Modeling time-varying extreme value dependence in European stock markets.
problem Non-stationary extremal dependence between European stock markets.
method Regression model for angular density of bivariate extreme value distribution.
result Evidence of increasing extremal dependence in recent years.
New tail dependence measures for stock indices.
problem Measuring tail dependence between financial variables.
method Introducing a new stochastic order and studying monotone tail dependence measures.
result Advantage of new tail dependence measures over classical ones.
Proposes a new dependency function for measuring non-linear relationships.
problem Need for a general-purpose measure of dependency between random variables.
method Revision of ideal properties and proposal of a new dependency function.
result Proposes a new dependency function that meets all desired properties.
The paper explores the relationship between joint mixability and negative dependence structures.
problem Understanding the connection between joint mixability and various negative dependence concepts.
method Analyzes the properties of joint mixes and their relation to negative dependence structures.
result Derives necessary and sufficient conditions for a joint mix to be negatively dependent.
Extend classical theory of affine processes to path-dependent setting
problem Path-dependent affine processes
method Introduce path-dependent coefficients and provide analytic formulas for their Fourier--Laplace transform
result Define path-dependent affine processes through their exponential-affine Fourier--Laplace transform and establish a characterization theorem
Forecaster uses graph Transformers to forecast spatial and time-dependent data.
problem Complex spatial and temporal dependencies in data.
method Graph Transformer architecture with sparsification for spatial and temporal dependencies.
result Forecaster significantly outperforms state-of-the-art baselines in taxi demand forecasting.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
Copulas reveal strong positive dependencies in stock demand fluctuations due to volume imbalances.
problem Analyzing dependencies of stock demands using local volume fluctuations.
method Copula analysis of empirical data to model dependence structures.
result Large local fluctuations of signed traded volumes increase positive dependencies in demand but slightly lower negative ones.
The paper proposes a framework to adjust dependency measure estimates for chance.
problem Challenges in interpreting and ranking dependency measures on finite samples.
method Simple adjustments to improve interpretability and accuracy of dependency measures.
result Improves interpretability and accuracy of dependency measures, demonstrated on MIC and random forests.
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
problem Challenges in predicting flow due to spatial-temporal dependencies.
method Spatial-Temporal Self-Attention Network (ST-SAN) that addresses temporal and spatial dependencies.
result Significant improvement in flow prediction accuracy (9% in inflow, 4% in outflow) compared to state-of-the-art methods.
Proposes MCDE for estimating dependency in data streams.
problem Estimating dependency of variables in data streams is challenging.
method Monte Carlo Dependency Estimation (MCDE) framework.
result MWP dependency estimator shows superiority over state-of-the-art measures.
The paper develops methods to price and hedge options in path-dependent stock models.
problem Pricing and hedging options under complex stock models.
method Develops a path-dependent PDE for option pricing and differentiability of path-dependent SDE solutions.
result Provides formulas for option Greeks and differentiability of path-dependent SDE solutions.
New inequalities for learning from graph-dependent data with stability bounds.
problem Learning from dependent data with graph dependency.
method Proved McDiarmid-type concentration inequalities for graph-dependent variables, showed concentration relies on forest complexity.
result Proved stability bounds for learning from graph-dependent data.
Survey on learning with graph-dependent data, deriving new generalization bounds.
problem Traditional i.i.d. data assumption fails in many real-life applications.
method Collect and analyze graph-dependent concentration bounds, derive generalization bounds.
result New generalization bounds for graph-dependent data.
Paper tackles instance-dependent label noise by approximating it with part-dependent noise.
problem Learning with instance-dependent label noise is challenging.
method Approximate instance-dependent label noise with part-dependent noise. Use transition matrices for parts to model noise.
result Method outperforms state-of-the-art approaches for instance-dependent label noise.
Study on diversification of α-stable risks, revealing limits to diversification due to tail dependence.
problem Diversification of α-stable risks with tail dependence. method Analysis of aggregated Value-at-Risk under different tail dependence structures.
result Limits to diversification are violated, especially for low tail index values and positive dependence.
Study shows significance of vector field dependence in control theory.
problem Existence of non-trivial singular trajectories in dependence locus.
method Geometric control theory approach.
result Generic appearance of non-trivial singular trajectories.
