We introduce vine computational graphs for efficient ML integration of vine copulas.
problem Integrating vine copulas into modern machine learning pipelines.
method Developed vine computational graphs and algorithms for conditional sampling, scheduling, and structure construction.
result Gradient flow through vine copulas improves performance in machine learning models.
New method for constructing truncated vine copulas.
problem High-dimensional parameter space in vine copulas.
method Propose a new score and algorithm for constructing truncated vines.
result New algorithms exploit conditional independences.
A novel stepwise VI method using vine copulas for complex latent dependence.
problem Modeling complex latent dependence structures in probabilistic models.
method Stepwise estimation of vine copula parameters using Rényi divergence and a stopping criterion.
result Our method outperforms mean-field VI and is more parsimonious in complex applications.
We propose to use nonparametric Bernstein copulas as bivariate pair-copulas in high-dimensional vine models. The resulting smooth and nonparametric vine copulas completely obviate the error-prone need for choosing the pair-copulas from parametric copula families. By means of a simulation study and an empirical analysis…
Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.
problem Time-varying dependence in multivariate systems, including tail behavior, asymmetry, and conditional structure.
method Dynamic Vine Copulas (DVC) framework for estimating and diagnosing non-Gaussian dependence, using fixed-root-order C-vines and smooth parameter trajectories.
result DVC detects and quantifies time-varying higher-order interactions, distinguishing between pairwise and conditional dependence.
This paper clarifies vine copula structures using graph and matrix representations.
problem Ambiguity in vine copula representations in literature.
method Graph and matrix representations to clarify vine structures, including cherry and chordal sequences.
result A unique matrix representation of vine structures when given a perfect elimination ordering.
Calibration of simplified vine copulas using noise contrastive estimation
problem Modeling complex multivariate dependence structures
method Noise contrastive estimation for calibration
result Improved model accuracy when simplifying assumption is violated
Bayesian model selection of vine copulas: a loss-based perspective
problem Efficient model selection and estimation in Bayesian vine methodology
method Combines loss-based model priors with shotgun stochastic search strategy
result Promotes sparsity and enables fast and effective structure selection
We employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns. We analyze the asset allocations performed during the 2008-2009 financial crisis and test different portfolio strategies such as maximum Sharpe ratio, minimum variance, and minimum conditiona…
A new vine copula mixture model improves clustering accuracy for non-Gaussian data.
problem Finite mixture models struggle with asymmetric tail dependencies and non-elliptical clusters.
method Proposes a vine copula mixture model for clustering non-Gaussian data, addressing model selection and parameter estimation.
result Significant improvement in clustering accuracy for data with asymmetric tail dependencies or non-Gaussian margins.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
problem Assessing risks of late-frost and drought in Bavaria due to climate change.
method Used vine copula models for non-Gaussian and asymmetric dependencies, with univariate and bivariate regression analyses.
result Identified 'at-risk' regions for forest adaptation.
QB-Vine extends Quasi-Bayesian methods to high dimensions using vine copulas.
problem Efficiently predicting high-dimensional distributions without sampling.
method Recursive Quasi-Bayesian construction for marginals and vine copulas for dependence modeling.
result QB-Vine is a fully non-parametric density estimator with analytical form and convergence rate independent of dimension.
A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a v…
TVineSynth generates synthetic data to balance privacy and utility.
problem Balancing privacy and utility in synthetic data generation.
method Uses vine copula with truncation to control privacy and utility trade-off.
result Achieves superior privacy-utility balance compared to competitors.
New methods using vine copulas improve accuracy of feature dependence in predictive models.
problem Inaccurate feature dependence assumptions in Shapley values lead to incorrect explanations.
method Proposed two new approaches based on vine copulas to model feature dependence.
result Vine copula approaches give more accurate approximations to true Shapley values.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
problem Need for research in multivariate quantiles, especially for bivariate responses.
method Constructs bivariate (conditional) quantiles using vine copula based bivariate regression model with a novel tree sequence graph structure.
result Avoids typical shortfalls of regression like transformations, interactions, collinearity, and quantile crossings.
New copula models capture volatility and directionality in financial time series.
problem Modeling financial return series with volatility and serial correlation.
method Stationary d-vine copula processes with v-transforms for stochastic volatility and directionality.
result Models can rival and sometimes outperform GARCH family models.
New vine copula method forecasts portfolio risk measures robust to market downturns.
problem Inaccurate risk measure estimation for financial portfolios due to lack of cross-dependency capture.
method Combines vine copulas with ARMA-GARCH models for marginal risk estimation.
result Portfolio is robust to American market downturns but not European market.
