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Vine Copulas as Differentiable Computational Graphs
Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations.
Cheng, Tuoyuan +3 more
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Copula-based risk aggregation with trapped ion quantum computers. [PDF]
Sci Rep, 2023 Zhu D +5 more
europepmc +1 more source
An information ratio-based goodness-of-fit test for copula models on censored data. [PDF]
Biometrics, 2023 Sun T, Cheng Y, Ding Y.
europepmc +1 more source
Value-at-Risk with Vine Copulas
, 2023 Especially when considering a portfolio with multiple assets the dependence structure in-between them is crucial regarding the grade of diversification and risk assessment. Models which consist of the multivariate normal distribution are popular because of their simplicity and fast calculation time.
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The linkage between Bitcoin and foreign exchanges in developed and emerging markets. [PDF]
Financ Innov, 2023 BenSaïda A.
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Bi-factor and Second-Order Copula Models for Item Response Data. [PDF]
Psychometrika, 2023 Kadhem SH, Nikoloulopoulos AK.
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Generalised logistic regression with vine copulas
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Haff, Ingrid Hobæk +2 more
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Comparison of Value at Risk (VaR) Multivariate Forecast Models. [PDF]
Comput Econ, 2022 Müller FM, Righi MB, Righi MB.
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LSTM-augmented vine copula modelling for energy-finance contagion analysis. [PDF]
Sci RepZeng L, Huang J, Lin X.
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Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan. [PDF]
J Quant Econ, 2022 Khan YA.
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