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Found 9 papers, 2 papers with code
Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria
In light of climate change's impacts on forests, including extreme drought and late-frost, leading to vitality decline and regional forest die-back, we assess univariate drought and late-frost risks and perform a joint risk analysis in Bavaria, Germany, from 1952 to 2020.
Vine Copula based portfolio level conditional risk measure forecasting
Accurately estimating risk measures for financial portfolios is critical for both financial institutions and regulators.
On the Observability of Gaussian Models using Discrete Density Approximations
In the first step of the proposed algorithm, the discrete density approximations are used to generate a single representative design observation vector to test for observability.
Bivariate vine copula based regression, bivariate level and quantile curves
We construct bivariate (conditional) quantiles using the level curves of vine copula based bivariate regression model.
Environmental, Social, Governance scores and the Missing pillar -- Why does missing information matter?
Environmental, Social, and Governance (ESG) scores measure companies' performance concerning sustainability and societal impact and are organized on three pillars: Environmental (E), Social (S), and Governance (G).
ESG, Risk, and (Tail) Dependence
While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open.
Nonparametric C- and D-vine based quantile regression
Further, we show that the nonparametric conditional quantile estimator is consistent.
Methodology
Vine copula mixture models and clustering for non-Gaussian data
Since vine copulas are very flexible in capturing these types of dependencies, we propose a novel vine copula mixture model for continuous data.
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso
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.