In data science, it is often required to estimate dependencies between different data sources. These dependencies are typically calculated using Pearson's correlation, distance correlation, and/or mutual information. However, none of these measures satisfy all the Granger's axioms for an "ideal measure". One such ideal…
Deep signature algorithm for pricing path-dependent options.
problem Pricing path-dependent options with complex payoff functions.
method Extended backward scheme for state-dependent FBSDEs with reflections, incorporating signature layer for path-dependent FBSDEs.
result Convergence analysis of the algorithm with explicit dependence on truncation order and neural network approximation errors.
Model discovers dynamic dependencies in multivariate time series data.
problem Discovering non-linear and time-variant dependencies in multivariate time series.
method seq2graph model using multi-layer GRUs and multi-level attention.
result Jointly trained model discovers both lagged and inter-dependencies accurately.
New measures capture tail dependence and non-exchangeability in financial data.
problem Underestimation of tail dependence and inability to capture non-exchangeable tail dependence.
method Tail copulas and novel tail dependence measures (MTCM, ATCM) are proposed.
result Captures non-exchangeable tail dependence and provides analytical forms for various copulas.
Study on pairwise counter-monotonicity, a type of negative dependence.
problem Understanding and quantifying extremal negative dependence structures.
method Established stochastic representation and invariance property; showed implications and connections.
result Pairwise counter-monotonicity implies negative association and joint mix dependence.
This paper presents a new methodology for clustering multivariate time series leveraging optimal transport between copulas. Copulas are used to encode both (i) intra-dependence of a multivariate time series, and (ii) inter-dependence between two time series. Then, optimal copula transport allows us to define two distan…
Study on generalization for data-dependent hypothesis sets.
problem Understanding generalization in hypothesis sets dependent on data.
method Learning guarantee based on transductive Rademacher complexity and hypothesis set stability.
result Generalization bound for data-dependent hypothesis sets.
Modeling financial institution dependence structures for systemic risk.
problem Understanding and measuring systemic risk in financial systems.
method Dynamic model of dependence structure using Markov structures of joint credit migrations.
result Different Markov structures with distinct dependence structures lead to varying systemic instability.
Study nearest-neighbor radii under dependent sampling, finding they remain informative.
problem Analyzing nearest-neighbor radii under dependent sampling.
method Consider strong mixing dependent observations, establish distribution-free almost sure convergence and sharp non-asymptotic moment bounds.
result Nearest-neighbor geometry remains informative under dependence sampling.
Study of time-dependent metrics and connections in geometry.
problem Understanding geodesics and connections in time-dependent Riemannian manifolds.
method Examine connections on product manifolds, explore parallel transport, geodesics, and torsion.
result Define the derivative of a one-parameter family of connections.
Estimates binary labels from dependent data using Markov Random Fields.
problem Statistical estimation from dependent data across spatial, temporal, and social domains.
method Modeling dependencies as Markov Random Fields and providing efficient estimation algorithms.
result Statistically efficient estimation rates for Ising models from a single sample.
Develops a numerical scheme for solving path-dependent FBSDEs and PDEs.
problem Solving path-dependent FBSDEs and PDEs numerically.
method Picard iteration method for FBSDEs, concentration inequality for estimator, supervised learning with neural networks for PDEs.
result Proves convergence and rate of convergence for the Picard iteration method.
Paper examines quantile dependence and volatility forecasting between US and international markets.
problem Improving volatility forecasting between international stock markets.
method Cross-quantilogram analysis and quantile-augmented volatility model.
result Quantile dependence and directional predictability between US and international markets improve volatility forecasting.
This paper improves tail dependence analysis by introducing a path-based approach.
problem The classical tail dependence coefficient fails to capture non-exchangeable features of tail dependence.
method The paper introduces a path-based maximal tail dependence approach to capture the most pronounced feature of dependence over all possible paths.
result The paper proves the existence and provides an explicit characterization of the path-based maximal TDC, improving analytical and computational tractability.
Develops path-dependent optimal transport for exotic derivatives calibration.
problem Calibrating volatility models to exotic derivatives prices.
method Semimartingale optimal transport in path-dependent setting, duality results, dimension reduction via semifiltrations.
result Exact calibration of volatility models to path-dependent derivative prices.
Bayesian test assesses dependence between mixed data types.
problem Assessing dependence between text, image, and sound data.
method Bayesian kernelised correlation test using Dirichlet process model.
result Demonstrated effectiveness compared to other methods.
