Novel framework synthesizes stochastic trajectories with anticipated structural breaks.
problem Synthesizing forward-looking, time-evolving stochastic trajectories with anticipated structural breaks.
method Anticipatory Neural Jump-Diffusion (ANJD) flow, AVNSG for dynamic spectral whitening.
result The framework effectively captures non-commutative moments and high-order stochastic texture.
ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.
problem Inaccurate foresight in non-Markovian environments due to state-based methods' limitations.
method Lifted state space into a signature-augmented manifold, using a self-consistent field approach to anticipate future path-law.
result ARL achieves deterministic evaluation of expected returns with reduced computational complexity and variance.
We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic "N-1" …
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
problem The nature of information structures and their impact on non-anticipative aggregation.
method Local reduction of pricing to the natural price filtration, stability properties, and the establishment of new facts.
result Non-anticipative signals can reveal future information, requiring dependence among signals (masking relation) and not independence.
Proves and tests methods for learning time-series with breaks.
problem Learning time-series with structural breaks.
method Complete proofs and experimental validation of a regularized loss function.
result Experimental results support the validity of the techniques.
Bayesian ARMA model with directional shifts captures structural breaks in compositional time series.
problem Structural breaks in compositional time series due to external shocks or policy changes.
method Developed a Bayesian Dirichlet ARMA model augmented with a directional-shift intervention mechanism.
result The model captures structural breaks through interpretable parameters and produces coherent probabilistic forecasts.
Paper proposes a method to efficiently estimate structural breaks in cointegrating regressions.
problem Estimating structural breaks in cointegrating regressions is challenging due to inconsistency of group lasso.
method Adaptive group lasso procedure using a first step group lasso estimation of diverging breakpoint candidates to produce weights for a second estimation.
result The adaptive group lasso estimator delivers consistent parameter changes and oracle properties.
Paper detects and estimates breaks in high-dimensional functional time series.
problem Detecting and estimating structural breaks in heterogeneous mean functions of high-dimensional functional time series.
method Proposes a new test statistic combining functional CUSUM and power enhancement components, with a clustering algorithm for group structure estimation.
result The proposed techniques have satisfactory performance in finite samples, detecting and estimating breaks effectively.
The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…
Anticipatory portfolios use richer models to optimize investments.
problem Optimizing investments with richer models than used for calibration.
method Decision-theoretic definition of anticipation, quadratic geometry, and LQG decomposition.
result Correct anticipation creates value, vacuous anticipation has zero value, and misspecified anticipation is harmful.
Proposes using Dynamic Mode Decomposition with delays for short-term human motion anticipation.
problem Lack of interpretability and explainability in neural network-based motion anticipation methods.
method Dynamic Mode Decomposition with delays for motion representation and prediction.
result Anticipation errors comparable or better than recurrent neural networks for very short times.
The paper examines how markets can anticipate and react to arbitrage opportunities, revealing biases and risks.
problem The tension between no arbitrage, information efficiency, and risk anticipation in markets.
method Continuous time analysis with model- or event-risk, allowing pre-horizon risk-resolution and Risk-Neutral Equivalent pricing.
result Optimised trading can suppress the anticipation of predictable risk-outcomes, creating an apparent Status Quo Bias.
This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.
problem Investors' mislearning of factor risk premia under structural breaks in asset pricing models.
method Proposes a minimal Bayesian framework to study how investors learn under a misspecified model that underestimates structural breaks.
result Elevated mislearning is associated with stronger long-horizon returns and Sharpe ratios, consistent with an equilibrium premium for acute model uncertainty.
New algorithm reduces simultaneous asset shocks in financial portfolios.
problem Reducing simultaneous asset shocks in financial portfolios.
method Uses semi-metrics to determine distance between asset structural breaks for portfolio optimization.
result Proposed method outperforms existing metrics in synthetic and real data, reducing volatility and drawdown.
Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely exchangeab…
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
problem Capturing anticipative information in financial markets with Brownian motion and Poisson processes.
method Using Malliavin calculus and filtration enlargement techniques, the paper computes the semimartingale decomposition of the processes.
result The paper provides the exact value of anticipative information in the pure jump case.
Classifies patterns of symmetry breaking and vacuum degeneracy in scalar and gauge fields.
problem Understanding patterns of symmetry breaking and vacuum degeneracy in complex field systems.
method Uses mathematical classification of singular foliations to encode and classify patterns of spontaneous symmetry breaking and vacuum degeneracy.
result Mathematical classification provides a qualitative understanding of possible patterns of vacuum degeneracy.
This article investigates the evolution of the Mexican stock market integration into the world market. First, we estimate the time-varying Mexican degree of market integration using an international conditional version of the CAPM with segmentation effects. Second, we study the structural breaks in this series. Finally…
The Lucas critique has exposed the problem of the trade-off between changes in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an empirical model…
In this paper, we study a class of Anticipated Backward Stochastic Differential Equations (ABSDE) with jumps. The solution of the ABSDE is a triple (Y,Z,ψ) where Y is a semimartingale, and (Z,ψ) are the diffusion and jump coefficients. We allow the driver of the ABSDE to have linear growth on the uniform norm of …
Derives functional Itô formula for non-anticipative maps of rough paths.
problem Functional Itô formula for non-anticipative maps of càdlàg rough paths.
method Approximation properties of the signature and Marcus transformation.
result Functional Taylor expansion for sufficiently regular non-anticipative maps.
Machine learning predicts criminal networks' missing partnerships and future behavior.
problem Predicting and understanding criminal networks' properties and future behavior.
method Combining graph representation learning and machine learning methods.
result Outstanding accuracy in predicting missing criminal partnerships and future behavior.