We extend existing models in the financial literature by introducing a cluster-derived canonical vine (CDCV) copula model for capturing high dimensional dependence between financial time series. This model utilises a simplified market-sector vine copula framework similar to those introduced by Heinen and Valdesogo (200…
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure. First, an autoencoder (AE) compresses the data into a lower dimensional representation. Second, the multivariate distribution of the encoded data is estimated …
Efficiently calibrates computationally expensive models using vine copulas.
problem Computational models are expensive and hard to calibrate with real data.
method Variational Bayes inference with vine copulas for dependent data.
result Computational scalability and efficiency of the proposed algorithm.
To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas are more flexible by combining arbitrary marginal distributions and (conditional) bivariate copulas. Yet, this adaptability is accompanied b…
Time series models generalize ARMA and ARFIMA with non-Gaussian dependence.
problem Modeling non-Gaussian serial dependence in time series data.
method Infinite-order partial copula dependence in s-vine processes.
result Rich class of models that generalize linear processes.
In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility…
The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.
problem Constructing well-calibrated prediction sets for correlated target variables.
method The method uses vine copulas to estimate the joint cumulative distribution function of non-conformity scores and improves the asymptotic efficiency of the quantile estimate.
result The method guarantees asymptotically exact coverage and competitive efficiency on real-world regression problems.
As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional …
Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, i…
For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature, that correlations and higher moments between different i…
This paper examines how ESG scores can indicate riskiness.
problem Determining if ESG scores can convey information on a company's riskiness.
method High-dimensional vine copula modeling to analyze (tail) dependence structure of companies with various ESG scores.
result ESG scores can be associated with (tail) riskiness, especially during crises.
A new model integrates LSTM and copulas for high-dimensional financial data.
problem Modeling high-dimensional dependencies across financial markets.
method Variational LSTM with regular vine copulas.
result Outperforms benchmarks in cross-market portfolio forecasting.
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vi…
In real-world and online social networks, individuals receive and transmit information in real time. Cascading information transmissions (e.g. phone calls, text messages, social media posts) may be understood as a realization of a diffusion process operating on the network, and its branching path can be represented by …
CopulaSMOTE addresses class imbalance in diabetes prediction models.
problem Class imbalance in diabetes prediction models, especially with fewer confirmed cases.
method Copula-based oversampling approach that models joint dependence structure.
result CopulaSMOTE improves minority-class recovery in larger diabetes datasets.
In the paper, we use and investigate copulas models to represent multivariate dependence in financial time series. We propose the algorithm of risk measure computation using copula models. Using the optimal mean-CVaR portfolio we compute portfolio's Profit and Loss series and corresponded risk measures curves. Value-…
A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate cop- ula functions. Therefore, changes in each of these factors can be detected and corrected…
GTMs model complex multivariate data with varying conditional independencies.
problem Modeling multivariate data with intricate marginals and complex dependency structures.
method Semiparametric approach using penalized splines and lasso regularization.
result GTMs accurately learn complex dependencies and identify conditional independencies.
Levy copulas are the most general concept to capture jump dependence in multivariate Levy processes. They translate the intuition and many features of the copula concept into a time series setting. A challenge faced by both, distributional and Levy copulas, is to find flexible but still applicable models for higher dim…
A method for accurate pricing of multidimensional derivatives under uncertain volatility.
problem High-dimensional stochastic control problem in uncertain volatility model.
method Backward actor-critic stochastic policy gradient scheme combining DP, PPO, and neural networks.
result Accurate and efficient pricing of multidimensional derivatives compared to benchmarks.
Study analyzes portfolio performance of crypto and traditional assets.
problem Impact of cryptocurrencies on portfolio performance.
method Used GARCH-Copula and GARCH-Vine Copula methods for risk structure calculation; Markowitz optimization for optimal asset weights.
result Portfolio with both crypto and traditional assets has higher Sharpe ratio and more stable performance.
CSD improves goodness-of-fit testing for higher-order dependence.
problem Insensitivity of standard KSDs to higher-order dependence features like tail dependence.
method Introduces Copula-Stein Discrepancy (CSD) that targets dependence geometry directly on copula density.
result CSD is sensitive to differences in tail dependence coefficients and metrizes weak convergence of copula distributions.
The paper models systemic risk in European and U.S. banks using factor copulas.
problem Modeling the joint and conditional distress probabilities of banks across Europe and the U.S.
method Employing Credit Default Swaps (CDS) and factor copulas, the paper proposes multi-factor, structured factor, and factor-vine models.
result Systematic contagion channel drives distress probabilities in the banking system as a whole, while regional factors are important within each region.
The paper presents a framework for optimizing crypto-currency portfolios using generative models.
problem Optimizing crypto-currency portfolios using generative models.
method The approach involves evaluating diverse pairings of generative model forecasts and objective functions, using simulations and blending strategies.
result Eclectic blended portfolios outperform individual generative model-based portfolios.