Project estimates risk-neutral dependence from option prices.
problem Extracting risk-neutral dependence from option prices.
method Projection estimator using portfolios of observed options.
result Estimates risk-neutral dependence in incomplete markets.
Proposes DR-ACI for causal effect intervals with temporal dependence.
problem Causal effect intervals under temporal dependence.
method Doubly robust adaptive conformal inference (DR-ACI).
result Constructs prediction intervals for causal effects.
Optimizes insurance pricing to minimize ruin probability under various claim dependencies.
problem Determining optimal insurance premiums in the presence of dependencies between claim occurrences.
method Analyzes both independent and dependent claim processes, considering single and multiple risks.
result Optimal insurance premiums depend on initial reserve and claim dependencies.
This paper presents theory for Normalized Random Measures (NRMs), Normalized Generalized Gammas (NGGs), a particular kind of NRM, and Dependent Hierarchical NRMs which allow networks of dependent NRMs to be analysed. These have been used, for instance, for time-dependent topic modelling. In this paper, we first introdu…
Dupire's functional Itô calculus provides an alternative approach to the classical Malliavin calculus for the computation of sensitivities, also called Greeks, of path-dependent derivatives prices. In this paper, we introduce a measure of path-dependence of functionals within the functional Itô calculus framework. Name…
Geometrizes time-dependent mechanics using second order tangent bundles.
problem Modeling time-dependent Lagrangian mechanics.
method Introducing time-dependent connections, semisprays, and vector bundle structures on second order tangent bundles.
result Established a framework for time-dependent mechanics using second order tangent bundles.
Proposes a copula-based model for multi-view clustering with directional dependency.
problem Challenges in integrating multi-source datasets with directional dependency.
method Copula-based multi-view clustering model accounting for directional dependence.
result Ignoring directional dependence negatively impacts clustering performance.
New framework relaxes independence assumption for graph-mixing dependencies.
problem Tackles limitations of existing generalization results for graph-mixing dependencies.
method Proposes a framework where dependencies decay with graph distance, derives generalization bounds leveraging online-to-PAC framework.
result Derives high-probability generalization guarantees that depend on mixing rate and graph's chromatic number.
The paper studies geometrical objects in time-dependent Hamiltonian systems.
problem Understanding time-dependent Hamiltonian systems through geometrical objects.
method Analyzes distinguished tensors, time-dependent semisprays, and nonlinear connections on the dual jet space.
result Mathematical connections between these geometrical objects in time-dependent Hamiltonian systems.
The study examines insurance demand under rough volatility and path-dependent shocks.
problem Optimal insurance and investment strategies under rough volatility and path-dependent shocks.
method Rough volatility model and Hawkes process with power kernel, Functional Ito formula extension.
result Individuals demand more catastrophe insurance when path-dependent effects are considered.
Paper compresses SM kernels with time-phase modulated dependency structures for better GP performance.
problem Improving the expressiveness and generalization of Gaussian processes with complex patterns.
method Introducing time-phase modulated dependency structures and a novel structure adaptation algorithm to compress SM kernels.
result The proposed SMD kernel shows improved performance on both synthetic and real-life applications.
Measures dependence between two systems using Bayesian model comparison.
problem Quantifying dependence between two systems in a dataset.
method Bayesian model comparison of independence and dependence models.
result Dependence measure quantifies evidence for dependence in data.
Improved gap-dependent bounds for reinforcement learning with linear approximations.
problem Achieving nearly minimax-optimal performance with linear function approximation.
method Developed and analyzed the LSVI-UCB++ algorithm and its concurrent variant.
result First gap-dependent regret bound for nearly minimax-optimal algorithm LSVI-UCB++.
LDDP models space-time dependencies in DP using Gaussian processes.
problem Lack of dependency information in basic DP models for spatial and temporal data.
method Developed location dependent Dirichlet processes (LDDP) integrating Gaussian processes.
result Demonstrated effectiveness on image segmentation task.
New measure detects direct causal influences and captures strong dependencies.
problem Shortcomings of existing dependency measures in detecting direct causal influences and group selection.
method Inspired by Dobrushin's coefficients, the measure uses conditional distribution properties.
result Advantages over related measures in detecting dependencies and causal influences.