New model accurately predicts chemical bond breaking.
problem Challenges in describing bond breaking in quantum chemistry.
method Pretrained deep neural network wavefunction model Orbformer.
result Consistently converges to chemical accuracy (1 kcal/mol).
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established unde…
This study extends and evaluates the forecasting performance of the Singular Spectrum Analysis (SSA) technique using a general non-linear form for the re- current formula. In this study, we consider 24 series measuring the monthly seasonally adjusted industrial production of important sectors of the German, French and …
A discrete (finite-difference) analogue of differential forms is considered, defined on simplicial complexes, including triangulations of continuous manifolds. Various operations are explicitly defined on these forms, including exterior derivative and exterior product. The latter one is non-associative. Instead, as ant…
Geometric mechanism mimics physics' symmetry breaking.
problem Understanding spontaneous symmetry breaking in geometry.
method Analogous to physics, studying symmetry breaking in differential geometry.
result Symmetry breaking can be used to solve geometric problems.
Study examines extreme and erratic cryptocurrency behaviour during COVID-19.
problem Analyse extreme and erratic cryptocurrency behaviour during the pandemic.
method Analyze distribution extremities and structural breaks in 51 cryptocurrencies.
result Identify cryptocurrencies with most irregular extreme and erratic behaviour.
Least symmetry breaking principle explains SGD's local minima in shallow ReLU networks.
problem Understanding the structure of local minima in two-layer ReLU networks.
method Analyzing the squared loss optimization problem for ReLU networks with Gaussian inputs and applying the principle of least symmetry breaking.
result The principle of least symmetry breaking explains the structure of spurious local minima detected by SGD.
We find prominent similarities in the features of the time series for the overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations.
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
problem Comparing dynamics and strategies between cryptocurrency and equity markets.
method Random matrix theory, PCA, spectral dynamics, structural break analysis, portfolio simulation.
result Cryptocurrency and equity markets exhibit distinct evolutionary dynamics and time-varying sector behaviors.
In this article we prove that iterated renormalisations of Cr circle diffeomorphisms with d breaks, r>2, with given size of breaks, converge to an invariant family of piecewise Moebius maps, of dimension 2d. We prove that this invariant family identifies with a \textit{relative character variety} $χ(…
We consider the pricing of European-style structured credit payoff in a static framework, where the underlying default times are independent given a common factor. A practical application would consist of the pricing of nth-to-default baskets under the Gaussian copula model (GCM). We provide necessary and sufficient co…
Study uncovers tactical line-breaking passes in football using clustering.
problem Detecting and analyzing line-breaking passes in football matches.
method Unsupervised clustering-based framework using event and tracking data.
result Introduced tactical metrics to quantify pass effectiveness.
We obtain a decomposition of the call option price for a very general stochastic volatility diffusion model extending the decomposition obtained by E. Alòs in [2] for the Heston model. We realize that a new term arises when the stock price does not follow an exponential model. The techniques used are non anticipative. …
AntLer anticipates future learning to improve control performance.
problem Improving control performance through online learning is not well understood.
method AntLer uses a probabilistic model to anticipate future learning and optimize control parameters.
result AntLer approximates optimal solutions with high probability.
Paper proposes a VB method for TS-SBP mixture models with reduced computational cost.
problem Efficiently learning tree-structured stick-breaking process mixture models.
method Utilizes Bayes coding algorithm for context tree models to calculate sums over all possible trees.
result Proposes a learning algorithm with less computational cost for TS-SBP mixture of Gaussians.
Researchers create BPS monopoles with any desired symmetry breaking.
problem Creating monopoles with specific symmetry breaking patterns.
method Using a new class of Nahm data to construct finite energy BPS monopoles.
result Arbitrary symmetry breaking monopoles can be constructed.
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
problem Maximizing logarithmic utility for an insider with different anticipating techniques.
method Comparison of Russo-Vallois forward and Skorokhod integrals.
result Skorokhod insider outperforms forward insider in logarithmic utility maximization.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
The paper classifies and proves properties of symmetry breaking operators for specific groups.
problem Classifying and understanding symmetry breaking operators for de Sitter and Lorentz groups.
method Constructing and classifying differential symmetry breaking operators, proving localness, and showing sporadic nature.
result All symmetry breaking operators are differential and sporadic, not obtainable by residue formulas.
Study compares different integrals for optimal portfolio optimization with insider information.
problem Optimizing portfolios in a financial market with insider information.
method Anticipating stochastic calculus and various integrals (Russo-Vallois forward, Ayed-Kuo, Hitsuda-Skorokhod).
result The Hitsuda-Skorokhod and Ayed-Kuo integrals do not provide a financially meaningful investment strategy.
We give a complete classification of intertwining operators (symmetry breaking operators) between spherical principal series representations of G=O(n+1,1) and G'=O(n,1). We construct three meromorphic families of the symmetry breaking operators, and find their distribution kernels and their residues at all poles explic…
Investigates spontaneous symmetry breaking in non-equilibrium systems.
problem Spontaneous symmetry breaking of ergodicity in non-equilibrium systems.
method Mathematical and effective field theory approaches to investigate symmetry breaking.
result Symmetry breaking phenomena observed in stochastic processes.
Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments…
Adaptive models improve electricity demand forecasting during lockdown.
problem Poor load forecasting due to sudden consumption changes during lockdown.
method Adaptive generalized additive models with Kalman filters and expert aggregation.
result Significant reduction in prediction errors compared to traditional models.
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for int…