Information Field Theory with neural network parameterization and optimization algorithms.
result Proposes a method to simplify and enrich state path parameterizations using neural networks, improving inference accuracy.
The maximum likelihood approach is adapted to the problem of estimation of drift and diffusion functions of stochastic processes from measured time series. We reconcile a previously devised iterative procedure [Kleinhans et al., Physics Letters A (346), 2005] and put the application of the method on a firm theoretical …
The paper defines and implements risk-indifference pricing for American-style contingent claims.
problem Pricing American-style contingent claims under uncertainty.
method Indifference pricing using convex risk measures and stochastic volatility models, with numerical solutions via deep learning.
result Characterization of indifference prices via Backward Stochastic Differential Equations (BSDEs).
This study examines the collateral choice option and its valuation and hedging.
problem Non-zero collateral basis spreads impact asset valuation and require complex modeling.
method Develops a stochastic valuation model for the collateral choice option and proposes hedging strategies.
result The stochastic model attributes risks to all involved collateral currencies, unlike the deterministic model.
Modified model for Quanto CDS pricing with stochastic recovery and reduced complexity.
problem Modeling Quanto CDS with stochastic recovery and reduced complexity of interest rate.
method Modified Itkin, Shcherbakov, and Veygman (2019) model with RBF-FD method.
result Influence of recovery rate volatility and mean-reversion on Quanto CDS spread.
New framework quantifies uncertainty in reduced-order models for PDEs.
problem Quantifying reliability of reduced-order model predictions for PDEs.
method Combining stochastic representation of reduced bases with conformal-type methods.
result Provides prediction sets with coordinate miscoverage guarantees.
New methods reduce computational cost for Gaussian Markov Random Fields with sparse constraints.
problem Inference and simulation of GMRFs are computationally prohibitive with many constraints.
method Proposes a basis transformation into blocks of constrained and non-constrained subspaces.
result Significantly outperforms existing alternatives in computational cost.
Unified analysis of asynchronous stochastic optimization with perturbed iterates.
problem Asynchronous stochastic optimization methods and their convergence rates.
method Perturbed iterate analysis for asynchronous stochastic optimization.
result Unified and simplified analyses of asynchronous optimization algorithms.
Calibrating American options is sped up using model reduction techniques.
problem Calibrating American options is computationally challenging due to their flexibility and constraints.
method Two model reduction strategies: reduced basis method and de-Americanization.
result Calibration process is significantly faster with reduced model complexity.
New method estimates SDE parameters efficiently using WCE and SGD.
problem Parameter estimation for stochastic differential equations.
method Wiener Chaos Expansion and Stochastic Gradient Descent.
result Accurate parameter recovery from noisy observations.
We model messaging activities as a hierarchical doubly stochastic point process with three main levels, and develop an iterative algorithm for inferring actors' relative latent positions from a stream of messaging activity data. Each of the message-exchanging actors is modeled as a process in a latent space. The actors…
New model for intervention control in markets solves tuning problem.
problem Optimal intervention control in commodity and stock markets.
method Developed a stochastic intervention control model as a Markov process with discrete time, solved the tuning problem.
result Obtained a deterministic optimal control for the model, with an explicit analytical representation.
This paper considers method of creation of an advisor and indicator based on the spectral stochastic analysis model, both with linear and non-linear approximation. The problem of entrance to one or another trade position is solved on the basis of combined analysis of dynamics of quotations of all currency pairs, what a…
New stochastic gradient descent with random search directions improves efficiency and convergence.
problem Efficiency and convergence of stochastic gradient descent methods.
method Developed a new class of stochastic gradient descent algorithms with random search directions.
result Established almost sure convergence and provided Lp rates of convergence. The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
Generative models improve digital twins for structures with uncertainties.
problem Uncertainty in structural modelling limits deterministic models.
method Two types of generative models: physics-based SFE and data-driven cGANs.
result Data-driven cGANs outperform physics-based models in nonlinear structures.
Adaptive sampling method reduces variance in stochastic optimization.
problem Reducing variance in stochastic optimization with limited gradient computations.
method Adaptive increase in sample size based on inner product test.
result Algorithm converges globally on nonconvex functions and linearly on strongly convex functions.
Statistical emulators speed up longevity risk contract valuation.
problem Valuation of mortality-linked contracts in complex stochastic models.
method Use of machine learning to create a flexible non-parametric surrogate for stochastic processes.
result Performance guarantees and removal of nested simulation need.
The paper proposes a method to improve Koopman operator estimation using indicator functions.
problem Difficulty in identifying good observables for Koopman operator expansion.
method Clustering procedure based on Hidden Markov Model (HMM) to infer surrogate observables.
result Inferred indicator functions significantly improve estimation of Koopman operator eigenvalues and transition timescales.
Unified framework for gradient estimation in combinatorial spaces.
problem Scaling relaxed gradient estimators to large combinatorial distributions.
method Introducing stochastic softmax tricks within the perturbation model framework.
result Stochastic softmax tricks improve model performance and discover more latent structure.
Hybrid model prices vulnerable options with stochastic volatility.
problem Pricing options with stochastic volatility and credit risk.
method Closed-form hybrid credit risk model with Heston-Nandi GARCH processes.
result Explicit pricing formula for vulnerable options.
The paper explains why futures prices often differ from spot prices in grain markets.
problem Non-convergence of futures and spot prices in grains markets.
method Incorporates stochastic spot price and storage cost, solves an optimal double stopping problem.
result Explicit no-arbitrage prices for shipping certificates and futures contracts are derived.
Paper presents a new insurance model equation for diverse structures.
problem Handling diverse insurance models with a single equation.
method Developed a canonical model construction and stochastic backward equations.
result Comparison theorems for different models follow from the new equation.
New optimization algorithm for mixed-variable problems improves efficiency.
problem Optimizing functions with both continuous and categorical variables.
method Combines radial basis function and metric stochastic response surface methods with modifications for categorical variables and parallel processing.
result Numerical experiments show the effectiveness of the proposed modifications.
This paper conditions non-linear infinite-dimensional diffusion processes.
problem Conditioning non-linear and infinite-dimensional diffusion processes.
method Infinite-dimensional Girsanov's theorem to condition function-valued stochastic processes.
result Conditioning of non-linear infinite-dimensional diffusion processes is achieved.
This paper simplifies hedge ratios in financial models using pathwise algorithmic differentiation.
problem Expensive and unstable computation of hedge ratios from pathwise sensitivities.
method Develops reduced stochastic hedge ratios of the form φ_j^r = Σ_j^r ξ_j^q X_q, retaining sensitivity tensor through empirical averages.
result Two coefficient criteria are introduced to minimize pathwise residuals and satisfy moment equations.
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
The paper shows how to stabilize off-policy reinforcement learning using specific state representations.
problem Stability issues in reinforcement learning with function approximation and off-policy learning.
method Formal analysis of representation learning schemes based on the transition matrix of a policy.
result Schur and orthogonal bases of the Krylov subspace provide stable representations for TD learning.