5 papers
Calibrating simplified vine copulas with a noise contrastive estimation approach
Michael Denis Kraus, David Huk, Claudia Czado
Vine copulas provide a flexible framework for modeling complex multivariate dependence structures using only bivariate building blocks. Their practical success relies heavily on th…
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
David Huk, Dongshan Wang, Miha Bresar
Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge during extreme market events.…
Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies
David Huk, Theodoros Damoulas
Copulas are a fundamental tool for modelling multivariate dependencies in data, forming the method of choice in diverse fields and applications. However, the adoption of existing m…
Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula
David Huk, Rilwan A. Adewoyin, Ritabrata Dutta
This work introduces a novel approach for generating conditional probabilistic rainfall forecasts with temporal and spatial dependence. A two-step procedure is employed. Firstly, m…
Your copula is a classifier in disguise: classification-based copula density estimation
David Huk, Mark Steel, Ritabrata Dutta
We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint densit…